Data_Processing_in_Python_.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/hussain0048/Machine-Learning/blob/master/Data_Processing_in_Python_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YX1XA2lOe_hC"
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      "source": [
        "# **Table Content**"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRvGGuOwfGui"
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      "source": [
        "\n",
        "\n",
        "\n",
        "1.    📚Dataset Loading\n",
        "2.    📚Dataset Imblance Problem\n",
        "3.    📚 Handling Missing data\n",
        "4.    📚 Data Transformation\n",
        "5.    📚 Feature Scaling\n",
        "6.    📚 Data Spliting\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ssGUNVwEhKt"
      },
      "source": [
        "# **Data Collection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03SJEJN_EsD8"
      },
      "source": [
        "Data Collection is the first step in the project of the data science life cycle. It is one of the most important things in the life cycle. The data can be taken from various places likes the internet, company data, database, and many more…"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWkj57LrEtuT"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1kCl_cjl1lzyMJ96MXBuz0EppQ9gClTM7)"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1xpAC0Sf0EQ"
      },
      "source": [
        "# **Introduction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2tTizNTf7o3"
      },
      "source": [
        "Data preprocessing is an integral step in Machine Learning as the quality of data and the useful information that can be derived from it directly affects the ability of our model to learn; therefore, it is extremely important that we preprocess our data before feeding it into our model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLYwzxRyFsra"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1BmbAhW7JFubwX4r8lNIYQ700VD0VPa8h)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1U89AHcd7zu1"
      },
      "source": [
        "# **Import libaray**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-le5SNQq78rk"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from prettytable import PrettyTable\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **📚Section 1-DataSet loading**"
      ],
      "metadata": {
        "id": "xbcpm3gJnqUo"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzD5U0xTyspT"
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      "source": [
        "## **1.1- Synthetic Dataset**\n",
        "\n",
        "I am going to show you how there are data sets that are already included in a Python package. Not only that, but I am also going to show you how to find them, what they entail, and what kind of project they would be great for."
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### **Generate random dummy data**[11]\n",
        "\n",
        "To generate random ‘dummy’ data, we can make use of the make_classification() function in case of classification data, and make_regression() function in case of regression data. This is very useful in some cases when debugging or when you want to try out certain things on a (small) random data set.\n",
        "\n",
        "Below, we generate 10 classification data points consisting of 4 features (found in X) and a class label (found in y), where the data points belong to either the negative class (0) or the positive class (1):"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "from sklearn.datasets import make_classification\n",
        "import pandas as pd\n",
        "X, y = make_classification(n_samples=10, n_features=4, n_classes=2, random_state=123)"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "Here, X consists of the 4 feature columns for the generated data points:"
      ]
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      "source": [
        "pd.DataFrame(X, columns=['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4'])"
      ],
      "execution_count": null,
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              "   Feature_1  Feature_2  Feature_3  Feature_4\n",
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      "source": [
        "#And y contains the corresponding label of each data point:\n",
        "pd.DataFrame(y, columns=['Label'])\n"
      ],
      "execution_count": null,
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            "text/plain": [
              "   Label\n",
              "0      0\n",
              "1      1\n",
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              "</style>\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3fb780b2-d293-46f1-9f07-9b0578a9efc5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3fb780b2-d293-46f1-9f07-9b0578a9efc5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4fcc9f91-a0fd-46bc-bbfe-1e5adcddaa32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_classification\n",
        "X, y = make_classification(n_classes=2, class_sep=0.5,\n",
        "weights=[0.05, 0.95], n_informative=2, n_redundant=0, flip_y=0,\n",
        "n_features=2, n_clusters_per_class=1, n_samples=1000, random_state=10"
      ],
      "metadata": {
        "id": "4gq6RddS6iaW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Create Dataset with SMOTE**\n"
      ],
      "metadata": {
        "id": "wP_xqFAA7ReN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm = SMOTE(random_state=42)\n",
        "X_res, y_res = sm.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "5aqdwAiP7SRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zULD0DFiy6Qq"
      },
      "source": [
        "### **Faker [10]**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4iMy4XnjzCgZ"
      },
      "source": [
        "We might come across a situation where we need to generate some test data or use some dummy data in our analysis. One way to get dummy data is by using the Faker library. This will generate fake data for you very quickly when you need to."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Faker is a fantastic package to generate fake random data. Each time you use the faker library generator, it will result in different random data. There are numerous exciting methods like fake text, fake credit card numbers. The complete documentation of this faker library and its uses is at this link [21]"
      ],
      "metadata": {
        "id": "Gw9iwdMJ2BWy"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2yv_EKjzJQp"
      },
      "source": [
        "**Installation**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hNDEcTPzQba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9275c9dc-d1d1-45fb-f69b-02a1b952d2fb"
      },
      "source": [
        "!pip install faker\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faker\n",
            "  Downloading Faker-20.0.3-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.4 in /usr/local/lib/python3.10/dist-packages (from faker) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.4->faker) (1.16.0)\n",
            "Installing collected packages: faker\n",
            "Successfully installed faker-20.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVDOrl66zcRa"
      },
      "source": [
        "**Example**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8CbJXJLzx32"
      },
      "source": [
        "from faker import Faker\n",
        "fake = Faker()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXKpmmCRzeJl"
      },
      "source": [
        "# Generating fake email\n",
        "print (fake.email())\n",
        "# Generating fake country name\n",
        "print(fake.country())\n",
        "# Generating fake name\n",
        "print(fake.name())\n",
        "# Generating fake text\n",
        "print(fake.text())\n",
        "# Generating fake lat and lon\n",
        "print(fake.latitude(), fake.longitude())\n",
        "# Generating fake url\n",
        "print(fake.url())\n",
        "# Generating fake profile\n",
        "print(fake.profile())\n",
        "# Generating random number\n",
        "print(fake.random_number())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from faker import Faker\n",
        "fake = Faker()\n",
        "\n",
        "print(fake.profile())\n",
        "print(fake.credit_card_number())"
      ],
      "metadata": {
        "id": "spKNYfPH2TKx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dk60DEvnkCx7"
      },
      "source": [
        "### **Wine Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUyfFpqkkNfo"
      },
      "source": [
        "from sklearn.datasets import load_wine\n",
        "\n",
        "wine = load_wine()\n",
        "\n",
        "for line in wine.DESCR.split(\"\\n\")[5:29]:\n",
        "    print(line)\n",
        "\n",
        "wine_df = pd.DataFrame(data=wine.data, columns = wine.feature_names)\n",
        "wine_df[\"WineType\"] = wine.target\n",
        "\n",
        "wine_df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8aHRZSAtkYtR"
      },
      "source": [
        "### **Breast Cancer Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQicqZsBkdps"
      },
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "breast_cancer = load_breast_cancer()\n",
        "\n",
        "for line in breast_cancer.DESCR.split(\"\\n\")[5:31]:\n",
        "    print(line)\n",
        "\n",
        "breast_cancer_df = pd.DataFrame(data=breast_cancer.data, columns = breast_cancer.feature_names)\n",
        "breast_cancer_df[\"TumorType\"] = breast_cancer.target\n",
        "\n",
        "breast_cancer_df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdjrebzJkkft"
      },
      "source": [
        "### **Boston Housing Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDIDJeqhkv1w"
      },
      "source": [
        "from sklearn.datasets import load_boston\n",
        "\n",
        "boston = load_boston()\n",
        "\n",
        "for line in boston.DESCR.split(\"\\n\")[5:29]:\n",
        "    print(line)\n",
        "\n",
        "boston_df = pd.DataFrame(data=boston.data, columns = boston.feature_names)\n",
        "boston_df[\"Price\"] = boston.target\n",
        "\n",
        "boston_df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOLVCtlQ-A5H"
      },
      "source": [
        "### **Seaborn Datasets**[16]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOWnQ7d7-GzP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3a6ad465-3982-4884-bbae-c5b6bc0a3dc8"
      },
      "source": [
        "#Importing the modules\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "#Loading the dataset\n",
        "planets = sns.load_dataset('planets')\n",
        "#Showing 10 first row in the planets data\n",
        "planets.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            method  number  orbital_period   mass  distance  year\n",
              "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
              "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
              "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
              "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
              "4  Radial Velocity       1         516.220  10.50    119.47  2009"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fea91e85-02d7-4911-ab7b-e796fcf41003\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>method</th>\n",
              "      <th>number</th>\n",
              "      <th>orbital_period</th>\n",
              "      <th>mass</th>\n",
              "      <th>distance</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Radial Velocity</td>\n",
              "      <td>1</td>\n",
              "      <td>269.300</td>\n",
              "      <td>7.10</td>\n",
              "      <td>77.40</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Radial Velocity</td>\n",
              "      <td>1</td>\n",
              "      <td>874.774</td>\n",
              "      <td>2.21</td>\n",
              "      <td>56.95</td>\n",
              "      <td>2008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Radial Velocity</td>\n",
              "      <td>1</td>\n",
              "      <td>763.000</td>\n",
              "      <td>2.60</td>\n",
              "      <td>19.84</td>\n",
              "      <td>2011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Radial Velocity</td>\n",
              "      <td>1</td>\n",
              "      <td>326.030</td>\n",
              "      <td>19.40</td>\n",
              "      <td>110.62</td>\n",
              "      <td>2007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Radial Velocity</td>\n",
              "      <td>1</td>\n",
              "      <td>516.220</td>\n",
              "      <td>10.50</td>\n",
              "      <td>119.47</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fea91e85-02d7-4911-ab7b-e796fcf41003')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fea91e85-02d7-4911-ab7b-e796fcf41003 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fea91e85-02d7-4911-ab7b-e796fcf41003');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a7962cfd-8bf9-483d-87c9-99b7687c70ef\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7962cfd-8bf9-483d-87c9-99b7687c70ef')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a7962cfd-8bf9-483d-87c9-99b7687c70ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we need to import the seaborn package first\n",
        "import seaborn as sb\n",
        "\n",
        "# import pandas for data frame\n",
        "import pandas as pd\n",
        "\n",
        "# using seaborn, we can return a list of the data set names\n",
        "sb.get_dataset_names()"
      ],
      "metadata": {
        "id": "3tWcn3Rnnv2C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05091adc-1603-4102-da86-f397d4ae6920"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['anagrams',\n",
              " 'anscombe',\n",
              " 'attention',\n",
              " 'brain_networks',\n",
              " 'car_crashes',\n",
              " 'diamonds',\n",
              " 'dots',\n",
              " 'dowjones',\n",
              " 'exercise',\n",
              " 'flights',\n",
              " 'fmri',\n",
              " 'geyser',\n",
              " 'glue',\n",
              " 'healthexp',\n",
              " 'iris',\n",
              " 'mpg',\n",
              " 'penguins',\n",
              " 'planets',\n",
              " 'seaice',\n",
              " 'taxis',\n",
              " 'tips',\n",
              " 'titanic']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we can load the dataset directly onto a data frame\n",
        "df = sb.load_dataset('anscombe')\n",
        "\n",
        "# look at a portion to see what we are working with\n",
        "df.head()"
      ],
      "metadata": {
        "id": "HuAepZ5Qn4aB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c933b2b3-4100-4450-8767-0a17f2f8810b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  dataset     x     y\n",
              "0       I  10.0  8.04\n",
              "1       I   8.0  6.95\n",
              "2       I  13.0  7.58\n",
              "3       I   9.0  8.81\n",
              "4       I  11.0  8.33"
            ],
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dataset</th>\n",
              "      <th>x</th>\n",
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              "      <th>0</th>\n",
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              "      <td>8.04</td>\n",
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              "      <th>1</th>\n",
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              "      <td>8.0</td>\n",
              "      <td>6.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I</td>\n",
              "      <td>13.0</td>\n",
              "      <td>7.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>11.0</td>\n",
              "      <td>8.33</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9f6a8744-9312-4cf8-bf27-a2d0a44333bd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9f6a8744-9312-4cf8-bf27-a2d0a44333bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47cf3557-00c4-4053-9ce4-e2694bbc93a8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Dataset Details**[16]"
      ],
      "metadata": {
        "id": "vGdGqC4AKWtF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Anscombe**\n",
        "\n",
        "Anscombe’s quartet is comprised of four data sets that have nearly identical simple descriptive statistics — meaning that they all have eleven points and their averages and variance are all very similar. But they all have very different distributions and appear very different when graphed. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance of graphing data before analyzing it and the effect of outliers and other influential observations on statistical properties. Both of these are important ideas to a good data scientist.\n",
        "\n",
        "The trend lines show a striking contrast to their points. Pay close attention to how their observations form unique patterns but the trend lines are all similar.Suggested Projects\n",
        "We can do a few things here:\n",
        "- Clustering — build an algorithm that can detect patterns in the data and see if there is any similarity between points in datasets I, II, III, and IV.\n",
        "- Logistic Regression — try and predict which point belongs to which data set\n",
        "- Linear Regression — try and predict the missing value of x or y"
      ],
      "metadata": {
        "id": "NkUHaNG8IPxT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Attention**\n",
        "\n",
        "This data set is built upon test takers, whether they were focused, and what score they received. The data set looks like this:"
      ],
      "metadata": {
        "id": "4xWDaB1GI3hC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Suggested Projects:**\n",
        "\n",
        "Logistic Regression — we can try and predict whether the target variable is ‘focused’ or ‘distracted.’"
      ],
      "metadata": {
        "id": "1tDdH6cxI-h-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Car Crashes**\n",
        "\n",
        "This data set is divided into different states in the U.S. Each state has the following features:\n",
        "As you can see, the data set is flush with continuous variables. Ignoring the ‘abbrev’ feature which just tells us the state, we can use this data set to predict almost any feature."
      ],
      "metadata": {
        "id": "5UiJnEcUJE0v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Suggested Projects:**\n",
        "\n",
        "- Multivariate Linear Regression — using the combination of features you can try to build a model that will predict any of the features.\n",
        "- Clustering — grouping states together based on the features. An interesting analysis idea would be to look at the clusters to try and determine group models using outside research (ie. traffic laws)."
      ],
      "metadata": {
        "id": "a_229sWDJOAR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Diamonds**\n",
        "\n",
        "Let’s take a look at the data set below:\n",
        "n this data set, each observation is a unique diamond. Features include carat, color, cut, clarity, and dimensions (x,y,z). This is a well-known beginner data set that can be used in a variety of projects."
      ],
      "metadata": {
        "id": "rzYBuzFeJZ95"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Suggested Projects:**\n",
        "\n",
        "- Multivariate Linear Regression — there are a lot of continuous variables that could be predicted\n",
        "- Multivariate Logistic Regression — try to predict either color, cut, or clarity using the other features\n",
        "- Clustering — analyze clusters that come up and determine shared characteristics (ie. overpriced, fairly priced, etc)"
      ],
      "metadata": {
        "id": "RCRuJQ6UJngr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Flights**\n",
        "\n",
        "This data set is the first we are going to talk about that is time-related. It looks like this:It is the monthly passenger amount from January 1949 to December 1960. With some data wrangling, you can get year and month features to combine leaving you with a nice time step."
      ],
      "metadata": {
        "id": "_W8CTfRXJwue"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Suggested Projects:**\n",
        "\n",
        "Time Series — using the data, you can set out to predict the passenger at the later end of the time frame.\n",
        "Be careful, there is a cyclical nature to this data set."
      ],
      "metadata": {
        "id": "VundnVKYKFo4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. Iris**\n",
        "\n",
        "The Iris data set is another fundamental data set that most of us in data science have worked with in one way or another. It contains sepal and petal dimensions for four types of iris flowers."
      ],
      "metadata": {
        "id": "1wqNHYMHKQs0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Suggested Projects:**\n",
        "\n",
        "Multivariate Logistic Regression — using the dimensions above, you can try and predict the species of flower.\n",
        "Clustering — using the features, aside from species, you can try and create clusters and analyze them for patterns"
      ],
      "metadata": {
        "id": "5flERgpEKl9q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7. MPG**\n",
        "\n",
        "The MPG data set is a data frame full of cars with features and names attached. As you can see below, there is a mixture of variables that can be used for regression style models."
      ],
      "metadata": {
        "id": "VWAyIR1ZKwXS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Suggested Projects:**\n",
        "\n",
        "- Multivariate Linear Regression — trying to predict weight or displacement could be a worthwhile goal\n",
        "- Multivariate Logistic Regression — the non-continuous variables include origin and model year\n",
        "- Clustering — ignoring the name column, you can cluster them to determine which observations are the most similar. This would mean outside research."
      ],
      "metadata": {
        "id": "hZheMH2RKy76"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**8. Planets**\n",
        "\n",
        "The ‘planets’ data set is interesting if you are a fan of astronomy. It has a series of observations which are all exoplanets. Take a quick look at the data set itself:"
      ],
      "metadata": {
        "id": "Tug0JSegLAx-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see there a number of features, not all of which are easy to understand. So let’s talk about the most important features for a second.\n",
        "- method — this reflects how the planet was detected. Be careful, there is a large class imbalance here.\n",
        "- orbital period — how long it takes to complete one cycle of rotation around its center. Note: some are so far away from a star that it is unclear where the orbit begins, thus the NaN\n",
        "- mass — relative weight of the exoplanet\n",
        "- distance — light-years from Earth. If you are unfamiliar, a light-year is a distance that it takes light a year to travel. Since light is the fastest thing we know, this is a large distance."
      ],
      "metadata": {
        "id": "eFTkqUsCLDfi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Suggested Projects:**\n",
        "\n",
        "- Logistic Regression — you can try and predict the method of detection using the other variables. Note: large class imbalance here\n",
        "Clustering — you can do a deep dive and find patterns among the exoplanets. Use different techniques like PCA to help account for variance.\n",
        "- Linear Regression — you can determine whether there is a linear relationship between any of the variables and the mass of the exoplanet."
      ],
      "metadata": {
        "id": "h89sbHhlLU0G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**9. Titanic**\n",
        "\n",
        "This data set is the most well-known and used in my honest opinion — even the subject of an ongoing Kaggle competition. In this data set, you get a manifest of the passenger vessel that sank due to the infamous iceberg on its way from Ireland to New York City."
      ],
      "metadata": {
        "id": "iERx3m5-L5XJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are many features here — whether they traveled alone, what passenger class they were in, gender, and even if they had siblings — but I will only recommend one as the target variable for tradition's sake. The survived class records whether they survived or not. It is interesting to think about the final product telling you which coefficients were most important for predicting if a person lived or died.\n",
        "- Logistic Regression — looking at the ‘survived’ column, you can build a model to predict which passenger will survive and which will not.\n",
        "This will be a fun project and if you can master it, submit it to the Kaggle competition."
      ],
      "metadata": {
        "id": "C3FHTVMCL-MY"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2WuuWtcgNG4"
      },
      "source": [
        "## **1.2- Data Loading**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Biql2sZngvIB"
      },
      "source": [
        "### **Data Loading from Google Drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gwp1rpowgTKQ",
        "outputId": "7083b58b-dc8e-4e6b-87a5-e7600a349e93"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RoIsrRghTBm"
      },
      "source": [
        "import pandas as pd\n",
        "Train_data = pd.read_csv('/content/drive/MyDrive/Datasets/Malyria_2-21-2021.csv',encoding = 'latin')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hwlf9uUhdzP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "outputId": "4d9e2618-ae53-4ba2-8487-2029a730c247"
      },
      "source": [
        "Train_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0      SECTION_NO            MRNO        PATIENT_NAME  \\\n",
              "0               1  K04HEM20123774  K0460000001363  Saqib Mehmood Khan   \n",
              "1               2  K04HEM18160758  K0460000001680      Muhammad Iqbal   \n",
              "2               3  K04HEM18160758  K0460000001680      Muhammad Iqbal   \n",
              "3               4  K04HEM17056995  K0460000000106       Shahbaz Ahmad   \n",
              "4               5  K04HEM17073233  K0460000000150       Mehfooz Ahmad   \n",
              "...           ...             ...             ...                 ...   \n",
              "22911       22912  K04HEM21017730  K0400002457330       Rooh Ul Ameen   \n",
              "22912       22913  K04HEM21018207  K0400002459565       Zeshan Rustam   \n",
              "22913       22914  K04HEM21019179  K0400002461645              M Umer   \n",
              "22914       22915  K04HEM21021055  K04ACE21027742      Shaheen Akhtar   \n",
              "22915       22916  K04HEM21020544  K0400002464110        Fakhraj Bibi   \n",
              "\n",
              "               FATHER_NAME HUSBAND_NAME       NIC_NEW REFERRED_FROM  \\\n",
              "0      MUHAMMAD AFSAR KHAN          NaN  1.310163e+12           NaN   \n",
              "1             ABDUR REHMAN          NaN  1.310134e+12           NaN   \n",
              "2             ABDUR REHMAN          NaN  1.310134e+12           NaN   \n",
              "3             MUMTAZ AHMAD          NaN  4.220136e+12           NaN   \n",
              "4             HAIDER ZAMAN          NaN  1.310109e+12           NaN   \n",
              "...                    ...          ...           ...           ...   \n",
              "22911          ABDUL SALAM          NaN  8.139494e+10           NaN   \n",
              "22912             M RUSTAM          NaN           NaN           NaN   \n",
              "22913             M JUNAID          NaN           NaN           NaN   \n",
              "22914                  NaN     M SALEEM           NaN           NaN   \n",
              "22915              M FAYAZ          NaN           NaN           NaN   \n",
              "\n",
              "              AGE  GENDER  NO_OF_ADMISSION    DISTRICT      TEHSIL  \\\n",
              "0      34 Year(s)    Male                0  Abbottabad     havelia   \n",
              "1      43 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "2      43 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "3      28 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "4      42 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "...           ...     ...              ...         ...         ...   \n",
              "22911  13 Year(s)    Male                1     Haripur     Haripur   \n",
              "22912  20 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "22913  21 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "22914  45 Year(s)  Female                0  Abbottabad  Abbottabad   \n",
              "22915  22 Year(s)  Female                0  Abbottabad  Abbottabad   \n",
              "\n",
              "                  REPORT_VERIFIED            CPT_ID RESULT_VALUE  \\\n",
              "0      11/12/2020 10:46:53 A11P11  1000000000085007            Y   \n",
              "1          9/29/2018 9:26:21 A9P9  1000000000087207            Y   \n",
              "2          9/29/2018 9:26:07 A9P9  1000000000085007            Y   \n",
              "3           5/4/2017 9:48:37 A5P5  1000000000085007            Y   \n",
              "4          6/3/2017 11:59:38 A6P6  1000000000085007            Y   \n",
              "...                           ...               ...          ...   \n",
              "22911     2/10/2021 16:53:12 A2P2  1000000000085060            Y   \n",
              "22912     2/11/2021 11:49:58 A2P2  1000000000085007            Y   \n",
              "22913     2/13/2021 11:55:21 A2P2  1000000000087207            Y   \n",
              "22914     2/17/2021 11:09:47 A2P2  1000000000087207            Y   \n",
              "22915     2/16/2021 12:37:12 A2P2  1000000000085651            Y   \n",
              "\n",
              "               CPT_ID.1    SECTION_NO.1 RESULT_TEXT  \n",
              "0      1000000000087207  K04HEM20123774    Negative  \n",
              "1      1000000000087207  K04HEM18160758    Negative  \n",
              "2      1000000000087207  K04HEM18160758    Negative  \n",
              "3      1000000000087207  K04HEM17056995    Negative  \n",
              "4      1000000000087207  K04HEM17073233    Negative  \n",
              "...                 ...             ...         ...  \n",
              "22911  1000000000087207  K04HEM21017730    Negative  \n",
              "22912  1000000000087207  K04HEM21018207    Negative  \n",
              "22913  1000000000087207  K04HEM21019179    Negative  \n",
              "22914  1000000000087207  K04HEM21021055    Negative  \n",
              "22915  1000000000087207  K04HEM21020544    Negative  \n",
              "\n",
              "[22916 rows x 19 columns]"
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <td>K0460000000106</td>\n",
              "      <td>Shahbaz Ahmad</td>\n",
              "      <td>MUMTAZ AHMAD</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.220136e+12</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>K04HEM17073233</td>\n",
              "      <td>K0460000000150</td>\n",
              "      <td>Mehfooz Ahmad</td>\n",
              "      <td>HAIDER ZAMAN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.310109e+12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>6/3/2017 11:59:38 A6P6</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM17073233</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22911</th>\n",
              "      <td>22912</td>\n",
              "      <td>K04HEM21017730</td>\n",
              "      <td>K0400002457330</td>\n",
              "      <td>Rooh Ul Ameen</td>\n",
              "      <td>ABDUL SALAM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.139494e+10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>2/10/2021 16:53:12 A2P2</td>\n",
              "      <td>1000000000085060</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21017730</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22912</th>\n",
              "      <td>22913</td>\n",
              "      <td>K04HEM21018207</td>\n",
              "      <td>K0400002459565</td>\n",
              "      <td>Zeshan Rustam</td>\n",
              "      <td>M RUSTAM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/11/2021 11:49:58 A2P2</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21018207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22913</th>\n",
              "      <td>22914</td>\n",
              "      <td>K04HEM21019179</td>\n",
              "      <td>K0400002461645</td>\n",
              "      <td>M Umer</td>\n",
              "      <td>M JUNAID</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/13/2021 11:55:21 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21019179</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22914</th>\n",
              "      <td>22915</td>\n",
              "      <td>K04HEM21021055</td>\n",
              "      <td>K04ACE21027742</td>\n",
              "      <td>Shaheen Akhtar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>M SALEEM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/17/2021 11:09:47 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21021055</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22915</th>\n",
              "      <td>22916</td>\n",
              "      <td>K04HEM21020544</td>\n",
              "      <td>K0400002464110</td>\n",
              "      <td>Fakhraj Bibi</td>\n",
              "      <td>M FAYAZ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/16/2021 12:37:12 A2P2</td>\n",
              "      <td>1000000000085651</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21020544</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22916 rows × 19 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4c0e55b-6dfe-4408-bf60-016aab9ab724')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a4c0e55b-6dfe-4408-bf60-016aab9ab724 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a4c0e55b-6dfe-4408-bf60-016aab9ab724');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9fd1edac-44cb-45e1-a1e7-100c8721701c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9fd1edac-44cb-45e1-a1e7-100c8721701c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9fd1edac-44cb-45e1-a1e7-100c8721701c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data loading From Github**"
      ],
      "metadata": {
        "id": "veBKTm_lIp7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm markets.csv\n",
        "!wget https://raw.githubusercontent.com/shadgriffin/ml_bias/main/markets.csv\n",
        "\n",
        "df = pd.read_csv(\"markets.csv\", sep=\",\", header=0)"
      ],
      "metadata": {
        "id": "yt5sXwmPIxAH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77d7922c-42cd-491e-ea92-f42257885353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "rm: cannot remove 'markets.csv': No such file or directory\n",
            "--2024-01-16 06:36:26--  https://raw.githubusercontent.com/shadgriffin/ml_bias/main/markets.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5809035 (5.5M) [text/plain]\n",
            "Saving to: ‘markets.csv’\n",
            "\n",
            "markets.csv         100%[===================>]   5.54M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2024-01-16 06:36:27 (71.5 MB/s) - ‘markets.csv’ saved [5809035/5809035]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# one hot encode the breast cancer dataset\n",
        "from pandas import read_csv\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "# define the location of the dataset\n",
        "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/breast-cancer.csv\"\n",
        "# load the dataset\n",
        "dataset = read_csv(url, header=None)\n",
        "# retrieve the array of data\n",
        "data = dataset.values\n",
        "# separate into input and output columns\n",
        "X = data[:, :-1].astype(str)\n",
        "y = data[:, -1].astype(str)"
      ],
      "metadata": {
        "id": "ZvArJgY9_aDT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NgGpVwLIuwx9"
      },
      "source": [
        "# **3- Python Usufull library**[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpoRvASfvnt8"
      },
      "source": [
        "## **Numerizer**\n",
        "\n",
        "Amazing library to convert text numerics into int and float. Useful library for NLP projects. For more details, please check PyPI and this github repo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yY9DuivVvvKt"
      },
      "source": [
        "!pip install numerizer\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9wD_hPsEv2Xe"
      },
      "source": [
        "**Example**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yf7tknpKv1YV"
      },
      "source": [
        "#importing numerize library\n",
        "from numerizer import numerize\n",
        "from numerizer import numerize\n",
        "#examples\n",
        "a = int(numerize('Three'))\n",
        "print(a)\n",
        "print(numerize('five thousand two hundred and twenty'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UhofRji70CQr"
      },
      "source": [
        "## **EMOT [10]**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBckIXJS0IoD"
      },
      "source": [
        "Collecting and analyzing data on emojis as well as emoticons give useful insights, especially in sentiment analysis. An emoji is an image small enough to insert into text that expresses an emotion or idea. An emoticon is a representation of a human facial expression using only keyboard characters such as letters, numbers, and punctuation marks.\n",
        "emot helped us to convert the emojis and emoticons into words. For more details on this library, please check this Github repo. It has a good collection of emoticons and emojis with the corresponding words."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Pf_yaWw0YLp"
      },
      "source": [
        "!pip install emot\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDquWCWZ0bUi"
      },
      "source": [
        "#Importing libraries\n",
        "import re\n",
        "from emot.emo_unicode import UNICODE_EMO, EMOTICONS\n",
        "# Function for converting emojis into word\n",
        "def convert_emojis(text):\n",
        "    for emot in UNICODE_EMO:\n",
        "        text = text.replace(emot, \"_\".join(UNICODE_EMO[emot].replace(\",\",\"\").replace(\":\",\"\").split()))\n",
        "    return text\n",
        "# Example\n",
        "text1 = \"Hilarious 😂. The feeling of making a sale 😎, The feeling of actually fulfilling orders 😒\"\n",
        "convert_emojis(text1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBt3kxpC0lAz"
      },
      "source": [
        "# Function for converting emoticons into word\n",
        "def convert_emoticons(text):\n",
        "    for emot in EMOTICONS:\n",
        "        text = re.sub(u'('+emot+')', \"_\".join(EMOTICONS[emot].replace(\",\",\"\").split()), text)\n",
        "    return text# Example\n",
        "text = \"Hello :-) :-)\"\n",
        "convert_emoticons(text)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Pretty-Table**"
      ],
      "metadata": {
        "id": "lyfWTFY41cii"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The prettytable library is used to represent data in a relational table. With prettytable other operations on the tabular data can be performed like adding or deleting a row, alignment of columns, and clearing all the data in the table [21]"
      ],
      "metadata": {
        "id": "XuN9VSQ81kia"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "python -m pip install -U prettytable"
      ],
      "metadata": {
        "id": "AKMDCG0U1wRx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from prettytable import PrettyTable\n",
        "table = PrettyTable()\n",
        "\n",
        "table.field_names = [\"Month\",\"Earning\"]\n",
        "table.add_rows(\n",
        "    [\n",
        "        [\"JANUARY\",1020],\n",
        "        [\"FEBRUARY\", 1233],\n",
        "        [\"MARCH\", 1892],\n",
        "        [\"APRIL\", 1500]\n",
        "    ]\n",
        ")\n",
        "print(table)"
      ],
      "metadata": {
        "id": "eL77_T-q15Hq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Barcode**"
      ],
      "metadata": {
        "id": "vsQy87Y52li3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accessible and yet powerful library in Python to create the barcode. We are using this library, a barcode in .png format. In the program, we have used European Article Number(EAN) 13 number barcode image. You can try to use different methods present in the barcode library [21]"
      ],
      "metadata": {
        "id": "0tT6E6VV2vAY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install python-barcode"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JHf7NBGo20BA",
        "outputId": "5b8cc154-aef6-4d3c-a69c-fe6a61f9365b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-barcode\n",
            "  Downloading python_barcode-0.13.1-py3-none-any.whl (217 kB)\n",
            "\u001b[?25l\r\u001b[K     |█▌                              | 10 kB 23.4 MB/s eta 0:00:01\r\u001b[K     |███                             | 20 kB 15.3 MB/s eta 0:00:01\r\u001b[K     |████▌                           | 30 kB 10.7 MB/s eta 0:00:01\r\u001b[K     |██████                          | 40 kB 9.4 MB/s eta 0:00:01\r\u001b[K     |███████▌                        | 51 kB 9.3 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 61 kB 9.9 MB/s eta 0:00:01\r\u001b[K     |██████████▋                     | 71 kB 8.8 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 81 kB 9.8 MB/s eta 0:00:01\r\u001b[K     |█████████████▋                  | 92 kB 10.1 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 102 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 112 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 122 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████▋            | 133 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 143 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 153 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 163 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 174 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 184 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 194 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▏ | 204 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 215 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 217 kB 10.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: python-barcode\n",
            "Successfully installed python-barcode-0.13.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from barcode import EAN13\n",
        "from barcode.writer import ImageWriter\n",
        "\n",
        "number='123456789123'\n",
        "mycode=EAN13(number, writer=ImageWriter())\n",
        "mycode.save('new')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OA3KTNLZ27rA",
        "outputId": "dd4f84bf-5057-4924-9ae1-db243562ae34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'new.png'"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Num2Words**"
      ],
      "metadata": {
        "id": "wldYxBFO46FF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "With the num2words library, we can change any given number value into words. This library is helpful while building invoice apps where you require to change the entered number into words [21]"
      ],
      "metadata": {
        "id": "t6XSoAdP4_v9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install num2words"
      ],
      "metadata": {
        "id": "p7cgmWjl5CS-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from num2words import num2words\n",
        "\n",
        "num = input(\"Enter the number \")\n",
        "print(num2words(num))"
      ],
      "metadata": {
        "id": "vnqevNi25J1n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbnAH9rKhntf"
      },
      "source": [
        "# **4- Dropping feature**\n",
        "\n",
        "Dropping feature usually isn’t recommended because you’re losing information. But if you’re sure that the column isn’t important, or simply has too many missing values, you can choose to drop them. For example, for this dataset, the host_name column was removed for ethical reasons, and id was removed because it was was unnecessary.\n",
        "To drop features, use drop and set axis to 1 and inplace to true. Axis is 1 because we want to drop columns (0 means row), and inplace is True because you're transforming it directly on your dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfufIU6Ph4DJ"
      },
      "source": [
        "## **3.1 Drop Index Columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRMwPJugh_cV"
      },
      "source": [
        "Train_data=Train_data.reset_index()\n",
        "Train_data=Train_data.drop(columns=['index'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ywNezHyiN1J"
      },
      "source": [
        "## **3.2 Drop Multiple featurs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0iDC_p4nigy-"
      },
      "source": [
        "New5=Train_data.drop(Train_data.columns[[0,1,3,4,5,6,7,10,17]], axis=1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJUyNsNCygat"
      },
      "source": [
        "# Drop unnecessary columns that are not important\n",
        "colsToDrop = ['id','host_name','last_review']\n",
        "\n",
        "airbnb.drop(colsToDrop, axis=1, inplace=True)\n",
        "\n",
        "missing_cols(airbnb)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qsjo7VQeynsV"
      },
      "source": [
        "## **3.3-Dropping the row**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYvBStP2yx5f"
      },
      "source": [
        "If you want to remove rows, you do so using dropna . I’m not going to do that because there are no missing values in price."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Fb5a_4iyw4b"
      },
      "source": [
        "# remove rows with missing values in price\n",
        "airbnb['price'].dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjrtdkJdkvGz"
      },
      "source": [
        "# **5- Convert target variable into binary Form**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPeuZ7VPk50C"
      },
      "source": [
        "Train_data['RESULT_TEXT'].replace({'Negative': 0, 'Positive': 1},inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XgG-pUdEI3CK"
      },
      "source": [
        "# **📚Section 2-Data Imbalanced problem** [8]\n",
        "\n",
        "\n",
        "An imbalance occurs when one or more classes have very low proportions/probability in the training data as compared to the other classes. Our model gives us poor predictive performance, specifically for the minority class if we don't handle the imbalanced data.\n",
        "\n",
        "Imagine you are trying to build a classification model, and you have two classes: Cats and Dogs. Unfortunately, your data is very **skewed**: there are 950 cat pictures and 50 dog pictures.[17]\n",
        "\n",
        "If your model classifies every picture as a Cat (dumb, right?), you’ll be 95% accurate! Think about that for a second. The **distribution** in your dataset becomes a big problem really quickly [17]\n",
        "\n",
        "For classification tasks, one may encounter situations where the target class label is un-equally distributed. Such conditions are termed imbalanced data. Due to the presence of class imbalance the model might become biased towards the majority class data sample. A data scientist might face some challenges while modeling an imbalanced class dataset.\n",
        "There are various hacks or techniques to handle an imbalanced class data sample prior to modeling including[19]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AENif9HTYX7P"
      },
      "source": [
        "Classification is one of the most common machine learning problems. One of the common issues found in datasets that are used for classification is imbalanced classes issue. Generally, we expect the labels to be relatively even distributed. In reality, the samples we can get maybe unsatisfactory.\n",
        "\n",
        "Take a binary classification problem for example, we might encounter too few positive samples in the training dataset, called minority class. And most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important. Such an imbalanced dataset can be addressed by sampling techniques. There are mainly three types of sampling techniques: **Oversampling**, **Undersampling** and **Synthetic Sampling**."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Which ML algoritherm is sutiable for imbalanced Problem**[17]\n",
        "\n",
        "As a somewhat related note, make sure you are using the right algorithm to solve your problem. For example, Decision Tree-based models are excellent at handling imbalanced classes. When dealing with structured data, that might be all you need."
      ],
      "metadata": {
        "id": "05umKgfCNkBG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Performance metrics** [17]"
      ],
      "metadata": {
        "id": "KHUY9WhnOMTz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy is not the best performance metric on these problems\n",
        "Accuracy is not a good performance metric when you have an imbalanced dataset.\n",
        "Instead, and depending on your specific problem, you should look at any of the following:\n",
        "\n",
        "- Precision, Recall, F-Score\n",
        "- Confusion Matrix\n",
        "- ROC Curves"
      ],
      "metadata": {
        "id": "1BlaRfvGOTmb"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKIZZsiLYuLh"
      },
      "source": [
        "**Why Imbalanced dataset is bad?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhwsurmIY_aD"
      },
      "source": [
        "So why does imbalance lead to poor model performance? Because any algorithm cannot obtain enough information from a class with a small sample size to make accurate predictions. The uneven distribution of the corresponding variables reduces the accuracy of the algorithm, and the prediction accuracy for small classes will be very low. The algorithm itself is error-driven, that is, the goal of the model is to minimize the overall error, and the contribution of small classes to the overall error is very low. The algorithms themselves assume that the class distribution of the data set is balanced, and they may also assume that different classes of errors bring the same loss.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Check Imbalanced Problem in Data**"
      ],
      "metadata": {
        "id": "rAsOZDDFmv0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize = (8,5))\n",
        "Train_data.RESULT_TEXT.value_counts(normalize = True).plot(kind='bar', color= ['skyblue','navy'], alpha = 0.9, rot=0)\n",
        "plt.title('Results Status Negative(0) and Positive(1) in the Imbalanced Dataset')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KPr-Jzsnm5jd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6.1-Handle Imbalanced dataset problem**"
      ],
      "metadata": {
        "id": "TH7wfpZGWnjd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 1) Oversampling Minority Class\n",
        "- 2) Downsampling Majority Class\n",
        "- 3) Generate Synthetic Data\n",
        "- 4) Balanced Class Weight\n",
        "- 5) Combine Oversampling & Downsampling Techniques"
      ],
      "metadata": {
        "id": "QXxp7TkkW2YE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6.1.1- Oversampling**"
      ],
      "metadata": {
        "id": "fEdbkeXzHGC9"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQNqBvsQZy1P"
      },
      "source": [
        "This method mainly deals with small classes. Use repeated observations to balance the data. There will not be any information loss using this method, but the addition of small-type repeated samples will easily lead to over-fitting, and the calculation time and storage overhead will also increase."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Upsampling or Oversampling refers to create artificial minority class data points to balance the distribution between the majority and minority class sample. Whereas, downsampling or undersampling refers to the removal of majority class data points to balance the target class distribution.[19]"
      ],
      "metadata": {
        "id": "ZKsApw4QXFpS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are various oversampling techniques such as Random Oversampler, SMOTE, ADASYN, etc, that can be used to generate artificial or duplicate minority class data points. Similarly, various undersampling techniques include Random Undersampler, TomekLinks, ENN, NearMiss, etc, that removes the minority class sample.[19]"
      ],
      "metadata": {
        "id": "1Ti2smNoXRa4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Over Sampling is used to increase the minority class by randomly duplicating them [23]"
      ],
      "metadata": {
        "id": "gp-I5BqO9axO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Idea:**\n",
        "\n",
        "The idea is to combine the oversampling and undersampling techniques to handle the imbalance in the dataset and further train a robust machine learning model."
      ],
      "metadata": {
        "id": "p8XDYnloXZfQ"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1GSVonCaZ23"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1Zze6DH82-UqfgfYK8J4gerDY18LbH7rO)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Naive RandomOverSampler**"
      ],
      "metadata": {
        "id": "hCc3-e-iMUFf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "mporting library RandomOverSampler for Over Sampling and then fitting independent features X and dependent feature y.[23]"
      ],
      "metadata": {
        "id": "iQagGi509h1_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "ros = RandomOverSampler()\n",
        "X_os, y_os = ros.fit_resample(X,y)\n"
      ],
      "metadata": {
        "id": "alrSDeBp9d22"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "[8]"
      ],
      "metadata": {
        "id": "L9bHjt-9NKYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "ros = RandomOverSampler(random_state=0)\n",
        "X_resampled, y_resampled = ros.fit_resample(X, y)\n",
        "from collections import Counter\n",
        "print(sorted(Counter(y_resampled).items()))\n",
        "[(0, 4674), (1, 4674), (2, 4674)]"
      ],
      "metadata": {
        "id": "m8qdGa0XMGGZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The main disadvantage of over sampling is, it will cause overfitting the machine learning model as we are duplicating the records of minority classes randomly [23]"
      ],
      "metadata": {
        "id": "OQ1PXbEb9vIB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **SMOTE**"
      ],
      "metadata": {
        "id": "xV-f2SHfN73h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm = SMOTE(random_state=42)\n",
        "X_res, y_res = sm.fit_resample(X_train, y_trai"
      ],
      "metadata": {
        "id": "IH0H-SNVOnHW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm = SMOTE(random_state=42)\n",
        "X_res, y_res = sm.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "Eq1bqw8RmRv5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize = (8,5))\n",
        "Train_data.RESULT_TEXT.value_counts(normalize = True).plot(kind='bar', color= ['skyblue','navy'], alpha = 0.9, rot=0)\n",
        "plt.title('Results Status Negative(0) and Positive(1) in the Imbalanced Dataset')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "V_tIM2R0O3XG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SMOTEENN:**"
      ],
      "metadata": {
        "id": "3Mz0N9vtPAKO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smt = SMOTEENN(random_state=42)\n",
        "X_res, y_res = smt.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "L9RonXQiPLQO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#SMOTENC [8]\n",
        "from imblearn.over_sampling import SMOTENC\n",
        "smote_nc = SMOTENC(categorical_features=[0, 2], random_state=0)\n",
        "X_resampled, y_resampled = smote_nc.fit_resample(X, y)\n",
        "print(sorted(Counter(y_resampled).items()))\n",
        "[(0, 30), (1, 30)]\n",
        "print(X_resampled[-5:])\n",
        "[['A' 0.5246469549655818 2]\n",
        " ['B' -0.3657680728116921 2]\n",
        " ['B' 0.9344237230779993 2]\n",
        " ['B' 0.3710891618824609 2]\n",
        " ['B' 0.3327240726719727 2]]"
      ],
      "metadata": {
        "id": "gFs0W-TgPox1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SMOTETomek:**"
      ],
      "metadata": {
        "id": "SjYMBB7FPVMm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smt = SMOTETomek(random_state=42)\n",
        "X_res, y_res = smt.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "8VliYnuLPaTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Borderline-SMOTE**"
      ],
      "metadata": {
        "id": "Gm42GVhbRRFd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import BorderlineSMOTE\n",
        "X_train_Before= X_train\n",
        "y_train_Before= y_train\n",
        "borderlineSMOTE = BorderlineSMOTE(k_neighbors = 10, random_state = 42)\n",
        "X_train, y_train = borderlineSMOTE.fit_resample(X_train_Before,y_train_Before)"
      ],
      "metadata": {
        "id": "KnRDBix5RTP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "[8] ADASYN"
      ],
      "metadata": {
        "id": "hZxf1ZI7O-HO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE, ADASYN\n",
        "X_resampled, y_resampled = SMOTE().fit_resample(X, y)\n",
        "print(sorted(Counter(y_resampled).items()))\n",
        "[(0, 4674), (1, 4674), (2, 4674)]\n",
        "clf_smote = LinearSVC().fit(X_resampled, y_resampled)\n",
        "X_resampled, y_resampled = ADASYN().fit_resample(X, y)\n",
        "print(sorted(Counter(y_resampled).items()))\n",
        "[(0, 4673), (1, 4662), (2, 4674)]\n",
        "clf_adasyn = LinearSVC().fit(X_resampled, y_resampled)"
      ],
      "metadata": {
        "id": "QbboEfXBOw2H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6.1.2-Undersampling**"
      ],
      "metadata": {
        "id": "YY2ynWWYHXL2"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l07UlmTMapWX"
      },
      "source": [
        "This method is mainly to deal with large categories. Use to reduce the number of observations in large categories to balance the data set. It is suitable when the overall data set is large, and this method can also reduce the calculation time and storage overhead (the training set samples are less). It is also possible to combine the two methods of oversampling and undersampling. The large class uses undersampling without replacement, and the small class uses oversampling with replacement."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Under Sampling is used to decrease the majority class by randomly eliminating the majority class. This is done until majority and minority class instances are balanced out [23]"
      ],
      "metadata": {
        "id": "6VHysBnE93gN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **NearMiss**"
      ],
      "metadata": {
        "id": "P2L-ltMdHiQG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing library NearMiss for Under Sampling and then fitting independent features X and dependent feature y.[23]"
      ],
      "metadata": {
        "id": "7xj8FX079-vV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import NearMiss\n",
        "nm = NearMiss()\n",
        "X_us, y_us = nm.fit_resample(X,y)"
      ],
      "metadata": {
        "id": "YjI3YEgZ995N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The main disadvantage of under-sampling is, We lose the information of data. We can use under-sampling when we have millions of records [23]"
      ],
      "metadata": {
        "id": "lJHFPFso-Gcr"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODqF6fKbbSDe"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=10OLxqUKqVGd_Uknw-Griz4Ultd9sjMYM)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **6.1.3- Combine both techniques**"
      ],
      "metadata": {
        "id": "mTKuU0Pjny6J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Edited Nearest Neighbour (ENN)**"
      ],
      "metadata": {
        "id": "dkhXFiWGoSBv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smt = SMOTEENN(random_state=42)\n",
        "X_res, y_res = smt.fit_resample(X_train, y_train"
      ],
      "metadata": {
        "id": "OYgqGn77oFod"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**SMOTETomek**"
      ],
      "metadata": {
        "id": "_NoQMXb_oc75"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smt = SMOTETomek(random_state=42)\n",
        "X_res, y_res = smt.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "k0h5WhwlooO0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cTBMrno6lFox"
      },
      "source": [
        "**Synthetic Sampling**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hf7crddvlIaY"
      },
      "source": [
        "The synthetic data sampling method uses artificial data instead of repeating the original observations to solve the imbalance. Among those algorithms, ASMOTE (Synthetic Minority Oversampling Technique) is the most common a data sampling technique.\n",
        "SMOTE works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line.\n",
        "Specifically, a random example from the minority class is first chosen. Then k of the nearest neighbors for that example are found (typically k=5). A randomly selected neighbor is chosen and a synthetic example is created at a randomly selected point between the two examples in feature space.\n",
        "This procedure can be used to create as many synthetic examples for the minority class as are required. As described in the paper, it suggests first using random undersampling to trim the number of examples in the majority class, then use SMOTE to oversample the minority class to balance the class distribution.\n",
        "The approach is effective because new synthetic examples from the minority class are created that are plausible, that is, are relatively close in feature space to existing examples from the minority class.\n",
        "A general downside of the approach is that synthetic examples are created without considering the majority class, possibly resulting in ambiguous examples if there is a strong overlap for the classes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWLw27HglTGH"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1TwyAC1SK2t7fXcdsin6aNGNq0pT48kke)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3p7TRBevkGlk"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize = (8,5))\n",
        "Train_data.RESULT_TEXT.value_counts(normalize = True).plot(kind='bar', color= ['skyblue','navy'], alpha = 0.9, rot=0)\n",
        "plt.title('Results Status Negative(0) and Positive(1) in the Imbalanced Dataset')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S0qwuF7Dkaw2"
      },
      "source": [
        "## **Data resample**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAnm9UBIkiC1"
      },
      "source": [
        "from sklearn.utils import resample\n",
        "no = Train_data[Train_data.RESULT_TEXT == 0]\n",
        "yes = Train_data[Train_data.RESULT_TEXT == 1]\n",
        "yes_oversampled = resample(yes, replace=True, n_samples=len(no), random_state=123)\n",
        "oversampled = pd.concat([no, yes_oversampled])\n",
        "fig = plt.figure(figsize = (8,5))\n",
        "oversampled.RESULT_TEXT.value_counts(normalize = True).plot(kind='bar', color= ['skyblue','navy'], alpha = 0.9, rot=0)\n",
        "plt.title('Waterbrone Disease status Nagative(0) and Postive(1) after Oversampling (Balanced Dataset)')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vj6qu54toUvU"
      },
      "source": [
        "## **SMOTE** [3,4]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SMOTEENN:[19]**"
      ],
      "metadata": {
        "id": "T841P3IEZiqM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can perform oversampling of the minority class using SMOTE technique and further perform undersample using Edited Nearest Neighbour (ENN). The advantage of performing the undersampling technique — ENN after oversampling is that it cleans or removes the noise majority class data points.\n",
        "Imblearn library comes with the implementation of SMOTEENN.[19]"
      ],
      "metadata": {
        "id": "hOTp1yn1ZqxD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smt = SMOTEENN(random_state=42)\n",
        "X_res, y_res = smt.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "4Tid24r6ZuXL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SMOTETomek:** [19]\n",
        "\n",
        "Similarly, we can perform oversampling of the minority class using SMOTE technique and further undersample or perform cleaning using the Tomek Links technique.\n",
        "Imblearn library comes with the implementation of SMOTETomek."
      ],
      "metadata": {
        "id": "bXNosJq9Z5M6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "SMOTETomek is the combination of both over sampling and under sampling using SMOTE and Tomek links [23]"
      ],
      "metadata": {
        "id": "vTkCqZgGBqPs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "smt = SMOTETomek(random_state=42)\n",
        "X_res, y_res = smt.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "id": "telst6L2Z-cQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.combine import SMOTETomek\n",
        "smotetomek = SMOTETomek()\n",
        "X_smotetomek, y_smotetomek = smotetomek.fit_resample(X,y)"
      ],
      "metadata": {
        "id": "l7n8qO2YBtic"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmcHEc1llpEt"
      },
      "source": [
        "#**📚Section 3-  Handling missing data**\n",
        "\n",
        "Missing values is one of the most common problems you saw when you are doing feature engineering/data preparation. The main reason for the missing values because of humans errors and data privacy and so on…"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data cleaning is the technique of eliminating garbage, incorrect, duplicate, corrupted, or incomplete data in a dataset as the part of the data preparation process with a motive to build reliable, uniform and standardized data sets. Python pandas is an excellent library for manipulating data and analyzing it.[20]"
      ],
      "metadata": {
        "id": "i45sOOD3baa0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are four ways you can perform data cleaning :\n",
        "- Drop the missing values\n",
        "- Replace the missing values\n",
        "- Replace each NaN with a scalar value,\n",
        "- Fill the missing values forward or backward."
      ],
      "metadata": {
        "id": "e5il5oeQbqQa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.1- How to Check Missing Values**"
      ],
      "metadata": {
        "id": "B3OkgEishyv4"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgAsYprj-ywI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27b8b518-b1a0-4e24-e46e-8903ccb591b6"
      },
      "source": [
        "np.sum(Train_data.isnull().any(axis=1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "238"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **How to find the exact location of null values**"
      ],
      "metadata": {
        "id": "B_f0em1nepku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Train_data[Train_data.isna().any(axis=1)]"
      ],
      "metadata": {
        "id": "ZG7gm1rue3Dk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "1af51e31-7d6d-41ce-f8a2-604a358fb90d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 MRNO         AGE  GENDER    DISTRICT         TEHSIL  \\\n",
              "59     K0400000124388  10 Year(s)  Female     Batgram           Alai   \n",
              "110    K0400000228342  29 Year(s)    Male  Abbottabad     Abbottabad   \n",
              "211    K0400000375499  32 Year(s)    Male    Mansehra           Oghi   \n",
              "240    K0400000412427   7 Year(s)  Female  Abbottabad     Abbottabad   \n",
              "319    K0400000485472  53 Year(s)    Male    Mansehra       Mansehra   \n",
              "...               ...         ...     ...         ...            ...   \n",
              "22802  K0400002361945  15 Year(s)    Male     Batgram       Batagram   \n",
              "22807  K04ACE20160252  20 Year(s)    Male  Abbottabad     Abbottabad   \n",
              "22841  K04ACE20177210  23 Year(s)  Female  Abbottabad     Abbottabad   \n",
              "22888  K04ACE20223641  50 Year(s)    Male  Abbottabad     Abbottabad   \n",
              "22892  K0400002419131  32 Year(s)    Male     ORAKZAI  UPPER ORAKZAI   \n",
              "\n",
              "      REPORT_VERIFIED            CPT_ID RESULT_VALUE          CPT_ID.1  \\\n",
              "59                NaN  1000000000085045            N  1000000000087207   \n",
              "110               NaN  1000000000087207            Y  1000000000087207   \n",
              "211               NaN  1000000000085060            N  1000000000087207   \n",
              "240               NaN  1000000000085651            N  1000000000087207   \n",
              "319               NaN  1000000000087207            Y  1000000000087207   \n",
              "...               ...               ...          ...               ...   \n",
              "22802             NaN  1000000000087207            N  1000000000087207   \n",
              "22807             NaN  1000000000085007            N  1000000000087207   \n",
              "22841             NaN  1000000000085007            N  1000000000087207   \n",
              "22888             NaN  1000000000085007            N  1000000000087207   \n",
              "22892             NaN  1000000000085651            N  1000000000087207   \n",
              "\n",
              "      RESULT_TEXT  \n",
              "59       Negative  \n",
              "110      Negative  \n",
              "211      Positive  \n",
              "240      Negative  \n",
              "319      Negative  \n",
              "...           ...  \n",
              "22802    Negative  \n",
              "22807    Negative  \n",
              "22841    Negative  \n",
              "22888    Negative  \n",
              "22892    Negative  \n",
              "\n",
              "[238 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d7edc02-6b60-4d2d-be3e-68358e64d187\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MRNO</th>\n",
              "      <th>AGE</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>TEHSIL</th>\n",
              "      <th>REPORT_VERIFIED</th>\n",
              "      <th>CPT_ID</th>\n",
              "      <th>RESULT_VALUE</th>\n",
              "      <th>CPT_ID.1</th>\n",
              "      <th>RESULT_TEXT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>K0400000124388</td>\n",
              "      <td>10 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>Batgram</td>\n",
              "      <td>Alai</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000085045</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>K0400000228342</td>\n",
              "      <td>29 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>K0400000375499</td>\n",
              "      <td>32 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Mansehra</td>\n",
              "      <td>Oghi</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000085060</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>240</th>\n",
              "      <td>K0400000412427</td>\n",
              "      <td>7 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000085651</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>K0400000485472</td>\n",
              "      <td>53 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Mansehra</td>\n",
              "      <td>Mansehra</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22802</th>\n",
              "      <td>K0400002361945</td>\n",
              "      <td>15 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Batgram</td>\n",
              "      <td>Batagram</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22807</th>\n",
              "      <td>K04ACE20160252</td>\n",
              "      <td>20 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22841</th>\n",
              "      <td>K04ACE20177210</td>\n",
              "      <td>23 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22888</th>\n",
              "      <td>K04ACE20223641</td>\n",
              "      <td>50 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22892</th>\n",
              "      <td>K0400002419131</td>\n",
              "      <td>32 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>ORAKZAI</td>\n",
              "      <td>UPPER ORAKZAI</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1000000000085651</td>\n",
              "      <td>N</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>238 rows × 10 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d7edc02-6b60-4d2d-be3e-68358e64d187')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0d7edc02-6b60-4d2d-be3e-68358e64d187 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0d7edc02-6b60-4d2d-be3e-68358e64d187');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-339c96ea-052d-4eb6-b648-2e793ae8ab7d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-339c96ea-052d-4eb6-b648-2e793ae8ab7d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-339c96ea-052d-4eb6-b648-2e793ae8ab7d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[20]"
      ],
      "metadata": {
        "id": "QBu8QnutfRzZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **How to check for percentages of null values in each column**"
      ],
      "metadata": {
        "id": "d6rO-FGcfU81"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "aUvr3WatjLkY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# [20]\n",
        "Train_data.notnull().mean()"
      ],
      "metadata": {
        "id": "rKjglowhfZi2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa9d293f-eceb-44e8-c09c-d0fbbcb4e788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MRNO               1.000000\n",
              "AGE                1.000000\n",
              "GENDER             1.000000\n",
              "DISTRICT           1.000000\n",
              "TEHSIL             1.000000\n",
              "REPORT_VERIFIED    0.989614\n",
              "CPT_ID             1.000000\n",
              "RESULT_VALUE       1.000000\n",
              "CPT_ID.1           1.000000\n",
              "RESULT_TEXT        1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total = Train_data.isnull().sum().sort_values(ascending=False)\n",
        "percent = (Train_data.isnull().sum()/Train_data.isnull().count()).sort_values(ascending=False)\n",
        "missing = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing.head(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "id": "9gmBMkrFiTiv",
        "outputId": "55f9f916-e757-47c6-fe3b-0502a31c584a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Total   Percent\n",
              "REPORT_VERIFIED    238  0.010386\n",
              "MRNO                 0  0.000000\n",
              "AGE                  0  0.000000\n",
              "GENDER               0  0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b1da4217-3f46-4c51-af8c-2992f791a712\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total</th>\n",
              "      <th>Percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>REPORT_VERIFIED</th>\n",
              "      <td>238</td>\n",
              "      <td>0.010386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MRNO</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GENDER</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1da4217-3f46-4c51-af8c-2992f791a712')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b1da4217-3f46-4c51-af8c-2992f791a712 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b1da4217-3f46-4c51-af8c-2992f791a712');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e8571918-5540-4160-a1b7-c5f784758a5f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8571918-5540-4160-a1b7-c5f784758a5f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e8571918-5540-4160-a1b7-c5f784758a5f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7f1bbf8cb5b0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_0['Total'].plot(kind='hist', bins=20, title='Total')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-6b0f4d54-f2bd-4aa7-a073-52d73738e2f0\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-6b0f4d54-f2bd-4aa7-a073-52d73738e2f0\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_1['Percent'].plot(kind='hist', bins=20, title='Percent')\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-72669bf8-ebcf-472b-b153-da4168630e2e\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-72669bf8-ebcf-472b-b153-da4168630e2e\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7f1bbf8ca1d0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">2-d distributions</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_2.plot(kind='scatter', x='Total', y='Percent', s=32, alpha=.8)\n",
              "plt.gca().spines[['top', 'right',]].set_visible(False)"
            ],
            "text/html": [
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-2d262ec9-5e9d-4e5b-ac6d-6a8f335f70ca\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-2d262ec9-5e9d-4e5b-ac6d-6a8f335f70ca\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<google.colab._quickchart_helpers.SectionTitle at 0x7f1bbf8cb7f0>"
            ],
            "text/html": [
              "<h4 class=\"colab-quickchart-section-title\">Values</h4>\n",
              "<style>\n",
              "  .colab-quickchart-section-title {\n",
              "      clear: both;\n",
              "  }\n",
              "</style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_3['Total'].plot(kind='line', figsize=(8, 4), title='Total')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "AAAAOATFEwAAAA5B8QQAAIBD/H86O/d3xbSJmAAAAABJRU5ErkJggg==\n",
              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-2099dcb5-f237-4256-ba47-4979d725014a\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-2099dcb5-f237-4256-ba47-4979d725014a\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "from matplotlib import pyplot as plt\n",
              "_df_4['Percent'].plot(kind='line', figsize=(8, 4), title='Percent')\n",
              "plt.gca().spines[['top', 'right']].set_visible(False)"
            ],
            "text/html": [
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              "rDExMaW3JVnbtWtn83y7d++22de+fXtrZGSkdciQIdbU1NSbPkcAAIDqZFitfDARAAAAjsX0z6QC\n",
              "AAAAv+Qwn0l1VMeOHdM999xz1f133323Zs+ebUIiAAAA18e4HwAAAA6HcT8AAAAcDiUVAAAADoeS\n",
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              "\">\n",
              "      </div>\n",
              "      <script type=\"text/javascript\">\n",
              "        (() => {\n",
              "          const chartElement = document.getElementById(\"chart-91490d48-be7e-4eba-be7b-7c02dd85c724\");\n",
              "          async function getCodeForChartHandler(event) {\n",
              "            const chartCodeResponse =  await google.colab.kernel.invokeFunction(\n",
              "                'getCodeForChart', [\"chart-91490d48-be7e-4eba-be7b-7c02dd85c724\"], {});\n",
              "            const responseJson = chartCodeResponse.data['application/json'];\n",
              "            await google.colab.notebook.addCell(responseJson.code, 'code');\n",
              "          }\n",
              "          chartElement.onclick = getCodeForChartHandler;\n",
              "        })();\n",
              "      </script>\n",
              "      <style>\n",
              "        .colab-quickchart-chart-with-code  {\n",
              "            display: block;\n",
              "            float: left;\n",
              "            border: 1px solid transparent;\n",
              "        }\n",
              "\n",
              "        .colab-quickchart-chart-with-code:hover {\n",
              "            cursor: pointer;\n",
              "            border: 1px solid #aaa;\n",
              "        }\n",
              "      </style>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**If you want to see missing values for all columns, use this command:**"
      ],
      "metadata": {
        "id": "WS3jnHEtioGf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Train_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjWa2BueiuZ1",
        "outputId": "67b5e867-8d02-4f9a-fb7b-5a0c27aa0f20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MRNO                 0\n",
              "AGE                  0\n",
              "GENDER               0\n",
              "DISTRICT             0\n",
              "TEHSIL               0\n",
              "REPORT_VERIFIED    238\n",
              "CPT_ID               0\n",
              "RESULT_VALUE         0\n",
              "CPT_ID.1             0\n",
              "RESULT_TEXT          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.2-Visualization of Missing Values**"
      ],
      "metadata": {
        "id": "3_HLn8hrjfC_"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKSUNs2rxOy1"
      },
      "source": [
        "### **Missingo**[10]\n",
        "It is widespread to find missing values in a real-world dataset. We need to understand the missing values before imputing. Missingo offers a quick and helpful way to visualize the missing values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GBu0R6TxaR5"
      },
      "source": [
        "**Installation**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_LqBtqghxd_7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "411aa4b8-d09f-461f-f367-de46013123ac"
      },
      "source": [
        "!pip install missingno\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: missingno in /usr/local/lib/python3.10/dist-packages (0.5.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from missingno) (1.23.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from missingno) (3.7.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from missingno) (1.11.3)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (from missingno) (0.12.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (4.44.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->missingno) (2.8.2)\n",
            "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.10/dist-packages (from seaborn->missingno) (1.5.3)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->seaborn->missingno) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FADNe9Lix3Ys"
      },
      "source": [
        "**Usage**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqepihD_xoTi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "195c247c-d505-4500-e50f-10859fbce9a2"
      },
      "source": [
        "# importing necessary libraries\n",
        "import pandas as pd\n",
        "import missingno as mi\n",
        "# reading the dummy dataset\n",
        "#data = pd.read_excel(“dummy.xlsx”)\n",
        "# checking missing values\n",
        "Train_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MRNO                 0\n",
              "AGE                  0\n",
              "GENDER               0\n",
              "DISTRICT             0\n",
              "TEHSIL               0\n",
              "REPORT_VERIFIED    238\n",
              "CPT_ID               0\n",
              "RESULT_VALUE         0\n",
              "CPT_ID.1             0\n",
              "RESULT_TEXT          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMyjK33fyAIr"
      },
      "source": [
        "Dummy dataset has 11 rows and four columns. Missing values presented in Min, Temp, and city variables. We can visualize using a bar graph and matrix. It also supports heatmap, dendrogram. For more details, please check this Github repository."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jVLeXv2yCDd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "82f22514-e75e-4bd1-be1d-502d469756f4"
      },
      "source": [
        "#Visualizing using missingo\n",
        "print('Visualizing missing value using bar graph')\n",
        "mi.bar(Train_data, figsize = (10,5))\n",
        "print('Visualizing missing value using matrix')\n",
        "mi.matrix(Train_data, figsize = (10,5) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Visualizing missing value using bar graph\n",
            "Visualizing missing value using matrix\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYV__mY_ot7z"
      },
      "source": [
        "**Heatmap of missing values**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbSrUwtmowVM"
      },
      "source": [
        "Heatmaps are also useful to visualize your missing values, in particular at which point of the data do missing values exists."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJ1zvi_BpGLq"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BibF54QEo0xB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "outputId": "615a33ab-aed6-4c51-83bc-ac3d39cb8e0a"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(Train_data.isnull(), yticklabels=False, cmap='viridis', cbar=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bjlno6_vJrWT"
      },
      "source": [
        "## **2.3-Fill Missing Values in a Dataset**\n",
        "\n",
        "The scikit-learn library in Python offers the **SimpleImputer()** class which can be used for filling the missing values based on:\n",
        "\n",
        "- Mean of the known values\n",
        "- Median of the known values\n",
        "- Most frequent value among the known values\n",
        "\n",
        "So let’s go through all these methods one by one for filling the missing values of a dataset. I will first create a very simple dataset with some missing values:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwAapSDzIlmZ"
      },
      "source": [
        "import numpy as np\n",
        "data = np.array([[10, np.nan, 8],\n",
        "                 [9, 8, np.nan],\n",
        "                 [7, 10, 9]])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7T0jy1jtKHir",
        "outputId": "cc1359d2-01e9-4725-958d-ff9e04d3acbf"
      },
      "source": [
        "print(data)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10. nan  8.]\n",
            " [ 9.  8. nan]\n",
            " [ 7. 10.  9.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Data imputation**"
      ],
      "metadata": {
        "id": "bJvmZcJvmqeM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Filling Values with mean and Median**"
      ],
      "metadata": {
        "id": "7niTr_ixGxVB"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfYMTMvOKNAi"
      },
      "source": [
        "Here is how you can use the Mean of the other known values for filling the missing values:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SAPCWk9XKOli"
      },
      "source": [
        "# Filling Values with mean\n",
        "from sklearn.impute import SimpleImputer\n",
        "mean_values = SimpleImputer(strategy='mean')\n",
        "data1 = mean_values.fit_transform(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nAxbT6vXKWUL",
        "outputId": "81f8a178-25cd-4c78-99cd-0a5fc8ba9b68"
      },
      "source": [
        "print(data1)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10.   9.   8. ]\n",
            " [ 9.   8.   8.5]\n",
            " [ 7.  10.   9. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzSA3l3YKaCa"
      },
      "source": [
        "Here is how you can use the Median of the other known values for filling the missing values:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJY6oLTkKfKZ",
        "outputId": "68aae929-c794-43a0-9cee-fcf777bfe977"
      },
      "source": [
        "# Filling Values with median\n",
        "from sklearn.impute import SimpleImputer\n",
        "median_values = SimpleImputer(strategy='median')\n",
        "data2 = median_values.fit_transform(data)\n",
        "print(data2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10.   9.   8. ]\n",
            " [ 9.   8.   8.5]\n",
            " [ 7.  10.   9. ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Filling Values with most Frequent Value**"
      ],
      "metadata": {
        "id": "By9D4oYjHA8w"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHelgUGeKnfi"
      },
      "source": [
        "Here is how you can use the most frequent value among the other known values for filling the missing values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NE900achKj6a",
        "outputId": "03c82c2b-a9d6-4ef0-df06-5fb50000386c"
      },
      "source": [
        "# Filling values with most frequent values\n",
        "from sklearn.impute import SimpleImputer\n",
        "most_frequent = SimpleImputer(strategy='most_frequent')\n",
        "data3 = most_frequent.fit_transform(data)\n",
        "print(data3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[10.  8.  8.]\n",
            " [ 9.  8.  8.]\n",
            " [ 7. 10.  9.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCTUDxFjzd3g"
      },
      "source": [
        "**Mean/mode/median imputation**\n",
        "\n",
        "We can also do mean/median/mode imputation. For numerical data, we can compute it’s mean or median and use the result to replace missing values and for categorical (non-numerical) data, we can compute its mode to replace the missing value [24]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHy-lrI8zipr"
      },
      "source": [
        "For imputing, there are 3 main techniques shown below.\n",
        "\n",
        "- fillna — filling in null values based on given value (mean, median, mode, or specified value)\n",
        "- bfill / ffill — stands for backward fill and forward fill (filling in missing values based on the value after or before the column.)\n",
        "- Simple Imputer — Sk-learn’s built-in function that imputes missing values (commonly used alongside a pipeline when building ML models)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzkrPxWtzwlr"
      },
      "source": [
        "Below you can find examples of applying these methods to the price column if it had missing values."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "Train_data = pd.read_csv('/content/drive/MyDrive/Datasets/Malyria_2-21-2021.csv',encoding = 'latin')"
      ],
      "metadata": {
        "id": "xGrR_8ULm_NM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Train_data['RESULT_TEXT'].replace({'Negative': 0, 'Positive': 1},inplace = True)"
      ],
      "metadata": {
        "id": "YKse58-Fq1E_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQln8P7xzyN1"
      },
      "source": [
        "# imputing price with mean\n",
        "# imputing price with mean\n",
        "RESULT_TEXT_mean_value = round(Train_data['RESULT_TEXT'].mean(), 2)\n",
        "Train_data['RESULT_TEXT'].fillna(RESULT_TEXT_mean_value, inplace=True)\n",
        "\n",
        "# imputing price with median\n",
        "price_median_value = round(Train_data['RESULT_TEXT'].median(), 2)\n",
        "Train_data['RESULT_TEXT'].fillna(price_median_value, inplace=True)\n",
        "\n",
        "# imputing with bfill or ffill\n",
        "Train_data['RESULT_TEXT'].bfill(inplace=True)\n",
        "Train_data['RESULT_TEXT'].ffill(inplace=True)\n",
        "\n",
        "# imputing with SimpleImputor from the sklearn library\n",
        "from sklearn.impute import SimpleImputer\n",
        "# define the imputer\n",
        "imr = SimpleImputer(missing_values=np.nan, strategy='mean') # or median\n",
        "\n",
        "Train_data[['RESULT_TEXT']] = imr.fit_transform(Train_data[['RESULT_TEXT']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Replace each NaN with scalar value**"
      ],
      "metadata": {
        "id": "9jHt7T2ettzO"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDx8A9_Uz5jG"
      },
      "source": [
        "**Replace each NaN with a scalar value,**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wyjcdju7z8CP"
      },
      "source": [
        "To replace values, the fillna function is also used.Replace each NaN we have in the dataset, we can use the replace() method [24]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkqWOIfmz__l"
      },
      "source": [
        "You define the value you want to replace in the key, and the substitute in the value — {column_name: replacement_for_NA}\n",
        "\n",
        "Here are examples for replacing values in the columns reviews_per_month and name\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJIzbrvo0IgT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "885ce67d-e373-4ebc-f99e-6aef8f3ed551"
      },
      "source": [
        "# replace null values in reviews_per_month with 0\n",
        "Train_data.fillna({'RESULT_TEXT':0}, inplace=True)\n",
        "\n",
        "missing_cols(Train_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-5456ee047529>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mTrain_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'RESULT_TEXT'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmissing_cols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTrain_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'missing_cols' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2256NAA0P0b"
      },
      "source": [
        "# replace null values in name with 'None'\n",
        "airbnb.fillna({'name':'None'}, inplace=True)\n",
        "\n",
        "missing_cols(airbnb)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import NaN\n",
        "Train_data.replace({NaN:1.00})"
      ],
      "metadata": {
        "id": "X7pDcCfiH3zR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "outputId": "09eb089f-2e76-4caf-9fac-66a3a2d939e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0      SECTION_NO            MRNO        PATIENT_NAME  \\\n",
              "0               1  K04HEM20123774  K0460000001363  Saqib Mehmood Khan   \n",
              "1               2  K04HEM18160758  K0460000001680      Muhammad Iqbal   \n",
              "2               3  K04HEM18160758  K0460000001680      Muhammad Iqbal   \n",
              "3               4  K04HEM17056995  K0460000000106       Shahbaz Ahmad   \n",
              "4               5  K04HEM17073233  K0460000000150       Mehfooz Ahmad   \n",
              "...           ...             ...             ...                 ...   \n",
              "22911       22912  K04HEM21017730  K0400002457330       Rooh Ul Ameen   \n",
              "22912       22913  K04HEM21018207  K0400002459565       Zeshan Rustam   \n",
              "22913       22914  K04HEM21019179  K0400002461645              M Umer   \n",
              "22914       22915  K04HEM21021055  K04ACE21027742      Shaheen Akhtar   \n",
              "22915       22916  K04HEM21020544  K0400002464110        Fakhraj Bibi   \n",
              "\n",
              "               FATHER_NAME HUSBAND_NAME       NIC_NEW REFERRED_FROM  \\\n",
              "0      MUHAMMAD AFSAR KHAN          1.0  1.310163e+12           1.0   \n",
              "1             ABDUR REHMAN          1.0  1.310134e+12           1.0   \n",
              "2             ABDUR REHMAN          1.0  1.310134e+12           1.0   \n",
              "3             MUMTAZ AHMAD          1.0  4.220136e+12           1.0   \n",
              "4             HAIDER ZAMAN          1.0  1.310109e+12           1.0   \n",
              "...                    ...          ...           ...           ...   \n",
              "22911          ABDUL SALAM          1.0  8.139494e+10           1.0   \n",
              "22912             M RUSTAM          1.0  1.000000e+00           1.0   \n",
              "22913             M JUNAID          1.0  1.000000e+00           1.0   \n",
              "22914                  1.0     M SALEEM  1.000000e+00           1.0   \n",
              "22915              M FAYAZ          1.0  1.000000e+00           1.0   \n",
              "\n",
              "              AGE  GENDER  NO_OF_ADMISSION    DISTRICT      TEHSIL  \\\n",
              "0      34 Year(s)    Male                0  Abbottabad     havelia   \n",
              "1      43 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "2      43 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "3      28 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "4      42 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "...           ...     ...              ...         ...         ...   \n",
              "22911  13 Year(s)    Male                1     Haripur     Haripur   \n",
              "22912  20 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "22913  21 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "22914  45 Year(s)  Female                0  Abbottabad  Abbottabad   \n",
              "22915  22 Year(s)  Female                0  Abbottabad  Abbottabad   \n",
              "\n",
              "                  REPORT_VERIFIED            CPT_ID RESULT_VALUE  \\\n",
              "0      11/12/2020 10:46:53 A11P11  1000000000085007            Y   \n",
              "1          9/29/2018 9:26:21 A9P9  1000000000087207            Y   \n",
              "2          9/29/2018 9:26:07 A9P9  1000000000085007            Y   \n",
              "3           5/4/2017 9:48:37 A5P5  1000000000085007            Y   \n",
              "4          6/3/2017 11:59:38 A6P6  1000000000085007            Y   \n",
              "...                           ...               ...          ...   \n",
              "22911     2/10/2021 16:53:12 A2P2  1000000000085060            Y   \n",
              "22912     2/11/2021 11:49:58 A2P2  1000000000085007            Y   \n",
              "22913     2/13/2021 11:55:21 A2P2  1000000000087207            Y   \n",
              "22914     2/17/2021 11:09:47 A2P2  1000000000087207            Y   \n",
              "22915     2/16/2021 12:37:12 A2P2  1000000000085651            Y   \n",
              "\n",
              "               CPT_ID.1    SECTION_NO.1  RESULT_TEXT  \n",
              "0      1000000000087207  K04HEM20123774          0.0  \n",
              "1      1000000000087207  K04HEM18160758          0.0  \n",
              "2      1000000000087207  K04HEM18160758          0.0  \n",
              "3      1000000000087207  K04HEM17056995          0.0  \n",
              "4      1000000000087207  K04HEM17073233          0.0  \n",
              "...                 ...             ...          ...  \n",
              "22911  1000000000087207  K04HEM21017730          0.0  \n",
              "22912  1000000000087207  K04HEM21018207          0.0  \n",
              "22913  1000000000087207  K04HEM21019179          0.0  \n",
              "22914  1000000000087207  K04HEM21021055          0.0  \n",
              "22915  1000000000087207  K04HEM21020544          0.0  \n",
              "\n",
              "[22916 rows x 19 columns]"
            ],
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              "      <td>2/13/2021 11:55:21 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21019179</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>22914</th>\n",
              "      <td>22915</td>\n",
              "      <td>K04HEM21021055</td>\n",
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              "      <td>Shaheen Akhtar</td>\n",
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              "      <td>45 Year(s)</td>\n",
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              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/17/2021 11:09:47 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21021055</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>22915</th>\n",
              "      <td>22916</td>\n",
              "      <td>K04HEM21020544</td>\n",
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              "      <td>1.000000e+00</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/16/2021 12:37:12 A2P2</td>\n",
              "      <td>1000000000085651</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21020544</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to replace with a Scalar Value, use fillna() method"
      ],
      "metadata": {
        "id": "40FYUDwiH9L4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Train_data.fillna(12)"
      ],
      "metadata": {
        "id": "Pna6q-YtID9k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "outputId": "a194724e-4547-4b4a-f3dd-ea1613b7a7ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       Unnamed: 0      SECTION_NO            MRNO        PATIENT_NAME  \\\n",
              "0               1  K04HEM20123774  K0460000001363  Saqib Mehmood Khan   \n",
              "1               2  K04HEM18160758  K0460000001680      Muhammad Iqbal   \n",
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              "3               4  K04HEM17056995  K0460000000106       Shahbaz Ahmad   \n",
              "4               5  K04HEM17073233  K0460000000150       Mehfooz Ahmad   \n",
              "...           ...             ...             ...                 ...   \n",
              "22911       22912  K04HEM21017730  K0400002457330       Rooh Ul Ameen   \n",
              "22912       22913  K04HEM21018207  K0400002459565       Zeshan Rustam   \n",
              "22913       22914  K04HEM21019179  K0400002461645              M Umer   \n",
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              "\n",
              "               FATHER_NAME HUSBAND_NAME       NIC_NEW REFERRED_FROM  \\\n",
              "0      MUHAMMAD AFSAR KHAN           12  1.310163e+12            12   \n",
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              "...                    ...          ...           ...           ...   \n",
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              "\n",
              "              AGE  GENDER  NO_OF_ADMISSION    DISTRICT      TEHSIL  \\\n",
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              "1      43 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
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              "...           ...     ...              ...         ...         ...   \n",
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              "22915  22 Year(s)  Female                0  Abbottabad  Abbottabad   \n",
              "\n",
              "                  REPORT_VERIFIED            CPT_ID RESULT_VALUE  \\\n",
              "0      11/12/2020 10:46:53 A11P11  1000000000085007            Y   \n",
              "1          9/29/2018 9:26:21 A9P9  1000000000087207            Y   \n",
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              "3           5/4/2017 9:48:37 A5P5  1000000000085007            Y   \n",
              "4          6/3/2017 11:59:38 A6P6  1000000000085007            Y   \n",
              "...                           ...               ...          ...   \n",
              "22911     2/10/2021 16:53:12 A2P2  1000000000085060            Y   \n",
              "22912     2/11/2021 11:49:58 A2P2  1000000000085007            Y   \n",
              "22913     2/13/2021 11:55:21 A2P2  1000000000087207            Y   \n",
              "22914     2/17/2021 11:09:47 A2P2  1000000000087207            Y   \n",
              "22915     2/16/2021 12:37:12 A2P2  1000000000085651            Y   \n",
              "\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>K04HEM17056995</td>\n",
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              "      <td>12</td>\n",
              "      <td>4.220136e+12</td>\n",
              "      <td>12</td>\n",
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              "      <td>Abbottabad</td>\n",
              "      <td>5/4/2017 9:48:37 A5P5</td>\n",
              "      <td>1000000000085007</td>\n",
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              "      <td>K04HEM17073233</td>\n",
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              "      <td>Mehfooz Ahmad</td>\n",
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              "      <td>12</td>\n",
              "      <td>42 Year(s)</td>\n",
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              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
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              "      <td>1000000000085007</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
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              "      <td>22912</td>\n",
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              "      <td>Rooh Ul Ameen</td>\n",
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              "      <td>12</td>\n",
              "      <td>8.139494e+10</td>\n",
              "      <td>12</td>\n",
              "      <td>13 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>2/10/2021 16:53:12 A2P2</td>\n",
              "      <td>1000000000085060</td>\n",
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              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21017730</td>\n",
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              "      <td>22913</td>\n",
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              "      <td>12</td>\n",
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              "      <td>12</td>\n",
              "      <td>20 Year(s)</td>\n",
              "      <td>Male</td>\n",
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              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/11/2021 11:49:58 A2P2</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
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              "      <td>K04HEM21018207</td>\n",
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              "    <tr>\n",
              "      <th>22913</th>\n",
              "      <td>22914</td>\n",
              "      <td>K04HEM21019179</td>\n",
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              "      <td>M Umer</td>\n",
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              "      <td>1.200000e+01</td>\n",
              "      <td>12</td>\n",
              "      <td>21 Year(s)</td>\n",
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              "      <td>Abbottabad</td>\n",
              "      <td>2/13/2021 11:55:21 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21019179</td>\n",
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              "      <td>22915</td>\n",
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              "      <td>K04ACE21027742</td>\n",
              "      <td>Shaheen Akhtar</td>\n",
              "      <td>12</td>\n",
              "      <td>M SALEEM</td>\n",
              "      <td>1.200000e+01</td>\n",
              "      <td>12</td>\n",
              "      <td>45 Year(s)</td>\n",
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              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/17/2021 11:09:47 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
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              "      <td>K04HEM21021055</td>\n",
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              "    <tr>\n",
              "      <th>22915</th>\n",
              "      <td>22916</td>\n",
              "      <td>K04HEM21020544</td>\n",
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              "      <td>12</td>\n",
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              "      <td>12</td>\n",
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              "      <td>Abbottabad</td>\n",
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              "<p>22916 rows × 19 columns</p>\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f8aa33a7-cca7-4042-8430-b2a6d731bbe1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Drop the missine value colum**"
      ],
      "metadata": {
        "id": "6k2qTQAruC9V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drop the missing values**[24]"
      ],
      "metadata": {
        "id": "gYpEI28nHXAD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Train_data.dropna()"
      ],
      "metadata": {
        "id": "7lpTbnhrHkOQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "1e3711a2-20dc-450f-cf42-03cb152a91fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Unnamed: 0, SECTION_NO, MRNO, PATIENT_NAME, FATHER_NAME, HUSBAND_NAME, NIC_NEW, REFERRED_FROM, AGE, GENDER, NO_OF_ADMISSION, DISTRICT, TEHSIL, REPORT_VERIFIED, CPT_ID, RESULT_VALUE, CPT_ID.1, SECTION_NO.1, RESULT_TEXT]\n",
              "Index: []"
            ],
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c1bc5b96-d419-404e-aa44-bf79ad5ca1fb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c1bc5b96-d419-404e-aa44-bf79ad5ca1fb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Default axis=0 will excludes an entire row for an NaN value."
      ],
      "metadata": {
        "id": "_jFIf216Hn2o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Fill missing value forward or Backward**"
      ],
      "metadata": {
        "id": "LOcWYucIuT8N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fill the missing values forward or backward.**\n"
      ],
      "metadata": {
        "id": "mGdLBP-tJ9w8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To fill forward or backward, use the methods pad or fill, and to fill backward, use bfill and backfill [24]"
      ],
      "metadata": {
        "id": "KMeEv153KJSk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Train_data.fillna(method='backfill')"
      ],
      "metadata": {
        "id": "XHjKVue3KQmE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "outputId": "08cbe2ab-b111-41d6-ec41-f50c513f8681"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0      SECTION_NO            MRNO        PATIENT_NAME  \\\n",
              "0               1  K04HEM20123774  K0460000001363  Saqib Mehmood Khan   \n",
              "1               2  K04HEM18160758  K0460000001680      Muhammad Iqbal   \n",
              "2               3  K04HEM18160758  K0460000001680      Muhammad Iqbal   \n",
              "3               4  K04HEM17056995  K0460000000106       Shahbaz Ahmad   \n",
              "4               5  K04HEM17073233  K0460000000150       Mehfooz Ahmad   \n",
              "...           ...             ...             ...                 ...   \n",
              "22911       22912  K04HEM21017730  K0400002457330       Rooh Ul Ameen   \n",
              "22912       22913  K04HEM21018207  K0400002459565       Zeshan Rustam   \n",
              "22913       22914  K04HEM21019179  K0400002461645              M Umer   \n",
              "22914       22915  K04HEM21021055  K04ACE21027742      Shaheen Akhtar   \n",
              "22915       22916  K04HEM21020544  K0400002464110        Fakhraj Bibi   \n",
              "\n",
              "               FATHER_NAME HUSBAND_NAME       NIC_NEW REFERRED_FROM  \\\n",
              "0      MUHAMMAD AFSAR KHAN        YASIR  1.310163e+12        Others   \n",
              "1             ABDUR REHMAN        YASIR  1.310134e+12        Others   \n",
              "2             ABDUR REHMAN        YASIR  1.310134e+12        Others   \n",
              "3             MUMTAZ AHMAD        YASIR  4.220136e+12        Others   \n",
              "4             HAIDER ZAMAN        YASIR  1.310109e+12        Others   \n",
              "...                    ...          ...           ...           ...   \n",
              "22911          ABDUL SALAM     M SALEEM  8.139494e+10           NaN   \n",
              "22912             M RUSTAM     M SALEEM           NaN           NaN   \n",
              "22913             M JUNAID     M SALEEM           NaN           NaN   \n",
              "22914              M FAYAZ     M SALEEM           NaN           NaN   \n",
              "22915              M FAYAZ          NaN           NaN           NaN   \n",
              "\n",
              "              AGE  GENDER  NO_OF_ADMISSION    DISTRICT      TEHSIL  \\\n",
              "0      34 Year(s)    Male                0  Abbottabad     havelia   \n",
              "1      43 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "2      43 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "3      28 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "4      42 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "...           ...     ...              ...         ...         ...   \n",
              "22911  13 Year(s)    Male                1     Haripur     Haripur   \n",
              "22912  20 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "22913  21 Year(s)    Male                0  Abbottabad  Abbottabad   \n",
              "22914  45 Year(s)  Female                0  Abbottabad  Abbottabad   \n",
              "22915  22 Year(s)  Female                0  Abbottabad  Abbottabad   \n",
              "\n",
              "                  REPORT_VERIFIED            CPT_ID RESULT_VALUE  \\\n",
              "0      11/12/2020 10:46:53 A11P11  1000000000085007            Y   \n",
              "1          9/29/2018 9:26:21 A9P9  1000000000087207            Y   \n",
              "2          9/29/2018 9:26:07 A9P9  1000000000085007            Y   \n",
              "3           5/4/2017 9:48:37 A5P5  1000000000085007            Y   \n",
              "4          6/3/2017 11:59:38 A6P6  1000000000085007            Y   \n",
              "...                           ...               ...          ...   \n",
              "22911     2/10/2021 16:53:12 A2P2  1000000000085060            Y   \n",
              "22912     2/11/2021 11:49:58 A2P2  1000000000085007            Y   \n",
              "22913     2/13/2021 11:55:21 A2P2  1000000000087207            Y   \n",
              "22914     2/17/2021 11:09:47 A2P2  1000000000087207            Y   \n",
              "22915     2/16/2021 12:37:12 A2P2  1000000000085651            Y   \n",
              "\n",
              "               CPT_ID.1    SECTION_NO.1 RESULT_TEXT  \n",
              "0      1000000000087207  K04HEM20123774    Negative  \n",
              "1      1000000000087207  K04HEM18160758    Negative  \n",
              "2      1000000000087207  K04HEM18160758    Negative  \n",
              "3      1000000000087207  K04HEM17056995    Negative  \n",
              "4      1000000000087207  K04HEM17073233    Negative  \n",
              "...                 ...             ...         ...  \n",
              "22911  1000000000087207  K04HEM21017730    Negative  \n",
              "22912  1000000000087207  K04HEM21018207    Negative  \n",
              "22913  1000000000087207  K04HEM21019179    Negative  \n",
              "22914  1000000000087207  K04HEM21021055    Negative  \n",
              "22915  1000000000087207  K04HEM21020544    Negative  \n",
              "\n",
              "[22916 rows x 19 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-706999f5-2af8-4e84-873f-62da20ce00dc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>SECTION_NO</th>\n",
              "      <th>MRNO</th>\n",
              "      <th>PATIENT_NAME</th>\n",
              "      <th>FATHER_NAME</th>\n",
              "      <th>HUSBAND_NAME</th>\n",
              "      <th>NIC_NEW</th>\n",
              "      <th>REFERRED_FROM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>NO_OF_ADMISSION</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>TEHSIL</th>\n",
              "      <th>REPORT_VERIFIED</th>\n",
              "      <th>CPT_ID</th>\n",
              "      <th>RESULT_VALUE</th>\n",
              "      <th>CPT_ID.1</th>\n",
              "      <th>SECTION_NO.1</th>\n",
              "      <th>RESULT_TEXT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>K04HEM20123774</td>\n",
              "      <td>K0460000001363</td>\n",
              "      <td>Saqib Mehmood Khan</td>\n",
              "      <td>MUHAMMAD AFSAR KHAN</td>\n",
              "      <td>YASIR</td>\n",
              "      <td>1.310163e+12</td>\n",
              "      <td>Others</td>\n",
              "      <td>34 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>havelia</td>\n",
              "      <td>11/12/2020 10:46:53 A11P11</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM20123774</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>K0460000001680</td>\n",
              "      <td>Muhammad Iqbal</td>\n",
              "      <td>ABDUR REHMAN</td>\n",
              "      <td>YASIR</td>\n",
              "      <td>1.310134e+12</td>\n",
              "      <td>Others</td>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>9/29/2018 9:26:21 A9P9</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>K0460000001680</td>\n",
              "      <td>Muhammad Iqbal</td>\n",
              "      <td>ABDUR REHMAN</td>\n",
              "      <td>YASIR</td>\n",
              "      <td>1.310134e+12</td>\n",
              "      <td>Others</td>\n",
              "      <td>43 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>9/29/2018 9:26:07 A9P9</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM18160758</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>K04HEM17056995</td>\n",
              "      <td>K0460000000106</td>\n",
              "      <td>Shahbaz Ahmad</td>\n",
              "      <td>MUMTAZ AHMAD</td>\n",
              "      <td>YASIR</td>\n",
              "      <td>4.220136e+12</td>\n",
              "      <td>Others</td>\n",
              "      <td>28 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>5/4/2017 9:48:37 A5P5</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM17056995</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>K04HEM17073233</td>\n",
              "      <td>K0460000000150</td>\n",
              "      <td>Mehfooz Ahmad</td>\n",
              "      <td>HAIDER ZAMAN</td>\n",
              "      <td>YASIR</td>\n",
              "      <td>1.310109e+12</td>\n",
              "      <td>Others</td>\n",
              "      <td>42 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>6/3/2017 11:59:38 A6P6</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM17073233</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22911</th>\n",
              "      <td>22912</td>\n",
              "      <td>K04HEM21017730</td>\n",
              "      <td>K0400002457330</td>\n",
              "      <td>Rooh Ul Ameen</td>\n",
              "      <td>ABDUL SALAM</td>\n",
              "      <td>M SALEEM</td>\n",
              "      <td>8.139494e+10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>Haripur</td>\n",
              "      <td>2/10/2021 16:53:12 A2P2</td>\n",
              "      <td>1000000000085060</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21017730</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22912</th>\n",
              "      <td>22913</td>\n",
              "      <td>K04HEM21018207</td>\n",
              "      <td>K0400002459565</td>\n",
              "      <td>Zeshan Rustam</td>\n",
              "      <td>M RUSTAM</td>\n",
              "      <td>M SALEEM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/11/2021 11:49:58 A2P2</td>\n",
              "      <td>1000000000085007</td>\n",
              "      <td>Y</td>\n",
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              "      <td>K04HEM21018207</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22913</th>\n",
              "      <td>22914</td>\n",
              "      <td>K04HEM21019179</td>\n",
              "      <td>K0400002461645</td>\n",
              "      <td>M Umer</td>\n",
              "      <td>M JUNAID</td>\n",
              "      <td>M SALEEM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21 Year(s)</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/13/2021 11:55:21 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21019179</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22914</th>\n",
              "      <td>22915</td>\n",
              "      <td>K04HEM21021055</td>\n",
              "      <td>K04ACE21027742</td>\n",
              "      <td>Shaheen Akhtar</td>\n",
              "      <td>M FAYAZ</td>\n",
              "      <td>M SALEEM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/17/2021 11:09:47 A2P2</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21021055</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22915</th>\n",
              "      <td>22916</td>\n",
              "      <td>K04HEM21020544</td>\n",
              "      <td>K0400002464110</td>\n",
              "      <td>Fakhraj Bibi</td>\n",
              "      <td>M FAYAZ</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22 Year(s)</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>Abbottabad</td>\n",
              "      <td>2/16/2021 12:37:12 A2P2</td>\n",
              "      <td>1000000000085651</td>\n",
              "      <td>Y</td>\n",
              "      <td>1000000000087207</td>\n",
              "      <td>K04HEM21020544</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22916 rows × 19 columns</p>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-706999f5-2af8-4e84-873f-62da20ce00dc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-706999f5-2af8-4e84-873f-62da20ce00dc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6903b21b-9efd-4405-98e7-f9334ab04d5b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6903b21b-9efd-4405-98e7-f9334ab04d5b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6903b21b-9efd-4405-98e7-f9334ab04d5b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sryKvX_eg5-m"
      },
      "source": [
        "### **Filling missing value with KNNImputer[11]**\n",
        "\n",
        "Scikit-learn offers multiple ways to impute missing values. Here, we consider two approaches. The SimpleImputer class provides basic strategies for imputing missing values (through the mean or median for example). A more sophisticated approach the KNNImputer class, which provides imputation for filling in missing values using the K-Nearest Neighbors approach. Each missing value is imputed using values from the n_neighbors nearest neighbors that have a value for the particular feature. The values of the neighbors are averaged uniformly or weighted by distance to each neighbor.\n",
        "Below, we show an example application using both imputation methods:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIInDHsphTp8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09e2dd58-fa24-432d-b9be-35a9562b182e"
      },
      "source": [
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import SimpleImputer, KNNImputer\n",
        "from sklearn.datasets import make_classification\n",
        "import pandas as pd\n",
        "X, y = make_classification(n_samples=5, n_features=4, n_classes=2, random_state=123)\n",
        "X = pd.DataFrame(X, columns=['Feature_1', 'Feature_2', 'Feature_3', 'Feature_4'])\n",
        "print(X.iloc[1,2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.2129830531512615\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40vt2q_4hY17"
      },
      "source": [
        "Transform X[1, 2] to a missing value:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rT_3ELsphdJT"
      },
      "source": [
        "X.iloc[1, 2] = float('NaN')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7RCOaTLhfT6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e16b0d63-8694-4013-b591-bb1db38234b0"
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Feature_1  Feature_2  Feature_3  Feature_4\n",
              "0   0.070111  -0.727615   1.904188  -1.966186\n",
              "1   0.410745   0.155161        NaN  -2.791157\n",
              "2  -0.337408  -1.069994   0.090022   0.430782\n",
              "3  -0.334743  -0.528369  -0.989942   1.480690\n",
              "4   0.336622   1.802785  -1.578173   1.022805"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50804e04-cbbf-49f8-8f00-ef49057345bb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature_1</th>\n",
              "      <th>Feature_2</th>\n",
              "      <th>Feature_3</th>\n",
              "      <th>Feature_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.070111</td>\n",
              "      <td>-0.727615</td>\n",
              "      <td>1.904188</td>\n",
              "      <td>-1.966186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.410745</td>\n",
              "      <td>0.155161</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-2.791157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.337408</td>\n",
              "      <td>-1.069994</td>\n",
              "      <td>0.090022</td>\n",
              "      <td>0.430782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.334743</td>\n",
              "      <td>-0.528369</td>\n",
              "      <td>-0.989942</td>\n",
              "      <td>1.480690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.336622</td>\n",
              "      <td>1.802785</td>\n",
              "      <td>-1.578173</td>\n",
              "      <td>1.022805</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50804e04-cbbf-49f8-8f00-ef49057345bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-50804e04-cbbf-49f8-8f00-ef49057345bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-50804e04-cbbf-49f8-8f00-ef49057345bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-37e7aa8a-6284-492e-81dc-b8ed15c97dbc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-37e7aa8a-6284-492e-81dc-b8ed15c97dbc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-37e7aa8a-6284-492e-81dc-b8ed15c97dbc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDIAq3Fohj9q"
      },
      "source": [
        "First we make use of the simple imputer:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5NwBvlK3hl5i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "509ea019-b377-4c03-b73f-d12c12087f7d"
      },
      "source": [
        "imputer_simple = SimpleImputer()\n",
        "\n",
        "pd.DataFrame(imputer_simple.fit_transform(X))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2         3\n",
              "0  0.070111 -0.727615  1.904188 -1.966186\n",
              "1  0.410745  0.155161 -0.143476 -2.791157\n",
              "2 -0.337408 -1.069994  0.090022  0.430782\n",
              "3 -0.334743 -0.528369 -0.989942  1.480690\n",
              "4  0.336622  1.802785 -1.578173  1.022805"
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              "      <td>0.410745</td>\n",
              "      <td>0.155161</td>\n",
              "      <td>-0.143476</td>\n",
              "      <td>-2.791157</td>\n",
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              "    <tr>\n",
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              "      buttonEl.style.display =\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3595a00c-f02f-476f-b6e8-10b166f129a5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oERO6fnBhs4K"
      },
      "source": [
        "Resulting in a value of -0.143476.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8jxe0iMhykq"
      },
      "source": [
        "Next, we try the KNN imputer, where the 2 nearest neighbors are considered and the neighbors are weighted uniformly:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlyeMScIh0Ja",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9a0a51ad-3703-43f1-dcec-2521ce021c4b"
      },
      "source": [
        "imputer_KNN = KNNImputer(n_neighbors=2, weights=\"uniform\")\n",
        "pd.DataFrame(imputer_KNN.fit_transform(X))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0         1         2         3\n",
              "0  0.070111 -0.727615  1.904188 -1.966186\n",
              "1  0.410745  0.155161  0.997105 -2.791157\n",
              "2 -0.337408 -1.069994  0.090022  0.430782\n",
              "3 -0.334743 -0.528369 -0.989942  1.480690\n",
              "4  0.336622  1.802785 -1.578173  1.022805"
            ],
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              "        const element = document.querySelector('#df-22826b0a-a044-42db-ad7e-fad0daeebdf8');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d83c6bb8-a34d-4042-b588-cf64545a251b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbYlee8Ch8S5"
      },
      "source": [
        "Resulting in a value of 0.997105 (= 0.5*(1.904188+0.090022)).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **importing missing value with pipline**"
      ],
      "metadata": {
        "id": "FZqbMy0NxI5r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "#from sklearn.preprocessing import Imputer\n",
        "from sklearn.impute import SimpleImputer\n",
        "mp = SimpleImputer(missing_values='NaN', strategy='mean')\n",
        "logreg = LogisticRegression()\n",
        "steps = [('imputation', mp),('logistic_regression', logreg)]\n",
        "pipeline = Pipeline(steps)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
      ],
      "metadata": {
        "id": "Sl2-SSJSvZ12"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline.fit(X_train, y_train)\n",
        "y_pred = pipeline.predict(X_test)\n",
        "pipeline.score(X_test, y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "by36F0ibw1Yx",
        "outputId": "6cb04fa0-3579-445d-a587-e66a5d551e03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-73-5c3ec2a22e16>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    399\u001b[0m         \"\"\"\n\u001b[1;32m    400\u001b[0m         \u001b[0mfit_params_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_fit_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m         \u001b[0mXt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    402\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Pipeline\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_final_estimator\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"passthrough\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, **fit_params_steps)\u001b[0m\n\u001b[1;32m    357\u001b[0m                 \u001b[0mcloned_transformer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m             \u001b[0;31m# Fit or load from cache the current transformer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m             X, fitted_transformer = fit_transform_one_cached(\n\u001b[0m\u001b[1;32m    360\u001b[0m                 \u001b[0mcloned_transformer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/memory.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 353\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_and_shelve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    891\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_print_elapsed_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_clsname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    892\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtransformer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"fit_transform\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 893\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    894\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_set_output.py\u001b[0m in \u001b[0;36mwrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mdata_to_wrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_to_wrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0;31m# only wrap the first output for cross decomposition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    879\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    880\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 881\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    882\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/impute/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    388\u001b[0m             )\n\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_fit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m         \u001b[0;31m# default fill_value is 0 for numerical input and \"missing_value\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/impute/_base.py\u001b[0m in \u001b[0;36m_validate_input\u001b[0;34m(self, X, in_fit)\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 350\u001b[0;31m         \u001b[0m_check_inputs_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissing_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    351\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"i\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"u\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"f\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"O\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    352\u001b[0m             raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/impute/_base.py\u001b[0m in \u001b[0;36m_check_inputs_dtype\u001b[0;34m(X, missing_values)\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"f\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"i\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"u\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     31\u001b[0m             \u001b[0;34m\"'X' and 'missing_values' types are expected to be\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0;34m\" both numerical. Got X.dtype={} and \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: 'X' and 'missing_values' types are expected to be both numerical. Got X.dtype=float64 and  type(missing_values)=<class 'str'>."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TFbHCw8y0gp0"
      },
      "source": [
        "# **Inconsistent data/Irrelevant features**\n",
        "\n",
        "Inconsistent data refers to things like spelling errors in your data, column names that are not relevant to the data, the wrong data type, etc.\n",
        "Here are a couple examples for dealing with these issues."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slY5-kmu008V"
      },
      "source": [
        "**Remove rows based on regex**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JTjb9zNg03PD"
      },
      "source": [
        "Let’s say you want to remove rows that contain a certain word. For my example, I chose the word noisy/Noisy as my target, and I used the function str.contains() to find the indexes that contain those rows."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fh01cN2Q1hHe"
      },
      "source": [
        "Then, using the drop function, and setting axis to index, I can supply the indexes I have and drop those rows.\n",
        "Printing out the number of rows, you can see it reduced by three."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPUdWVLA1n9l"
      },
      "source": [
        "# example: remove rows that contain the target word\n",
        "target = '[Nn]oisy'\n",
        "\n",
        "noisy_airbnb = airbnb[airbnb['name'].str.contains(target, regex=True)]\n",
        "\n",
        "# show rows that contains the word noisy\n",
        "print(noisy_airbnb['name'])\n",
        "\n",
        "# get the index that contains the word noisy\n",
        "index_to_drop = noisy_airbnb['name'].index\n",
        "\n",
        "# print(index_to_drop)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaSmQkCq1tDk"
      },
      "source": [
        "# drop rows based on index\n",
        "airbnb.drop(index_to_drop, axis='index', inplace=True)\n",
        "\n",
        "print(len(airbnb_ori))\n",
        "print(len(airbnb))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgJXAq3H15Y6"
      },
      "source": [
        "**Spelling errors in categorical data**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4HwFy_K18iq"
      },
      "source": [
        "Sometimes your categorical data might have spelling errors or different capitalization that can mess up your categorization.\n",
        "\n",
        "I will be using the neighbourhood_group column as an example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuOFkxYd2BrV"
      },
      "source": [
        "airbnb['neighbourhood_group'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SksmVjW2cCK"
      },
      "source": [
        "You can see the different types of neighborhoods are already well categorized. But what if it wasn’t?\n",
        "\n",
        "To simulate a scenario where some of the data had capitalization or spelling issues, I sampled 2 rows from the data, and replaced them with the wrong spelling.\n",
        "\n",
        "You can see now how the categorization is messed up. “Manhattan” and “manhatann” refer to the same thing, but they aren’t in the same category because of capitalization. Same goes for “brookln” due to spelling issues.\n",
        "\n",
        "We can fix this by using the replace function in pandas. We first give the values that are wrong, then supply the right ones. Notice the values have to match each other in the list, i.e. “manhatann” → “Manhattan”."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGHMa3Kf2nwr"
      },
      "source": [
        "random_index = airbnb.sample(2, random_state = 10).index\n",
        "\n",
        "# airbnb['neighbourhood_group'].loc[random_index]\n",
        "## we randomly selected Manhattan and Brooklyn\n",
        "\n",
        "wrong_spelling = ['manhatann', 'brookln']\n",
        "\n",
        "# replace them with the wrong spelling\n",
        "airbnb.loc[random_index,'neighbourhood_group'] = wrong_spelling\n",
        "airbnb['neighbourhood_group'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "615qZufj2t4y"
      },
      "source": [
        "airbnb['neighbourhood_group'].replace(['manhatann', 'brookln'],\n",
        "                             ['Manhattan', 'Brooklyn'], inplace=True)\n",
        "airbnb['neighbourhood_group'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1vCjhl4211-"
      },
      "source": [
        "**Renaming columns**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92NhJPml236k"
      },
      "source": [
        "There are cases where you want to rename your columns as well.\n",
        "\n",
        "You can do this by using a dictionary, setting the key as the original column name, and the value as the new column name.\n",
        "\n",
        "Then using the rename function we give our dictionary and voila, the columns names have changed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCbivQOE3DEM"
      },
      "source": [
        "new_names = {'name':'listing_name', 'latitude':'lat', 'longitude':'long'}\n",
        "\n",
        "airbnb.rename(columns=new_names, inplace=True)\n",
        "airbnb.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rg8UEqbN8aSv"
      },
      "source": [
        "The dataset that I am using here does not have columns names, so let’s give the most appropriate names to these columns:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9g4R2ZE8f-h"
      },
      "source": [
        "data.columns = ['user_id', 'product_id','ratings','timestamp']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XY4SMhhE3JzD"
      },
      "source": [
        "**Converting to DateTime**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "38V5TvtC3Rh7"
      },
      "source": [
        "If you have data that should be a datetime object, but are strings, you can use the pd.to_datetime, and pass it the format that represents your data.\n",
        "\n",
        "Just like that, the column has converted into a datatime data type."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vo5kySe53VzP"
      },
      "source": [
        "airbnb_ori['last_review'] = pd.to_datetime(airbnb_ori['last_review'], format='%Y-%m-%d')\n",
        "airbnb_ori['last_review'].dtype.type"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2WhiJKU3dvq"
      },
      "source": [
        "**Duplicates**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaJJWjI_3g3w"
      },
      "source": [
        "There are cases where your rows have duplicate values, this could’ve happened due to some mishaps in your data collection.\n",
        "\n",
        "To find out if you have duplicated values, call duplicated().any() on your data frame, and if it’s true, use the drop_duplicates function\n",
        "\n",
        "You can also specify columns where you want to remove duplicate values like below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2A61D9z53pKj"
      },
      "source": [
        "airbnb.duplicated().any()\n",
        "\n",
        "## if true\n",
        "# airbnb.drop_duplicates()\n",
        "\n",
        "## if you want to drop duplicates at specific column\n",
        "# airbnb.drop('col_name', axis=1, inplace=True).drop_duplicates()\n",
        "False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxGRgpQN3tTC"
      },
      "source": [
        "**Change data type to reduce memory**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2EFB99C3wKj"
      },
      "source": [
        "Changing data type is common if you want to reduce memory usage.\n",
        "\n",
        "To do so, you can use the astype(‘dtype’) function where you specify the dtype you want.\n",
        "\n",
        "In my example, I changed the data type for the host_id column from int64 to int32\n",
        "\n",
        "Observe the memory before changing the data type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fafd0hgO38vE"
      },
      "source": [
        "airbnb.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFv7767y4Hru"
      },
      "source": [
        "airbnb['host_id'] = airbnb['host_id'].astype('int32')\n",
        "airbnb.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p46jyLsb4MZZ"
      },
      "source": [
        "You can see the memory reduced from 6.5+ to 6.3+ MB."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orpFOyirp-Cd"
      },
      "source": [
        "# **📚Section-4- Data Transformation**\n",
        "\n",
        "Data preparation or processing is one of the most important steps when working with real-world data on a machine learning project.Since ML model works on maths and numbers, so it’s necessary we encode these categorical variables into numbers.\n",
        "\n",
        "One of the major pains in such situations is working with categorical data. This is because most of the machine learning algorithms cannot work with categorical data directly. They are needed to be converted to numerical data. One-Hot Encoding of the data is a very good solution to handle categorical data [14]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3yUme6NY6VQ"
      },
      "source": [
        "## **Data Types**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4QgkyTXmeIS"
      },
      "source": [
        "**Machine Learning type of Data**[9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_JZmyZDm2Xp"
      },
      "source": [
        "Data Science is the art and science of solving real-world problems and making data-driven decisions. It primarily deals with all kinds of structured or unstructured data. Data, broadly, can be divided into two types i.e., Numerical and Categorical. Most of the data science models are equipped to work with **numerical data**; however, things get interesting when we have to deal with **Categorical data**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KC4ePMtEZF-r"
      },
      "source": [
        "### **Categorical data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dypN39IInD3w"
      },
      "source": [
        "**What is Categorical data?**[9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfrBwUDJnN9n"
      },
      "source": [
        "Categorical data is a form of data that takes on values within a finite set of discrete classes. It is difficult to count or measure categorical data using numbers and therefore they are divided into categories. An example of categorical data would be Gender of a person. It can only take values between Male, Female, and Others.\n",
        "There are two types of categorical variables:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ByZ8f5q7_IN2"
      },
      "source": [
        "Simply speaking, in categorical data the values are labels instead of numbers.[14]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXor3Iq__Ozl"
      },
      "source": [
        "When you want to categorize ‘salary’ in a data set, then you may label it as ‘low’, medium’, ‘high’ [14]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QJ8pOpcuCzrj"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1VuTzW-kjbhthiFpIERG6A-YmltAI7bn1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jq0RgYj3DBzh"
      },
      "source": [
        "Similarly, if you want to label ‘shape‘ of objects, you could do something like, ’round’, ‘square’, ‘triangular‘."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQSboEknDEIp"
      },
      "source": [
        "**The Problem with Categorical Data**[14]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuoLkpVCDKU5"
      },
      "source": [
        "You may be thinking that the above situation of categorizing values as labels seems fair enough and reasonable. Actually, you are right. But, when you bring the case of machine learning algorithms, then the situation changes.\n",
        "\n",
        "Most of the machine learning algorithms out there cannot handle categorical labels in a data set directly. Whether it may be for classification or regression, the algorithms specifically need numerical data to carry out the predictions.\n",
        "\n",
        "So, we need to convert the categorical labels into numerical labels. In the Machine Learning world, this is often termed as data **transformation**.\n",
        "\n",
        "Now, let us see the different ways in which categorical labels can be handled."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri0l29NYZP6b"
      },
      "source": [
        "### **Ordinal Variables**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fi5xyxuYoMDe"
      },
      "source": [
        "**I. Ordinal Variables:**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8vi5AOgoOsm"
      },
      "source": [
        "These variables maintain a natural order in their class of values. If we consider the level of education then we can easily sort them according to their education tag in the order of High School < Under-Graduate<post-Graduate < PhD. The review rating system can also be considered as an ordinal data type where 5 stars is definitely better than 1 star."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WoKYJjoZZXm3"
      },
      "source": [
        "### **Nominal Variables:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LjoCmUsyoZcE"
      },
      "source": [
        "I**I. Nominal Variables:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QBjqZNclooA5"
      },
      "source": [
        "These variables do not maintain any natural/logical order. The color of a car can be considered as Nominal Variable as we cannot compare the color with each other. It is impossible to state that “Red” is better than “Blue” (subjective!). Similarly, Gender is a type of Nominal Variable as again we cannot differentiate between Male, Female, and Others."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqHC9KLDEBQp"
      },
      "source": [
        "## **3.1- Handling Categorical Labels**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWgF3v2iENOy"
      },
      "source": [
        "We will focus mainly on two methods here.\n",
        "\n",
        "- Label Encoding\n",
        "- One-Hot Encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iGATCWIXou4Z"
      },
      "source": [
        "**Encoding Categorical Data:**[9]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5KT7XjYo4KI"
      },
      "source": [
        "Most of the Machine learning algorithms are designed to work with numeric data. Hence, we need to convert Categorical(text) data into numerical data for model building. There are multiple encoding techniques to convert categorical data into numerical data. Let’s look at some of them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1gF3x2VX8wL"
      },
      "source": [
        "When working on some datasets, we found that some of the features are categorical, if we pass that feature directly to our model, our model can't understand those feature variables. We all know that machines can't understand categorical data. Machines require all independent and dependent variables i.e input and output features to be numeric. This means that if our data contain a categorical variable, we must have to encode it to the numbers before we fit our data to the model.\n",
        "\n",
        "Models only work with numerical values. For this reason, it is necessary to convert the categorical values of the features into numerical ones, So the machine can learn from those data and gives the right model. This process of converting categorical data into numerical data is called **Encoding** [6]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFb6GAEApROW"
      },
      "source": [
        "There are multiple ways of encoding techniques to deal with these variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDOjfOBEYf5N"
      },
      "source": [
        "The two most popular techniques of encoding are,\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T1rZA8G2YiYF"
      },
      "source": [
        "### **Nominal Encoding [7]**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dQuw6Na-ZCU_"
      },
      "source": [
        "When we have a feature where variables are just names and there is no order or rank to this variable's feature.For example: City of person lives in, Gender of person, Marital Status, etc…\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ab2VkiHFZbCd"
      },
      "source": [
        "In the above example, We do not have any order or rank, or sequence. All the variables in the respective feature are equal. We can't give them any orders or ranks. Those features are called Nominal features.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0yExvnid2uo"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1EzEpCh6IEsZ0RKlDb_8c0fX376-M61ny)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AeCGPIcVeAHS"
      },
      "source": [
        "### **Ordinal Encoding [7]**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UY5SNrg8pb1l"
      },
      "source": [
        "Ordinal Encoding primarily encodes ordinal categories into ordered numerical values. Ordinal encoding maps each unique category value to a specific numerical value based on its order or rank. Consider the education column in the given data frame. Here, we define the ordering of the categories when creating an ordinal encoder using sklearn. so, in the example, we arrange the order inside the categories as a list in ascending order. First, we have the High School followed by Associate,Master, and then Ph.D. at the end.[9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPtUBoxTv-Pf"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPJHjLF3eOQo"
      },
      "source": [
        "When we have a feature where variables have some order/rank.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XtvDIhvedAe"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1G1oAtnnuIuAApxdPPU80679T8rtDpntH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYq-hnQAeSgK"
      },
      "source": [
        "For example: Student’s performance, Customer’s review, Education of person, etc…"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLROx2pJebAG"
      },
      "source": [
        "In the above example, we have orders/ranks/sequences. We can assign ranks based on student's performance, based on feedback given by customers, based on the highest education of the person. Those features are called Ordinal features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPoe8uD4p2Qr"
      },
      "source": [
        "[OrdinalEncoder(categories=[[‘HS’, ‘AS’, ‘M.S’, ‘Ph.D.’]])]\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "SvN-n_a03u_R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize list for the dataframe\n",
        "data = [['Adam','male','Ph.D','NY','M', 'PT'],['Dina','female','M.S','NY','D','PT'],\n",
        "        ['John','male','AS','MI','S','FT'],['Elton','male','HS','AL','M', 'Unemployed'],\n",
        "        ['Gina','female','HS','AK','D', 'Intern'],['Sara', 'female','M.S','AZ','S','FT'],\n",
        "        ['Lana','female','AS', 'CA','M','Intern'],['Tim','male','Ph.D','AR','D', 'PT'],\n",
        "        ['Suzan','female','M.S','AZ','S','FT'], ['Aj','male','AS','CA','M','FT'],\n",
        "        ['Luke','male','HS','CA','D', 'Unemployed'],['Salma','female','HS','CA','S', 'Intern'],\n",
        "        ['Abby', 'female','M.S','CA','M','PT'], ['Sandra','female','HS','CO','D', 'PT'],\n",
        "        ['Zen','male','Ph.D','CO','S', 'PT'],['Emma','female','M.S','DC','M','FT'],\n",
        "        ['Owen','male','AS','DE','D','Unemployed'],['Levi','male','HS','CT','S','Unemployed'],\n",
        "        ['Eva','female','HS','DC','M', 'PT'],['Sophia', 'female','M.S','DC','D','PT'],\n",
        "        ['Ella','female','HS','FL','S', 'Unemployed'],['Jacob','male','Ph.D','IL','M', 'FT'],\n",
        "        ['Mia','female','M.S','FL','D','PT'], ['Ethan','male','AS','FL','S','PT'],\n",
        "        ['Luke','male','HS','IL','M', 'Intern'],['Nadia','female','HS','IN','D', 'Intern'],\n",
        "        ['Tulip', 'female','M.S','IA','S','PT'],['Linda','female','HS','KS','M', 'PT'],\n",
        "        ['Jack','male','Ph.D','KI','D', 'PT'],['Kindra','female','M.S','LA','S','FT'],\n",
        "        ['Manson','male','AS','ME','M','Unemployed'],['Logan','male','HS','MD','D', 'Unemployed'],\n",
        "        ['Luna','female','HS','MA','S', 'Unemployed'],['Layla', 'female','M.S','MN','M','Unemployed'],\n",
        "        ['Lynn','female','HS','MS','D', 'PT'],['Nora','male','Ph.D','MS','S', 'FT'],\n",
        "        ['Suzy','female','M.S','MO','M','PT'], ['Lucas','male','AS','MT','D','PT'],\n",
        "        ['Chad','male','HS','TX','S', 'Unemployed'], ['Soleen','female','HS','TX','M','Unemployed'],\n",
        "        ['Zoey', 'female','M.S','NY','D','FT'], ['Ellie','female','HS','TX','S', 'Unemployed'],\n",
        "        ['Oliver','male','Ph.D','TX','M','PT'],['Tamara','female','M.S','NJ','D','PT'],\n",
        "        ['Noah','male','AS','NJ','S','FT'], ['Liam','male','HS','MI','M', 'Unemployed'],\n",
        "        ['Stella','female','HS','NM','D', 'Unemployed'],['Audrey', 'female','M.S','NC','S','PT'],\n",
        "        ['Lucy','female','HS','ND','M', 'PT'],['Timmy','male','Ph.D','OH','D', 'PT'],\n",
        "        ['Bella','female','M.S','OH','S','PT'], ['William','male','AS','OK','M','FT'],\n",
        "        ['Brian','male','HS','OK','D', 'Unemployed'],['Nova','female','HS','OR','S', 'Unemployed'],\n",
        "        ['AnnaAbby', 'female','M.S','MI','M','FT'], ['Elena','female','HS','NY','D', 'Intern']]"
      ],
      "metadata": {
        "id": "zuKwjsX130xA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create dataframe\n",
        "df = pd.DataFrame(data, columns = ['Name', 'Gender','Education','State','Marital Status','Employment Status'])"
      ],
      "metadata": {
        "id": "PT-Rx7Aj36sg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxdov92MqB4Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54f48f1d-7945-4ae0-a291-d4ab2e5d425a"
      },
      "source": [
        "df['Education'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ph.D', 'M.S', 'AS', 'HS'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j1KsFmt84Avg",
        "outputId": "a98f2d88-5c1b-4e9d-e0b2-ebd80cf38c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(56, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fE9NCOoV4Edg",
        "outputId": "b40ed255-2bd1-4555-9a67-91d76f5df468"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Name  Gender Education State Marital Status Employment Status\n",
              "0   Adam    male      Ph.D    NY              M                PT\n",
              "1   Dina  female       M.S    NY              D                PT\n",
              "2   John    male        AS    MI              S                FT\n",
              "3  Elton    male        HS    AL              M        Unemployed\n",
              "4   Gina  female        HS    AK              D            Intern"
            ],
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d12b1f6d-9084-460f-a52a-b2807d4b7cb6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d12b1f6d-9084-460f-a52a-b2807d4b7cb6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-47eac377-786e-4551-9e19-2eacbf9b05d2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-47eac377-786e-4551-9e19-2eacbf9b05d2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47eac377-786e-4551-9e19-2eacbf9b05d2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEdStYMbqL-5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2ae04826-dfc4-4a17-a006-16fee8d24ded"
      },
      "source": [
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "ordinal = OrdinalEncoder(categories=[['HS', 'AS', 'M.S','Ph.D']])\n",
        "df['Education'] = ordinal.fit_transform(df[['Education']])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Name  Gender  Education State Marital Status Employment Status\n",
              "0   Adam    male        3.0    NY              M                PT\n",
              "1   Dina  female        2.0    NY              D                PT\n",
              "2   John    male        1.0    MI              S                FT\n",
              "3  Elton    male        0.0    AL              M        Unemployed\n",
              "4   Gina  female        0.0    AK              D            Intern"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7484bd3-3ff9-40d1-b42a-a29d9b88c787\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>State</th>\n",
              "      <th>Marital Status</th>\n",
              "      <th>Employment Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adam</td>\n",
              "      <td>male</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NY</td>\n",
              "      <td>M</td>\n",
              "      <td>PT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dina</td>\n",
              "      <td>female</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NY</td>\n",
              "      <td>D</td>\n",
              "      <td>PT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>John</td>\n",
              "      <td>male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>MI</td>\n",
              "      <td>S</td>\n",
              "      <td>FT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Elton</td>\n",
              "      <td>male</td>\n",
              "      <td>0.0</td>\n",
              "      <td>AL</td>\n",
              "      <td>M</td>\n",
              "      <td>Unemployed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gina</td>\n",
              "      <td>female</td>\n",
              "      <td>0.0</td>\n",
              "      <td>AK</td>\n",
              "      <td>D</td>\n",
              "      <td>Intern</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7484bd3-3ff9-40d1-b42a-a29d9b88c787')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a7484bd3-3ff9-40d1-b42a-a29d9b88c787 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a7484bd3-3ff9-40d1-b42a-a29d9b88c787');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f3a537b2-cd82-4b38-a9c3-6d729f074b09\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3a537b2-cd82-4b38-a9c3-6d729f074b09')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3a537b2-cd82-4b38-a9c3-6d729f074b09 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYrfQTE5qQsq"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1jnxgK8GKTX1HyWXyOLDgqmYQqns98njI)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8boEcj6q8v5"
      },
      "source": [
        "The ordinal encoder is the most suitable option for encoding ordinal variables. It helps the machine learning model to establish a relationship between a categorical column and the target column. For example, if we want to predict the salary of an employee, it would depend on different features, and education level would be one of those features. Now, logically the one with Ph.D. will have a better salary than the one with a high school degree. so, the model will learn that a Ph.D. with a value of 3 in the data frame weighs more than the one with a high school degree with a value of 0. This way the model will learn that when the level of education goes up, the salary increases and vice versa.[9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dIoU6rrjrLaY"
      },
      "source": [
        "### **One Hot Encoding**:[9]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOtCZyEwwA52"
      },
      "source": [
        "If there is no ordinal relationship between the categorical variables then ordinal encoding might mislead the model. This is because the ordinal encoder will try to force an ordinal relationship on the variables to assume a natural ordering, thus resulting in poor performance.\n",
        "In this case, One Hot encoder should be used to treat our categorical variables. It will create dummy variables by converting N categories into N features/columns. Considering the gender column again. If we have a male in the first row, then its value is 1. Also if we have a female in the second row then its value is 0. Whenever the category exists its value is 1 and 0 where it does not. We can one-hot encode categorical variables in two ways. One, by using get_dummies in pandas and two, by using OneHotEncoder from sklearn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JEgDnRBkgjfG"
      },
      "source": [
        "In one-hot encoding, the numerical variables are replaced by binary variables.\n",
        "\n",
        "So, each of the category is either 0 or 1. Again, take the shape category example into account. If the shape is, say, ‘triangle‘, then it is labeled as 1 and all other shapes are labeled as zero.\n",
        "\n",
        "One-hot encoding is particularly used in those cases where there is no ordinal relationship between the labels.\n",
        "\n",
        "The following image may clear some things up[14]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1- get_dummies**"
      ],
      "metadata": {
        "id": "hX_0UEKU6Xzx"
      }
    },
    {
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          "height": 206
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        "outputId": "80bbe0bb-428f-4ffe-ad7e-83acfda91b31"
      },
      "source": [
        "pd.get_dummies(df['Gender']).head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   female  male\n",
              "0       0     1\n",
              "1       1     0\n",
              "2       0     1\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "452Af_mKwIf1"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1HuX3C28bC8pwODt9xiPXWzTxsN5ICxhJ)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5r7cKZrvwxwS"
      },
      "source": [
        "Another example is Marital Status. Here, we have three different categories Married: M, Divorced: D, and Single: S. we can reduce the dimensionality by one column by using: “drop_first=True” meaning the number of columns will be one less than the number of categories."
      ]
    },
    {
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      },
      "source": [
        "pd.get_dummies(df['Marital Status'],drop_first=True).head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   M  S\n",
              "0  1  0\n",
              "1  0  0\n",
              "2  0  1\n",
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              "4  0  0"
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mD9NJVmWxXk6"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1kbocIUE7gHMIcbnZtC7CluQMUqdty5G4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MUXlTIzxgjo"
      },
      "source": [
        "In the second row of the table above, we have zero for married and single, which effectively means that it is Divorced."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_b_Qho_xiT5"
      },
      "source": [
        "If we assign drop_first =False, then we still have three columns: Married, Single, and Divorced."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMhpy3o5xmWB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4f8b035a-2106-499e-ed2a-b56f3f4691c4"
      },
      "source": [
        "pd.get_dummies(df['Marital Status'],drop_first=False).head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   D  M  S\n",
              "0  0  1  0\n",
              "1  1  0  0\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utFZYVsfyFYS"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1j654yAZy1pZiMX7uyjmzXXPWlnkag-Vz)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2- OneHotEncoder**"
      ],
      "metadata": {
        "id": "7UTVOlkM6ba5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# one hot encode the breast cancer dataset\n",
        "from pandas import read_csv\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "# define the location of the dataset\n",
        "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/breast-cancer.csv\"\n",
        "# load the dataset\n",
        "dataset = read_csv(url, header=None)\n",
        "# retrieve the array of data\n",
        "data = dataset.values\n",
        "# separate into input and output columns\n",
        "X = data[:, :-1].astype(str)\n",
        "y = data[:, -1].astype(str)\n",
        "# summarize the raw data\n",
        "print(X[:3, :])\n",
        "# define the one hot encoding transform\n",
        "encoder = OneHotEncoder(sparse=False)\n",
        "# fit and apply the transform to the input data\n",
        "X_oe = encoder.fit_transform(X)\n",
        "# summarize the transformed data\n",
        "print(X_oe[:3, :])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JMsMtue06in5",
        "outputId": "509e666b-df56-4db3-e9a4-fe85c81b1722"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[\"'40-49'\" \"'premeno'\" \"'15-19'\" \"'0-2'\" \"'yes'\" \"'3'\" \"'right'\"\n",
            "  \"'left_up'\" \"'no'\"]\n",
            " [\"'50-59'\" \"'ge40'\" \"'15-19'\" \"'0-2'\" \"'no'\" \"'1'\" \"'right'\" \"'central'\"\n",
            "  \"'no'\"]\n",
            " [\"'50-59'\" \"'ge40'\" \"'35-39'\" \"'0-2'\" \"'no'\" \"'2'\" \"'left'\" \"'left_low'\"\n",
            "  \"'no'\"]]\n",
            "[[0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
            "  0. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
            "  0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0.\n",
            "  0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 1. 0.]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o8LqoGmZyPuP"
      },
      "source": [
        "As mentioned, we can also implement one-hot encoding through OneHotEncoder from sklearn."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df0 = df.copy()"
      ],
      "metadata": {
        "id": "QSsuEGDx7iWJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LlVVEu7ySC3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aed05a3b-bc26-491e-dcf4-316090a306a9"
      },
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ohe = OneHotEncoder(sparse=False)\n",
        "ohe.fit_transform(df0[['Marital Status']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        },
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          "output_type": "execute_result",
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              "       [1., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amsik25XyVyH"
      },
      "source": [
        "If we have a high number of categorical variables in a column, then we should avoid using one-hot encoding. It will result in an increase in the number of corresponding columns which will give rise to a problem called “Curse of Dimensionality”."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xarFJmqzqNdt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f5721ac-d0ab-4844-c00d-be5c6d38c26a"
      },
      "source": [
        "oversampled.select_dtypes(include=['object']).columns\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['MRNO', 'AGE', 'GENDER', 'DISTRICT', 'TEHSIL', 'REPORT_VERIFIED',\n",
              "       'RESULT_VALUE'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkfkPLXEqSSi"
      },
      "source": [
        "# Impute categorical var with Mode\n",
        "oversampled['MRNO'] = oversampled['MRNO'].fillna(oversampled['MRNO'].mode()[0])\n",
        "oversampled['AGE'] = oversampled['AGE'].fillna(oversampled['AGE'].mode()[0])\n",
        "oversampled['GENDER'] = oversampled['GENDER'].fillna(oversampled['GENDER'].mode()[0])\n",
        "oversampled['DISTRICT'] = oversampled['DISTRICT'].fillna(oversampled['DISTRICT'].mode()[0])\n",
        "oversampled['TEHSIL'] = oversampled['TEHSIL'].fillna(oversampled['TEHSIL'].mode()[0])\n",
        "oversampled['REPORT_VERIFIED'] = oversampled['REPORT_VERIFIED'].fillna(oversampled['REPORT_VERIFIED'].mode()[0])\n",
        "oversampled['RESULT_VALUE'] = oversampled['RESULT_VALUE'].fillna(oversampled['RESULT_VALUE'].mode()[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrGan4YLqbCI"
      },
      "source": [
        "# Convert categorical features to continuous features with Label Encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "lencoders = {}\n",
        "for col in oversampled.select_dtypes(include=['object']).columns:\n",
        "    lencoders[col] = LabelEncoder()\n",
        "    oversampled[col] = lencoders[col].fit_transform(oversampled[col])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPPO6refqh0o"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "# Multiple Imputation by Chained Equations\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "MiceImputed = oversampled.copy(deep=True)\n",
        "mice_imputer = IterativeImputer()\n",
        "MiceImputed.iloc[:, :] = mice_imputer.fit_transform(oversampled)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nCdkFRKsGV6"
      },
      "source": [
        "### **Label_Encoding**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRl1fZL-_EUB"
      },
      "source": [
        "The label encoder will convert each category into a unique numerical value. If implemented with Sklearn, then this encoder should be used to encode output values, i.e. y, and not the input X. It is similar to the ordinal encoder except, here the numeric values are assigned automatically without following any sort of natural order. Generally, the alphabetical order of the categorical values is used to determine which numerical value comes first. Considering our target variable “Job Status” column has four different categories. After applying label encoding to this column the four different categories are mapped into integers 0: Full Time, 1: Intern, 2: Part-Time, and 3:Unemployed. With this, it can be interpreted that Unemployed have a higher priority than Part-Time, Full Time, and Intern while training the model, whereas, there is no such priority or relation between these statuses. We can’t define the order of labels with the label encoding technique."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Y1miQzned9F"
      },
      "source": [
        "Label encoding is really simple thing. For each of the categorical label you assign an integer to it.\n",
        "\n",
        "If we again consider the salary example, the you will be able to encode low as 1, medium as 2 and high as 3 [14]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAj1Gf8UeqHz"
      },
      "source": [
        "This process is okay until the number of labels is considerably small. When the number of labels increases, this solution may not work very well.\n",
        "\n",
        "This brings us to the second technique, One-Hot Encoding.[14]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cy_rCAjPes5j"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1qVI_RpzUKwEnivhe418XZykb5t06pise\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRS-UR11_JJw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "916df1d6-af51-45af-86b1-116dcb30fa28"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "lbe = LabelEncoder()\n",
        "df['Employment Status']= lbe.fit_transform(df['Employment Status'])\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Name  Gender  Education State Marital Status  Employment Status\n",
              "0   Adam    male        3.0    NY              M                  2\n",
              "1   Dina  female        2.0    NY              D                  2\n",
              "2   John    male        1.0    MI              S                  0\n",
              "3  Elton    male        0.0    AL              M                  3\n",
              "4   Gina  female        0.0    AK              D                  1"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6Y9_6n8A0MU"
      },
      "source": [
        "\n",
        "[](https://drive.google.com/uc?export=view&id=1j654yAZy1pZiMX7uyjmzXXPWlnkag-Vz)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKP9_lp3_RRf"
      },
      "source": [
        "The disadvantage to label encoding is that it gives an order to the categorical value, which might not be suitable to some machine learning algorithms such as Linear Regression, as they are too sensitive to the values; in such case, one hot encoding provides better results.\n",
        "On the other hand, label encoding is suitable with Decision Tree and Random Forest algorithms because they don’t depend on the values of the categorical variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhYgelLK9G8Z"
      },
      "source": [
        "Import Dataset-\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuqmKLVv9NAx"
      },
      "source": [
        "import seaborn as sns\n",
        "df=sns.load_dataset('titanic')\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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        "id": "NSXNdKhz9Uwn"
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      "source": [
        "So, I am considering categorical features and will try to find out the top important features. Creating a data frame for categorical features. We need to compare all the categories with the output category (Survived)."
      ]
    },
    {
      "cell_type": "code",
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        "id": "Hdsfcixq9aSf",
        "outputId": "4cf5833d-0bea-4366-dd0a-0ef1497ba65a"
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      "source": [
        "##['sex','embarked','alone','pclass','Survived']\n",
        "df=df[['sex','embarked','alone','pclass','survived']]\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      sex embarked  alone  pclass  survived\n",
              "0    male        S  False       3         0\n",
              "1  female        C  False       1         1\n",
              "2  female        S   True       3         1\n",
              "3  female        S  False       1         1\n",
              "4    male        S   True       3         0"
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVtVy1br9gxJ"
      },
      "source": [
        "Let’s perform label encoding on the embarked\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6iF7vDNz9mtY"
      },
      "source": [
        "df['sex']=np.where(df['sex']==\"male\",1,0)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "oversampled.select_dtypes(include=['object']).columns\n",
        "\n",
        "# Impute categorical var with Mode\n",
        "oversampled['MRNO'] = oversampled['MRNO'].fillna(oversampled['MRNO'].mode()[0])\n",
        "oversampled['AGE'] = oversampled['AGE'].fillna(oversampled['AGE'].mode()[0])\n",
        "oversampled['GENDER'] = oversampled['GENDER'].fillna(oversampled['GENDER'].mode()[0])\n",
        "oversampled['DISTRICT'] = oversampled['DISTRICT'].fillna(oversampled['DISTRICT'].mode()[0])\n",
        "oversampled['TEHSIL'] = oversampled['TEHSIL'].fillna(oversampled['TEHSIL'].mode()[0])\n",
        "oversampled['REPORT_VERIFIED'] = oversampled['REPORT_VERIFIED'].fillna(oversampled['REPORT_VERIFIED'].mode()[0])\n",
        "oversampled['RESULT_VALUE'] = oversampled['RESULT_VALUE'].fillna(oversampled['RESULT_VALUE'].mode()[0])"
      ],
      "metadata": {
        "id": "qREDdqdR8R9z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical features to continuous features with Label Encoding\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "lencoders = {}\n",
        "for col in oversampled.select_dtypes(include=['object']).columns:\n",
        " lencoders[col] = LabelEncoder()\n",
        " oversampled[col] = lencoders[col].fit_transform(oversampled[col])"
      ],
      "metadata": {
        "id": "_JhHJQOs8XGZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "# Multiple Imputation by Chained Equations\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "MiceImputed = oversampled.copy(deep=True)\n",
        "mice_imputer = IterativeImputer()\n",
        "MiceImputed.iloc[:, :] = mice_imputer.fit_transform(oversampled)"
      ],
      "metadata": {
        "id": "znBSQFEL8bzC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mJjiGyo29zBz"
      },
      "source": [
        "###**Let’s perform label encoding on the embarked**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMFVQ6-B9wUY"
      },
      "source": [
        "import numpy as np\n",
        "### let's perform label encoding on embarked\n",
        "ordinal_label = {k: i for i, k in enumerate(df['embarked'].unique(), 0)}\n",
        "df['embarked'] = df['embarked'].map(ordinal_label)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "e4470d7a-8fd8-4087-dae0-bdeb66e3db38"
      },
      "source": [
        "df.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sex  embarked  alone  pclass  survived\n",
              "0    1         0  False       3         0\n",
              "1    0         1  False       1         1\n",
              "2    0         0   True       3         1\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f7df7c7d-6283-458f-a493-6de7327de2df button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f7df7c7d-6283-458f-a493-6de7327de2df');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8dd38552-bdd2-4d2b-a1d9-03ee23a58089')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8dd38552-bdd2-4d2b-a1d9-03ee23a58089 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOXQ48L5-GWW"
      },
      "source": [
        "#Performing label encoding on alone\n",
        "## let's perform label encoding on alone\n",
        "df['alone']=np.where(df['alone']==True,1,0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dj0pu0gh-URc"
      },
      "source": [
        "df.head()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I9lbjuhM_a4N"
      },
      "source": [
        "# **📚Section-5-Feature Scaling**\n",
        "\n",
        "After performing the feature engineering part, we need to do a feature scaling. The goal of feature scaling is to bring down all the data on the same scale, as each feature may vary in a different range."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CoY7XAQuAMXH"
      },
      "source": [
        "One of the primary objectives of normalization is to bring the data close to zero. That makes the optimization problem more “numerically stable”.[12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLgMXIiIAXh1"
      },
      "source": [
        "Now, the scaling using mean and standard deviation assumes that the data is normally distributed, that is, most of the data is sufficiently close to the mean. So shifting the mean to zero ensures that most components of most data points are close to 0. Specifically, 68% of data would be between -1 and 1, as can be seen from the following figure:[12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GE5vzrgbDqlu"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1z3xcwNThquBBCjH_0vCoZwwIG3I0DOcN)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXgzbHvjJqak"
      },
      "source": [
        "In this post we explore 3 methods of feature scaling that are implemented in scikit-learn:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_395pF3JyEj"
      },
      "source": [
        "- StandardScaler\n",
        "- MinMaxScaler\n",
        "- RobustScaler\n",
        "- Normalizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_OqSFSfpJ3Xw"
      },
      "source": [
        "## **StandardScaler** [12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOe0Rz7IKOKg"
      },
      "source": [
        "The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centered around 0, with a standard deviation of 1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1UIoACeQKTIH"
      },
      "source": [
        "The mean and standard deviation are calculated for the feature and then the feature is scaled based on:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iUVURlPQKcxP"
      },
      "source": [
        "If data is not normally distributed, this is not the best scaler to use.\n",
        "Let’s take a look at it in action:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcYgprnvKejY"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1p6CMCRRpK9YY96DR4z1zlJGr8SQdGOv2\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OhYvBC3p_orq"
      },
      "source": [
        "# Standardizing data\n",
        "from sklearn import preprocessing\n",
        "r_scaler = preprocessing.MinMaxScaler()\n",
        "r_scaler.fit(MiceImputed)\n",
        "MiceImputed = pd.DataFrame(r_scaler.transform(MiceImputed), index=MiceImputed.index, columns=MiceImputed.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eG8wv8wAW4d"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "matplotlib.style.use('ggplot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XKA4K1NQK_Zd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "0865eb41-ca57-4b0c-9852-9102dfd9c898"
      },
      "source": [
        "np.random.seed(1)\n",
        "df = pd.DataFrame({\n",
        "    'x1': np.random.normal(0, 2, 10000),\n",
        "    'x2': np.random.normal(5, 3, 10000),\n",
        "    'x3': np.random.normal(-5, 5, 10000)\n",
        "})\n",
        "scaler = preprocessing.StandardScaler()\n",
        "scaled_df = scaler.fit_transform(df)\n",
        "scaled_df = pd.DataFrame(scaled_df, columns=['x1', 'x2', 'x3'])\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 5))\n",
        "ax1.set_title('Before Scaling')\n",
        "sns.kdeplot(df['x1'], ax=ax1)\n",
        "sns.kdeplot(df['x2'], ax=ax1)\n",
        "sns.kdeplot(df['x3'], ax=ax1)\n",
        "ax2.set_title('After Standard Scaler')\n",
        "sns.kdeplot(scaled_df['x1'], ax=ax2)\n",
        "sns.kdeplot(scaled_df['x2'], ax=ax2)\n",
        "sns.kdeplot(scaled_df['x3'], ax=ax2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import scale\n",
        "X_scaled = scale(X)\n",
        "np.mean(X), np.std(X)\n",
        "np.mean(X_scaled), np.std(X_scaled)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnQSWG9ISLPo",
        "outputId": "1ff01894-3c98-4df6-f4f6-110ed3b35d5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-1.2008172234345692e-16, 1.0000000000000002)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G6ESPQaRLSLd"
      },
      "source": [
        "## **Min-Max Scaler** [12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rx4xTnbgLZmD"
      },
      "source": [
        "The MinMaxScaler is probably the most famous scaling algorithm, and follows the following formula for each feature:\n",
        "\n",
        "It essentially shrinks the range such that the range is now between 0 and 1 (or -1 to 1 if there are negative values).\n",
        "This scaler works better for cases in which the standard scaler might not work so well. If the distribution is not Gaussian or the standard deviation is very small, the min-max scaler works better.\n",
        "However, it is sensitive to outliers, so if there are outliers in the data, you might want to consider the Robust Scaler below.\n",
        "For now, let’s see the min-max scaler in action"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# example of normalizing input data\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "# define dataset\n",
        "X, y = make_classification(n_samples=1000, n_features=5, n_informative=5, n_redundant=0, random_state=1)\n",
        "# summarize data before the transform\n",
        "print(X[:3, :])\n",
        "# define the scaler\n",
        "trans = MinMaxScaler()\n",
        "# transform the data\n",
        "X_norm = trans.fit_transform(X)\n",
        "# summarize data after the transform\n",
        "print(X_norm[:3, :])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYP_IOr4Q5d1",
        "outputId": "3420d5ae-dfb6-4a6b-bca7-19eef30cf9a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 2.39324489 -5.77732048 -0.59062319 -2.08095322  1.04707034]\n",
            " [-0.45820294  1.94683482 -2.46471441  2.36590955 -0.73666725]\n",
            " [ 2.35162422 -1.00061698 -0.5946091   1.12531096 -0.65267587]]\n",
            "[[0.77608466 0.0239289  0.48251588 0.18352101 0.59830036]\n",
            " [0.40400165 0.79590304 0.27369632 0.6331332  0.42104156]\n",
            " [0.77065362 0.50132629 0.48207176 0.5076991  0.4293882 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZR0uDgQg4Tb"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1WCBHBiai34AgFic_Rs6iA2ILixqU1gQq)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqHc5mcThG_Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "ee439acf-1b32-4a80-b63a-06dd80fe7f8a"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "df = pd.DataFrame({\n",
        "    # positive skew\n",
        "    'x1': np.random.chisquare(8, 1000),\n",
        "    # negative skew\n",
        "    'x2': np.random.beta(8, 2, 1000) * 40,\n",
        "    # no skew\n",
        "    'x3': np.random.normal(50, 3, 1000)\n",
        "})\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "scaled_df = scaler.fit_transform(df)\n",
        "scaled_df = pd.DataFrame(scaled_df, columns=['x1', 'x2', 'x3'])\n",
        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(6, 5))\n",
        "ax1.set_title('Before Scaling')\n",
        "sns.kdeplot(df['x1'], ax=ax1)\n",
        "sns.kdeplot(df['x2'], ax=ax1)\n",
        "sns.kdeplot(df['x3'], ax=ax1)\n",
        "ax2.set_title('After Min-Max Scaling')\n",
        "sns.kdeplot(scaled_df['x1'], ax=ax2)\n",
        "sns.kdeplot(scaled_df['x2'], ax=ax2)\n",
        "sns.kdeplot(scaled_df['x3'], ax=ax2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardizing data\n",
        "from sklearn import preprocessing\n",
        "r_scaler = preprocessing.MinMaxScaler()\n",
        "r_scaler.fit(MiceImputed)\n",
        "MiceImputed = pd.DataFrame(r_scaler.transform(MiceImputed), index=MiceImputed.index, columns=MiceImputed.columns)"
      ],
      "metadata": {
        "id": "YeejXrY9SiC5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qgK__UuThdDt"
      },
      "source": [
        "Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07FEKVPyhmhE"
      },
      "source": [
        "## **Robust Scaler**[12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOhq2BOphubS"
      },
      "source": [
        "The RobustScaler uses a similar method to the Min-Max scaler but it instead uses the interquartile range, rather than the min-max, so that it is robust to outliers. Therefore it follows the formula:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvBvkb_TtWjm"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1lr_IkeqAV-MsYO--MmfAMTj9OfTr6VcG)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z_y5F-S2hzMc"
      },
      "source": [
        "For each feature.\n",
        "Of course, this means it is using less of the data for scaling so it’s more suitable for when there are outliers in the data.\n",
        "Let’s take a look at this one in action on some data with outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZLP4JT2h00b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "29301f8e-fef2-4bdf-e8c0-a48d0db38611"
      },
      "source": [
        "x = pd.DataFrame({\n",
        "    # Distribution with lower outliers\n",
        "    'x1': np.concatenate([np.random.normal(20, 1, 1000), np.random.normal(1, 1, 25)]),\n",
        "    # Distribution with higher outliers\n",
        "    'x2': np.concatenate([np.random.normal(30, 1, 1000), np.random.normal(50, 1, 25)]),\n",
        "})\n",
        "scaler = preprocessing.RobustScaler()\n",
        "robust_scaled_df = scaler.fit_transform(x)\n",
        "robust_scaled_df = pd.DataFrame(robust_scaled_df, columns=['x1', 'x2'])\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "minmax_scaled_df = scaler.fit_transform(x)\n",
        "minmax_scaled_df = pd.DataFrame(minmax_scaled_df, columns=['x1', 'x2'])\n",
        "fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(9, 5))\n",
        "ax1.set_title('Before Scaling')\n",
        "sns.kdeplot(x['x1'], ax=ax1)\n",
        "sns.kdeplot(x['x2'], ax=ax1)\n",
        "ax2.set_title('After Robust Scaling')\n",
        "sns.kdeplot(robust_scaled_df['x1'], ax=ax2)\n",
        "sns.kdeplot(robust_scaled_df['x2'], ax=ax2)\n",
        "ax3.set_title('After Min-Max Scaling')\n",
        "sns.kdeplot(minmax_scaled_df['x1'], ax=ax3)\n",
        "sns.kdeplot(minmax_scaled_df['x2'], ax=ax3)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Am45-QUFh777"
      },
      "source": [
        "Notice that after Robust scaling, the distributions are brought into the same scale and overlap, but the outliers remain outside of the bulk of the new distributions.\n",
        "However, in Min-Max scaling, the two normal distributions are kept separate by the outliers that are inside the 0–1 range."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "data = pd.read_csv(\"your_data.csv\")\n",
        "X = data[[\"feature1\", \"feature2\", ...]]  # Select numerical features\n",
        "y = data[\"target\"]  # Assuming you have a target variable\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "scaler = RobustScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train_scaled = scaler.transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "sUHyOm04SsqW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Npy5Jrf5mJoa"
      },
      "source": [
        "## **Normalizer** [12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xj0K1HPwmS-R"
      },
      "source": [
        "The normalizer scales each value by dividing each value by its magnitude in nn-dimensional space for nn number of features.\n",
        "Say your features were x, y, and z Cartesian co-ordinates your scaled value for x would be:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vn1lbD5CuHOJ"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=13kXDjD9ZJ1FsMcvOX_of9D-4pIam-z9h)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kImIKbW2mYuw"
      },
      "source": [
        "Each point is now within 1 unit of the origin on this Cartesian coordinate system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJOuXkTemaLx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "a191895c-56c5-4342-bf05-8d08557d9e8b"
      },
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "df = pd.DataFrame({\n",
        "    'x1': np.random.randint(-100, 100, 1000).astype(float),\n",
        "    'y1': np.random.randint(-80, 80, 1000).astype(float),\n",
        "    'z1': np.random.randint(-150, 150, 1000).astype(float),\n",
        "})\n",
        "scaler = preprocessing.Normalizer()\n",
        "scaled_df = scaler.fit_transform(df)\n",
        "scaled_df = pd.DataFrame(scaled_df, columns=df.columns)\n",
        "fig = plt.figure(figsize=(9, 5))\n",
        "ax1 = fig.add_subplot(121, projection='3d')\n",
        "ax2 = fig.add_subplot(122, projection='3d')\n",
        "ax1.scatter(df['x1'], df['y1'], df['z1'])\n",
        "ax2.scatter(scaled_df['x1'], scaled_df['y1'], scaled_df['z1'])\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qJoRdEemhYh"
      },
      "source": [
        "Note that the points are all brought within a sphere that is at most 1 away from the origin at any point. Also, the axes that were previously different scales are now all one scale."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Scaling in a pipeline**"
      ],
      "metadata": {
        "id": "JfwiVGsPS7zl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_classification\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "X, y = make_classification(n_samples=1000, n_features=5, n_informative=5, n_redundant=0, random_state=1)"
      ],
      "metadata": {
        "id": "wPSDkWwFT83x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "steps = [('scaler', StandardScaler()),('knn', KNeighborsClassifier())]\n",
        "pipeline = Pipeline(steps)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=21)\n",
        "knn_scaled = pipeline.fit(X_train, y_train)\n",
        "y_pred = pipeline.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)\n",
        "knn_unscaled = KNeighborsClassifier().fit(X_train, y_train)\n",
        "knn_unscaled.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoO1VywFTIoE",
        "outputId": "36ff20e6-3c9b-41f3-e2b4-2a4278fa0911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **CV and scaling in a pipeline**"
      ],
      "metadata": {
        "id": "8YjDe2hrVMfj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_classification\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "X, y = make_classification(n_samples=1000, n_features=5, n_informative=5, n_redundant=0, random_state=1)"
      ],
      "metadata": {
        "id": "cTsSjoxIV1d3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "steps = [('scaler', StandardScaler()),('knn', KNeighborsClassifier())]\n",
        "pipeline = Pipeline(steps)\n",
        "parameters = {\"knn__n_neighbors\": range(1, 50)}\n",
        "#parameters = {knn__n_neighbors=np.arange(1, 50)}\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=21)\n",
        "cv = GridSearchCV(pipeline, param_grid=parameters)\n",
        "cv.fit(X_train, y_train)\n",
        "y_pred = cv.predict(X_test)"
      ],
      "metadata": {
        "id": "418rR2a0VZZO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print best model and parameters\n",
        "best_model = cv.best_estimator_\n",
        "best_params = cv.best_params_\n",
        "print(\"Best model:\", best_model)\n",
        "print(\"Best parameters:\", best_params)"
      ],
      "metadata": {
        "id": "PclBXEBTkD84",
        "outputId": "94776b6f-3770-4119-b3c6-f6a3e60cdee0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best model: Pipeline(steps=[('scaler', StandardScaler()),\n",
            "                ('knn', KNeighborsClassifier(n_neighbors=4))])\n",
            "Best parameters: {'knn__n_neighbors': 4}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print(cv.best_params_){'knn__n_neighbors': 41}\n",
        "from sklearn.metrics import classification_report\n",
        "print(cv.score(X_test, y_test))\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VxH-e3cGVqJg",
        "outputId": "614b5da6-5f37-4f22-edba-77007bf02805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.89\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.92      0.90       106\n",
            "           1       0.90      0.86      0.88        94\n",
            "\n",
            "    accuracy                           0.89       200\n",
            "   macro avg       0.89      0.89      0.89       200\n",
            "weighted avg       0.89      0.89      0.89       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3uyiPD3Ivp_"
      },
      "source": [
        "# **Skewness [13]**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "636S73wKI_m-"
      },
      "source": [
        "We can also see the skewness of the target variable to check if it is right or left-skewed. SO, we can make the skewed distribution to normal distribution with mean and standard deviation.[13]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We all have come across the terms outliers or skewness while cleaning or preprocessing data for a project. So, in this article, we will be discussing the following points[15]"
      ],
      "metadata": {
        "id": "dybuJov1GGdf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What is skewness?**"
      ],
      "metadata": {
        "id": "Muo1jb3dGZsZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Skewness is an asymmetry in the distribution of data as it does not show any kind of symmetry in continuous data. Skewed data can be of 2 types. Right-Skewed data is also called as Positively-Skewed data and, Left-Skewed data is called as Negatively-Skewed data [15]\n",
        "\n",
        "Skewness is the measure of asymmetry of data distribution.\n",
        "If the data is positively skewed, then we can interpret that there are more values which are greater than the mean than the values that are lesser than the mean\n",
        "If the data is negatively skewed, then we can interpret that there are more values which are lesser than the mean than the values that are more than the mean\n",
        "This table elaborates the difference between positively and negatively skewed data [22]"
      ],
      "metadata": {
        "id": "f0A5wCSdGdqW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How to identify if your data is skewed or not?[15]**"
      ],
      "metadata": {
        "id": "aGwF3CtOGrxo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 2 main methods to identify skewness in the data. The first is the Observational method and, the second is the Statistical method."
      ],
      "metadata": {
        "id": "iqAKmaclG0cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. Observational Method**"
      ],
      "metadata": {
        "id": "fY3E8cr_JEiV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Identification of skewness can be done easily by plotting a histogram and observing a few characteristics.\n",
        "For a normal distribution i.e a distribution with no skewness the histogram should look like Fig(1) here, both the ends (tail regions) taper equally with a peek at the centre of the distribution like a “Bell-Curve”. For data with no skewness the Mean = Median = Mode.[15]"
      ],
      "metadata": {
        "id": "hJybwDSeJOVx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "![](https://drive.google.com/uc?export=view&id=1WjR2nnJUmKoiuZVUiDQADohDOqRGxkKk)"
      ],
      "metadata": {
        "id": "I2nDVuZ2JSoq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For distribution with left-skewness or negative skewness, the histogram should look like Fig(2) here the only the left part of the distribution tapers with the peak shifted towards the right-hand side. For Left-Skewed data the Mode > Median > Mean.[15]"
      ],
      "metadata": {
        "id": "O6J8yPqcJ78Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1_tcTjpoywmpL5Ixiyd_NcKZ-1IedALr0)"
      ],
      "metadata": {
        "id": "GAKQ2YVqKEg4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For distribution with right-skewness or positive-skewness the histogram should look like Fig(3) here the only the right part of the distribution tapers with the peak shifted towards the left-hand side. For Right-Skewed data the Mean > Median > Mode.[15]"
      ],
      "metadata": {
        "id": "BxdtfPkyKflA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=17aCr28uZ0v3Ue6mvtXKlPTAqe-EkGKpC)"
      ],
      "metadata": {
        "id": "iOv9bT3xM8Wl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Statistical Method**[15]"
      ],
      "metadata": {
        "id": "PEX7yNvSNG3s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, the method we studied above is the observational method. We can also calculate the skewness of our data using statistics as well. So the formula to calculate skewness is given below:"
      ],
      "metadata": {
        "id": "cfyUW9e7NMa-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1F_PaMDNfQyI8fEiZcMT0g7brEUzFeXVi)"
      ],
      "metadata": {
        "id": "TzzOZnT4Nerr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The acceptable value of skewness is considered to be between -3 and +3. If the value of skewness falls on either side of this range then it indicates that the skewness must be fixed."
      ],
      "metadata": {
        "id": "T5jn4cJYNpff"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Why is skewness a problem?**\n"
      ],
      "metadata": {
        "id": "AeBLb1jdNt7L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most of the statistical models do not work when the data is skewed. The reason behind this is that the tapering ends or the tail region of the skewed data distributions are the outliers in the data and it is known to us that outliers can severely damage the performance of a statistical model. The best example of this being regression models that show bad results when trained over skewed data.[15]"
      ],
      "metadata": {
        "id": "Rda5NpGMP2yl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How do we fix this skewness?**[15]\n"
      ],
      "metadata": {
        "id": "Cn2dEWYsP6Co"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, as we know that skewness is bad for our data and many machine learning algorithms prefer or perform better when numerical data has a normal distribution we need a way to remove this skewness, to do so in python most frequently used methods are PowerTransformer and QuantileTransformer. Both these methods are used to get a normal or Gaussian-like distribution. Now let us see how to use these methods with an example:\n",
        "For the example below the dataset considered is the European Credit Card dataset which you can find on Kaggle and I have considered a few random columns with skewness to perform the transformation. The code for implementing the transformers is as following:"
      ],
      "metadata": {
        "id": "JDSDQJN0QHbg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import PowerTransformer, QuantileTransformer\n",
        "cols1 = [\"V1\", \"V3\", \"V4\"]\n",
        "def test_transformers(columns):\n",
        "    pt = PowerTransformer()\n",
        "    qt = QuantileTransformer(n_quantiles=500, output_distribution='normal')\n",
        "    fig = plt.figure(figsize=(20,30))\n",
        "    j = 1\n",
        "    for i in columns:\n",
        "        array = np.array(df[i]).reshape(-1, 1)\n",
        "        y = pt.fit_transform(array)\n",
        "        x = qt.fit_transform(array)\n",
        "        plt.subplot(3,3,j)\n",
        "        sns.histplot(array, bins = 50, kde = True)\n",
        "        plt.title(f\"Original Distribution for {i}\")\n",
        "        plt.subplot(3,3,j+1)\n",
        "        sns.histplot(x, bins = 50, kde = True)\n",
        "        plt.title(f\"Quantile Transform for {i}\")\n",
        "        plt.subplot(3,3,j+2)\n",
        "        sns.histplot(y, bins = 50, kde = True)\n",
        "        plt.title(f\"Power Transform for {i}\")\n",
        "        j += 3\n",
        "test_transformers(cols1)"
      ],
      "metadata": {
        "id": "Vj_eyT3JNtFz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1D0uYyv5yQ1XgcNklue-Ah6GXhiSqUuHM)"
      ],
      "metadata": {
        "id": "lE2p1QEnQkXA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen that QuantileTransformer has performed better than PowerTransformer in terms of converting the skewed distribution into a normal distribution, but you should always select the method which works best with your data. It’s observed that QuantileTransformer works better on larger datasets than PowerTransformer.\n",
        "Other methods can be used to reduce the skewness as well. Like for left-skewed data squares, cubes or, higher power transforms can be performed and, for right-skewed data, we can use the Square-root, Cube-root or Log transforms. To know more about these, you can refer to this article."
      ],
      "metadata": {
        "id": "G_OFAAokQTNz"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "JQPfmlqUJOxo",
        "outputId": "75f6a9fa-6728-4ce4-ba0a-7c4b9ca95cb3"
      },
      "source": [
        "MiceImputed"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MRNO</th>\n",
              "      <th>AGE</th>\n",
              "      <th>GENDER</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>TEHSIL</th>\n",
              "      <th>REPORT_VERIFIED</th>\n",
              "      <th>CPT_ID</th>\n",
              "      <th>RESULT_VALUE</th>\n",
              "      <th>CPT_ID.1</th>\n",
              "      <th>RESULT_TEXT</th>\n",
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              "      <td>0.893454</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018519</td>\n",
              "      <td>0.476987</td>\n",
              "      <td>0.481995</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.888286</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>13513</th>\n",
              "      <td>0.627562</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018519</td>\n",
              "      <td>0.372402</td>\n",
              "      <td>0.633636</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>8974</th>\n",
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              "      <td>0.09</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.018519</td>\n",
              "      <td>0.468029</td>\n",
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              "    <tr>\n",
              "      <th>18480</th>\n",
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              "      <th>20294</th>\n",
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              "      <td>0.72</td>\n",
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              "      <td>0.567568</td>\n",
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              "<p>45512 rows × 10 columns</p>\n",
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            ],
            "text/plain": [
              "           MRNO   AGE  GENDER  ...  RESULT_VALUE  CPT_ID.1  RESULT_TEXT\n",
              "0      0.893023  0.30     0.5  ...           1.0       0.0          0.0\n",
              "1      0.893454  0.41     0.5  ...           1.0       0.0          0.0\n",
              "2      0.893454  0.41     0.5  ...           1.0       0.0          0.0\n",
              "3      0.888200  0.23     0.5  ...           1.0       0.0          0.0\n",
              "4      0.888286  0.40     0.5  ...           1.0       0.0          0.0\n",
              "...         ...   ...     ...  ...           ...       ...          ...\n",
              "13513  0.627562  0.84     0.5  ...           1.0       0.0          1.0\n",
              "8974   0.064083  0.09     0.5  ...           1.0       0.0          1.0\n",
              "18480  0.408010  0.20     0.5  ...           1.0       0.0          1.0\n",
              "20294  0.060034  0.72     0.0  ...           1.0       0.0          1.0\n",
              "1407   0.456072  0.87     0.5  ...           1.0       0.0          1.0\n",
              "\n",
              "[45512 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "t_MJLOLzJEI_",
        "outputId": "6de12c32-4a9c-4a52-8357-a91af639c637"
      },
      "source": [
        "#[13]\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.distplot(MiceImputed['RESULT_TEXT'], fit=stats.norm)\n",
        "# fit the mean and standard deviation\n",
        "(mu, sigma) = stats.norm.fit(MiceImputed['RESULT_TEXT'])\n",
        "# plot with the distribution\n",
        "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)], loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "#Probablity plot\n",
        "fig = plt.figure()\n",
        "stats.probplot(MiceImputed['RESULT_TEXT'], plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DJakRwCcFTukO3J79OUYCy0tavSGebWa1a8EcTucyXmRjNuIlDuVf/IrBvEWHvENr1mbOTG/7IH3OnFnz8YZQl1dPfYDBwPyBWV4DTmy4EApqC0ytsj0t2/cDknpIqpRUOX369BKFZta0bcI4RrEll3EGg9idTkykL4eSCviWt7ff/uFnTccbQl0SRZuIuAeYBxARc4C5DRfCoouIXhFREREVK6+8ct7hmDVqP+EbLuRsKqmgLe+wD/eyD/fzPi7Ml5O11vrhZ03HG0JdEsVXklaC1NJJ0lbAjIYLoaB3gHZVttfM9plZEWzNc4xlU87mYvpxIJ2YyP3sk3dYtoBWreCii9L6RRel7ZqON4S6JIqTgYHAOpKeBW4Hjm+4EAoaCByctX7aCpgREe+V6NlmDSIi7whqtzRfci1/5hm2oxUz+RWPcRh9+JQV8w6tWVl66e/HTWzRIn22b5+mFG/fPrWCat8+TQh4wAHp+AEHpO2ajjcERR3+FktaHNiA9HJyUqS+FIv+cOlOYCegDfABcC7QEiAibpIkoCewKzATOCwiKgvds6KiIiorC55iZlU9/jj06JFeah97LFx8MbRunXdUVmKSRkdERXXHah09VtLBC+zaTBIRcfuiBhYR+9dyPIBjF/U5ZlaNTz6BU05J4zNtsAGMGAHbbZd3VFaG6jLM+OZV1pcEfgGMIb2CMrPG6L77Uunho4/grLPgnHNgySXzjsrKVK2JIiJ+UB8haXngrqJFZGbF8/77cNxxKVFsuik89hh06ZJ3VFbmFmY+iq+AtRs6EDMrooj0iqlTJ3j44TSZ0KhRThJWJ3Wpo3gIvhsEcDGgE3BPMYMyswb05ptw1FGp0nq77aB371QnYVZHdamj+EeV9TnAWxExrUjxmFlDmTcvzTB35pmp3WTPnqmd5WKe2NLqpy51FE+VIhAza0CvvpqmJX32WfjVr+Dmm1MDe7OFUGOikPQFVDvvhEgtVz0lqlm5mT0brrgCzj8/jVndty8cdFAqUZgtpBoTRUS4x41ZYzJmDBxxRJpI6He/g3/+E1ZdNe+orAmo88tKSatIWmv+UsygzKwevv461UNssUVq/nr//XDPPU4S1mDqMh/FnpJeB94AniLNdvdokeMys7p45pnUxPXSS+GQQ2DiRNh777yjsiamLiWKC0izy70WEWuTemaPLGpUZlbYF1+kjnPbbw+zZsGQIXDrrbDCCnlHZk1QXRLF7Ij4GFhM0mIRMQyoduAoMyuBRx+Fzp3hhhvghBPgpZegW7e8o7ImrC79KD6TtAwwAugv6UNS72wzK6WPP4aTToI77oCOHVPT1623zjsqawZqLFFI+p2kJUnzVs8ETgIeA/4H/KY04ZkZEfCf/6ThN+68Mw3gN3ask4SVTKESxR+A60nzZd8JDI6IviWJysyS996DY46BBx6Arl3TMBybbJJ3VNbM1FiiiIi9gXWBJ0gz2k2TdJOkHUsVnFmzFQG33ZZeMT32GFx+OYwc6SRhuShYmR0Rn0dE34jYDdgQGAtcJ2lqSaIza46mTIFddkmd5zbZBMaPh9NOg8XrUqVo1vDq1OFO0grAb4HfAysC9xYzKLNmae5cuOYa2GijNAT4jTfCsGGw/vp5R2bNXKGxnpYB9gb2BzYFBpL6VAyPuky0bWZ1N3FiKkGMHAm77w433QTt2uUdlRlQuDL7TVIrpxtIFdmzSxKRWXMyaxZcdhlceCG0bg39+sEf/uBB/KysFEoU7SLi65JFYtbcVFamUsSLL8J++8G118Iqq+QdldmPFGr1VPQkIWlXSZMkTZZ0RjXHD5U0XdK4bDmy2DGZFd3XX8Ppp8OWW8JHH8GDD6b+EU4SVqZya0YhqQWpn8YvgWnAC5IGRsTEBU69OyKOK3mAZsXw1FNpQqHJk+GPf0zNXpdfPu+ozArKc07ELYDJETElImYBd5F6gZs1PZ9/nqYh3WmnNEXp0KHQq5eThDUKhVo9PUT1M9wBEBF7LuKz2wJV+2NMA7as5rx9JO0AvAacFBE/6sMhqQfQA2CttTxVhpWZRx6BP/0J3n0XTj4ZLrgAWrXKOyqzOitUovgHcCVpHoqvgVuy5UvSeE+l8BDQISI2BoYA1Q4hEhG9IqIiIipWXnnlEoVmVouPPoIDD4Q99oDlloPnnoMrr3SSsEan0FSoTwFIujIiqg4r/pCkygZ49jtA1Ybia2b7qsbwcZXN3sDlDfBcs+KKgLvvhuOPhxkz4Nxz4ayzYIkl8o7MbKHUpY5iaUk/nb8haW1g6QZ49gvAepLWlrQEsB+pU993JK1eZXNP4JUGeK5Z8bzzDuy1F+y/P6y9NoweDeed5yRhjVpdWj2dBAyXNAUQ0B44alEfHBFzJB1HGp22BXBbRLws6e9AZUQMBP4saU9gDvAJcOiiPtesKCKgd2849VSYPRv+8Q848URo0SLvyMwWmeoyGoeknwA/yzZfjYhvixrVIqioqIjKyoZ4M2ZWR//7X2rqOmxYatV0yy2w7rp5R2VWL5JGL1DN8J1aXz1JagWcBhwXEeOBtSTt0cAxmjU+c+fCVVelQfxGj07NXZ980knCmpy61FH8C5gFzJ9O6x3gwqJFZNYYTJgA22wDp5yS5queODGVKjxGkzVBdUkU60TE5cBsgIiYSaqrMGt+Zs2C88+HzTZL80bceWcagqNt27wjMyuaulRmz5K0FFnnO0nrAGVbR2FWNP/9bxrEb8KENMLrtddCmzZ5R2VWdHUpUZxLGm68naT+wFDg9KJGZVZOZs5Mr5i23ho+/RQeegj693eSsGajYIlC0mLA/NnttiK9cjohIj4qQWxm+Rs2LA3iN2VKGobj0ktTL2uzZqS2ObPnAadHxMcR8UhEPOwkYc3CjBnQowfsvDMsthgMH56mJnWSsGaoLq+enpB0qqR2klacvxQ9MrO8PPQQdOoEt94Kp50G48fDjjvmHZVZbupSmf377PPYKvsC+Gk155o1Xh9+CCecAHfdlfpGPPggVFTb/8isWak1UUTE2qUIxCw3EfDvf6ck8fnn8Pe/w1/+4vGZzDJ16pkt6a+SemXb67lntjUZU6fCb36ThgNfbz0YOxbOOcdJwqyK+vTM3ibbds9sa/zmzYObboLOnVPLpmuugWeeSdtm9gPumW3Nz+uvp9ZMRx8NW2wBL72UXjt5pFezatUlUbhntjUNc+bAFVfAxhvDuHGpVdOQIfBTt8swK6QurZ4W7Jm9LZ4Xwhqb8ePT8BujR0P37nDDDbDGGnlHZdYo1KXV0xBJY3DPbGuMvv0WLrww9ahecUW45x7Yd1+P8mpWDzUmCkmbLbDrvexzLUlrRcSY4oVl1gCefz6VIl55BQ46CK6+GlZaKe+ozBqdQiWKK7PPJYEKYDypRLExUMn381OYlZevvoKzz4brroM114RBg2C33fKOyqzRqrEyOyJ+HhE/J5UkNouIiojoCmxKaiJrVn6eeAI23DANAX7MMfDyy04SZouoLq2eNoiIl+ZvRMQEoGPxQjJbCJ9+ml4z/fKX0LIljBgBPXtC69Z5R2bW6NWl1dNLknoD/bLtA4AXixeSWT0NGJBKD9OnwxlnwN/+BkstlXdUZk1GXUoUhwIvAydky0TgsIZ4uKRdJU2SNFnSGdUc/4mku7PjoyR1aIjnVqd/f+jQIY0o3aFD2q7p2DHHfL/dpk1aJFh88R9+LrZY+vRSnGVVfcA9+j/47W8Z9/6qdJ07Cl16CWq1VO6x1WUxazQiosYFaAEMK3TOwi7Zvf9HGoV2CVJleacFzjkGuClb3w+4u7b7du3aNeqrX7+IVq0i0uhwaWnVKu2v7piXvJd5cRB942NWiG9YIs7kolicWWUQV/0Xs3IBVEZU/7ta8NVTRMyVNE/SchExo4Fz1BbA5IiYAiDpLqA7qcQyX3fgvGz9XqCnJGV/qAZz9tlptsuqZs5M++evW3lox9vczFHsxmM8yzYcwa1M4md5h2XWpNWljuJLUj3FEOCr+Tsj4s+L+Oy2wNQq29OALWs6JyLmSJoBrAT8oMOfpB5AD4C11lqr3oG8/Xb99lvpiXkczY1cyhmI4Hiu43qOJer09tTMFkVdEsX92VK2IqIX0AugoqKi3qWNtdaCt96qfj9Uf8xKZ30m0Zsj2Z5neJxf0oNevEWHvMMyazbqkijuBtbN1idHxDcN9Ox3gHZVttfkx/0z5p8zTdLiwHLAxw30/O9cdFGaHrnqK6ZWrdJ++PExK43Fmc0pXMl5nMfXLMWh/Iu+HAIevNispGost0taXNLlpFdCfYHbgamSLpfUsgGe/QKwnqS1JS1BqqweuMA5A4FDsvV9gScbun4C4IADoFcvaN8+tUZp3z5tH3BA9ceOPvr77ZVW+n5UiPmjVM//dMuWhdeFsYxiSy7lTB7h13TkFfpyKE0pSTT832Sz4lBNv7uSrgZaAydFxBfZvmWBfwBfR8QJi/xwaXfgGlILqNsi4iJJfyfVvg+UtCRwB6k3+CfAfvMrv2tSUVERlZWVixqa5eWbb+CCC+Cyy1K74+uvh332yTsqsyZP0uiIqHaS+EKJ4nVg/QX/BS+pBfBqRKzX4JE2ACeKRuzZZ1Pv6kmT4NBD4cor04ivZlZ0hRJFoSYjUd1rnoiYC7jQbA3nyy/hz3+G7bdPJYrBg+Ff/3KSMCsThRLFREkHL7hT0oHAq8ULyZqVwYPTIH49e8Jxx8GECbDLLnlHZWZVFGr1dCxwv6TDgdHZvgpgKWDvYgdmTdwnn8DJJ0PfvrDBBvD007DttnlHZWbVqDFRRMQ7wJaSdgY6Z7sHRcTQkkRmTdd998Gxx8JHH6Xu73/9Kyy5ZN5RmVkN6jIV6pPAkyWIxZq6995Lr5fuvx823RQeewy6dMk7KjOrhcc/sOKLgD59oFMneOSRNH/1f//rJGHWSNSlZ7bZwnvzzdS1fcgQ2G476N071UmYWaPhEoUVx9y5ac7qDTeE559PHeeeespJwqwRconCGt4rr8CRR8Jzz8Guu8JNN6UxT8ysUXKJwhrO7NlpJMUuXeDVV+H222HQICcJs0bOJQprGGPGwOGHw/jx8H//l147rbpq3lGZWQNwicIWzddfwxlnwBZbwAcfwIABcPfdThJmTYhLFLbwnn461UW89loazO+KK2CFFfKOyswamEsUVn+ff556Vu+wA8yalZq+9u7tJGHWRDlRWP08+mhq8nrjjXDiiWkQv27d8o7KzIrIicLq5uOP4eCDYffdoXXrNHfE1VfD0kvnHZmZFZkThRUWAffcAx07wp13wjnnpBZOW2+dd2RmViKuzLaavfsuHHMMPPggdO0KTzwBG2+cd1RmVmIuUdiPRcCtt6ZB/AYPhssvh5EjnSTMmimXKOyHpkyBP/4RnnwytWrq3RvWK8vp0c2sRHIpUUhaUdIQSa9nn9W2q5Q0V9K4bBlY6jiblblz4ZprYKON4IUXUqumYcOcJMwst1dPZwBDI2I9YGi2XZ2vI6JLtuxZuvCamZdfTtOQnnQS/PznaftPf4LF/GbSzPJLFN2Bvtl6X2CvnOJo3mbNggsuSLPNTZ4M/fvDQw9Bu3Z5R2ZmZSSvRLFqRLyXrb8P1DQw0JKSKiWNlFRjMpHUIzuvcvr06Q0ebJP0wgtQUQF/+xvss08aGvwPfwAp78jMrMwUrTJb0hPAatUcOrvqRkSEpKjhNu0j4h1JPwWelPRSRPxvwZMiohfQC6CioqKmexnAzJlw7rlw1VWw2mqp6euefqtnZjUrWqKIiBrHdZD0gaTVI+I9SasDH9Zwj3eyzymShgObAj9KFFZHw4enFk2TJ6fPK66A5ZbLOyozK3N5vXoaCBySrR8CPLjgCZJWkPSTbL0NsC0wsWQRNiUzZqTK6Z//HObNg6FDoVcvJwkzq5O8EsWlwC8lvQ50y7aRVCGpd3ZOR6BS0nhgGHBpRDhR1Ncjj0DnznDLLXDKKfDSS7DzznlHZWaNSC4d7iLiY+AX1eyvBI7M1p8DNipxaE3H9OlpdNd//zuN9nr//WlyITOzenJD+aYmIg3e16kT/Oc/cN55MHq0k4SZLTQP4dGUTJsGRx8NDz+cEsOtt6bShJnZInCJoimYNy9VTnfunCqqr7wSnnvOSZsuGCQAAAqcSURBVMLMGoRLFI3d/Kauw4enVk233ALrrJN3VGbWhLhE0VjNnZtKDhtvnCYSuuWWVJpwkjCzBuYSRWP00ktwxBFpGI7f/CaN9Nq2bd5RmVkT5RJFY/Ltt2n4jc02gzffhLvuSkNwOEmYWRG5RNFYjBqVShEvvwwHHJDmjmjTJu+ozKwZcImi3H31FZx8Mmy9dRqK4+GHoV8/JwkzKxmXKMrZk0+mFk1TpqSxmi67DJZdNu+ozKyZcYmiHH32WUoQv/hFmmVu+PBUYe0kYWY5cKIoNwMHpo5zt90Gp58OL74IO+6Yd1Rm1ow5UZSLDz+E/faD7t1hpZVS5fVll8FSS+UdmZk1c04UeYtIldMdO8KAAWkO68rKNE2pmVkZcGV2nqZOTZXUgwbBVlulQfw6dco7KjOzH3CJIg/z5qXK6c6dU0X1NdfAM884SZhZWXKJotReey21aBoxArp1S6O+rr123lGZmdXIJYpSmTMHLr8cNtkExo9Pr5kef9xJwszKnksUpTB+PBx+eBrlda+94PrrYY018o7KzKxOXKIopm+/hXPOSS2Ypk2De+5Jc1c7SZhZI+ISRbE8/3waxO+VV+Dgg+Gqq1L/CDOzRiaXEoWk30l6WdI8STV2GJC0q6RJkiZLOqOUMS60L7+EE0+EbbdNA/o9+ij07eskYWaNVl6vniYAvwVG1HSCpBbA9cBuQCdgf0nl3X50yBDYaCO49lo45hiYMAF23TXvqMzMFkkuiSIiXomISbWctgUwOSKmRMQs4C6ge/GjWwiffpoqq3fZBZZYIjV97dkTWrfOOzIzs0VWzpXZbYGpVbanZft+RFIPSZWSKqdPn16S4L4zYEDqKHf77XDGGamF0/bblzYGM7MiKlpltqQngNWqOXR2RDzYkM+KiF5AL4CKiopoyHvX6P334fjj4d57oUsXeOSRNEWpmVkTU7REERHdFvEW7wDtqmyvme3LVwTccUeqsJ45Ey6+GE49FVq2zDsyM7OiKOdXTy8A60laW9ISwH7AwFwjeust2G03OOSQNNrruHFw5plOEmbWpOXVPHZvSdOArYFHJA3O9q8haRBARMwBjgMGA68A90TEy3nEy7x5qXK6c+c0eN8//wlPPw0/+1ku4ZiZlVIuHe4iYgAwoJr97wK7V9keBAwqYWg/NmlS6jj37LOpVdPNN0OHDrmGZGZWSuX86ilfs2fDJZekQfwmToQ+feCxx5wkzKzZ8RAe1Rk7NpUixo6FffZJr51Wq64Bl5lZ0+cSRVXffANnnQWbbw7vvpuavt57r5OEmTVrLlHM98YbqUXTpElw2GFw5ZWwwgp5R2VmljsnivnatoV114XrrkuV1mZmBjhRfG+JJeDhh/OOwsys7LiOwszMCnKiMDOzgpwozMysICcKMzMryInCzMwKcqIwM7OCnCjMzKwgJwozMytIEaWZObRUJE0H3irCrdsAHxXhvk2Jv6Pa+Tuqnb+j2hXjO2ofEStXd6DJJYpikVQZERV5x1HO/B3Vzt9R7fwd1a7U35FfPZmZWUFOFGZmVpATRd31yjuARsDfUe38HdXO31HtSvoduY7CzMwKconCzMwKcqIwM7OCnCgWgqRTJIWkNnnHUm4kXSHpVUkvShogafm8YyoHknaVNEnSZEln5B1PuZHUTtIwSRMlvSzphLxjKleSWkgaK6lkM605UdSTpHbALsDbecdSpoYAG0bExsBrwJk5x5M7SS2A64HdgE7A/pI65RtV2ZkDnBIRnYCtgGP9HdXoBOCVUj7QiaL+rgZOB9wKoBoR8XhEzMk2RwJr5hlPmdgCmBwRUyJiFnAX0D3nmMpKRLwXEWOy9S9IP4Rt842q/EhaE/g10LuUz3WiqAdJ3YF3ImJ83rE0EocDj+YdRBloC0ytsj0N/wjWSFIHYFNgVL6RlKVrSP9QnVfKhy5eyoc1BpKeAFar5tDZwFmk107NWqHvKCIezM45m/Q6oX8pY7PGTdIywH3AiRHxed7xlBNJewAfRsRoSTuV8tlOFAuIiG7V7Ze0EbA2MF4SpFcqYyRtERHvlzDE3NX0Hc0n6VBgD+AX4Y46AO8A7apsr5ntsyoktSQlif4RcX/e8ZShbYE9Je0OLAksK6lfRBxY7Ae7w91CkvQmUBERHuWyCkm7AlcBO0bE9LzjKQeSFidV7P+ClCBeAP4QES/nGlgZUfrXV1/gk4g4Me94yl1Wojg1IvYoxfNcR2ENrSfQGhgiaZykm/IOKG9Z5f5xwGBSJe09ThI/si1wELBz9vdmXPYvZysDLlGYmVlBLlGYmVlBThRmZlaQE4WZmRXkRGFmZgU5UZiZWUFOFFa2JK1Upank+5LeydY/kzSxxLHsVXWQOkl/l1Sw42EN9+kgaULDRlev55+1wPZz2WeucVl5c6KwshURH0dEl4joAtwEXJ2td6EIY91kHeNqshdp5Nf5sf0tIp5o6BhK4AeJIiK2ySsQazycKKyxaiHplmzugsclLQUgaR1Jj0kaLelpST/L9neQ9GQ2T8ZQSWtl+/tIuknSKODy6q6XtA2wJ3BFVqJZJ7tu3+wem0t6TtJ4Sf+V1Dp73tOSxmRLwR9kJT2zOSuekDSoyv3fnD/3iaQKScOz9S0kPZ/NTfCcpA2y/YdKuj/7c7wu6fJs/6XAUtmfoX+278tqYmmhNK/IC9n3dVS2f3VJI7LrJ0jafhH/G1pjERFevJT9ApxHGrIAoANpwMEu2fY9wIHZ+lBgvWx9S+DJbP0h4JBs/XDggWy9D/Aw0KKW6/sA+1aJpw+wL7AEMAXYPNu/LGkMtVbAktm+9YDKKrFPqObP91vSXB4tgDWAz+Y/D3gTaJOtVwDDqz4rW+8G3JetH5rFtBxpTKC3gHbZsS8XeO6XC8YF9AD+mq3/BKgkjXN2CmngR7I4W+f998JLaRYPCmiN1RsRMS5bHw10yEYe3Qb4TzZwI6QfOoCtST/GAHcAl1e5138iYm4t19dkA+C9iHgBILIRTyUtDfSU1AWYC6xfy312AO6MiLnAu5KerOV8SImgr6T1SPOjtKxybGhEzMhimQi054dDnReyC7Dx/BJN9pz1SGNU3ZYN3vdAle/fmjgnCmusvq2yPhdYivQq9bNI9Rj18VX2ubDXV+ck4ANgk+y+3yzCvebw/WviJavsvwAYFhF7K83hMLzKsQW/n/r8vy7g+IgY/KMD0g6kiXP6SLoqIm6vx32tkXIdhTUZ2b/m35D0O/juvf8m2eHngP2y9QOAp+t5/RekwQ4XNAlYXdLm2TWts0rx5UgljXmkwe5a1BL+COD3Wf3A6sDPqxx7E+iare9TZf9yfD9c+aG13H++2VmJoJDBwNHzz5O0vqSlJbUHPoiIW0gzrG1Wx2daI+dEYU3NAcARksYDL/P9lKPHA4dJepH0w31CPa+/CzgtqzheZ/7JkaY2/T3wz+yaIaR/9d8AHJLt+xnfl1pqMgB4HZgI3A48X+XY+cC1kipJpYP5LgcukTSWupcYegEvzq/MrkHvLI4xWZPZm7P770Saj2Us6c98bR2faY2cR481K0OS+gAPR8S9ecdi5hKFmZkV5BKFmZkV5BKFmZkV5ERhZmYFOVGYmVlBThRmZlaQE4WZmRX0/10YdCcj7AXTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfZ907RtJuN-"
      },
      "source": [
        "From the above distribution, it is observed that the target column is right-skewed and one more observation we can conclude from this is that the outliers are on the right side. To make the target variable in a normal distribution we used log function so that the mean and standard deviation becomes to make as a standard normal distribution after standardizing the values in the target column.[13]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "id": "dg5GN17XJ0no",
        "outputId": "6abf3381-6be2-4b43-e416-836ecfe59334"
      },
      "source": [
        "#This target variabale is right skewed. Now, we need to transform this variable and make it normal distribution.\n",
        "#using log function to normalize#\n",
        "#[13]\n",
        "MiceImputed['price'] = np.log1p(MiceImputed['RESULT_TEXT'])\n",
        "#Check again for more normal distribution\n",
        "plt.subplots(figsize=(12,9))\n",
        "sns.distplot(MiceImputed['RESULT_TEXT'], fit=stats.norm)\n",
        "# fit the mean and standard deviation\n",
        "(mu, sigma) = stats.norm.fit(MiceImputed['RESULT_TEXT'])\n",
        "# plot with the distribution\n",
        "plt.legend(['Normal dist.($\\mu=$ {:.2f} and $\\sigma=$ {:.2f})'\n",
        "                           .format(mu, sigma)], loc='best')\n",
        "plt.ylabel('Frequency')\n",
        "#Probablity plot\n",
        "fig = plt.figure()\n",
        "stats.probplot(MiceImputed['RESULT_TEXT'], plot=plt)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DJakRwCcFTukO3J79OUYCy0tavSGebWa1a8EcTucyXmRjNuIlDuVf/IrBvEWHvENr1mbOTG/7IH3OnFnz8YZQl1dPfYDBwPyBWV4DTmy4EApqC0ytsj0t2/cDknpIqpRUOX369BKFZta0bcI4RrEll3EGg9idTkykL4eSCviWt7ff/uFnTccbQl0SRZuIuAeYBxARc4C5DRfCoouIXhFREREVK6+8ct7hmDVqP+EbLuRsKqmgLe+wD/eyD/fzPi7Ml5O11vrhZ03HG0JdEsVXklaC1NJJ0lbAjIYLoaB3gHZVttfM9plZEWzNc4xlU87mYvpxIJ2YyP3sk3dYtoBWreCii9L6RRel7ZqON4S6JIqTgYHAOpKeBW4Hjm+4EAoaCByctX7aCpgREe+V6NlmDSIi7whqtzRfci1/5hm2oxUz+RWPcRh9+JQV8w6tWVl66e/HTWzRIn22b5+mFG/fPrWCat8+TQh4wAHp+AEHpO2ajjcERR3+FktaHNiA9HJyUqS+FIv+cOlOYCegDfABcC7QEiAibpIkoCewKzATOCwiKgvds6KiIiorC55iZlU9/jj06JFeah97LFx8MbRunXdUVmKSRkdERXXHah09VtLBC+zaTBIRcfuiBhYR+9dyPIBjF/U5ZlaNTz6BU05J4zNtsAGMGAHbbZd3VFaG6jLM+OZV1pcEfgGMIb2CMrPG6L77Uunho4/grLPgnHNgySXzjsrKVK2JIiJ+UB8haXngrqJFZGbF8/77cNxxKVFsuik89hh06ZJ3VFbmFmY+iq+AtRs6EDMrooj0iqlTJ3j44TSZ0KhRThJWJ3Wpo3gIvhsEcDGgE3BPMYMyswb05ptw1FGp0nq77aB371QnYVZHdamj+EeV9TnAWxExrUjxmFlDmTcvzTB35pmp3WTPnqmd5WKe2NLqpy51FE+VIhAza0CvvpqmJX32WfjVr+Dmm1MDe7OFUGOikPQFVDvvhEgtVz0lqlm5mT0brrgCzj8/jVndty8cdFAqUZgtpBoTRUS4x41ZYzJmDBxxRJpI6He/g3/+E1ZdNe+orAmo88tKSatIWmv+UsygzKwevv461UNssUVq/nr//XDPPU4S1mDqMh/FnpJeB94AniLNdvdokeMys7p45pnUxPXSS+GQQ2DiRNh777yjsiamLiWKC0izy70WEWuTemaPLGpUZlbYF1+kjnPbbw+zZsGQIXDrrbDCCnlHZk1QXRLF7Ij4GFhM0mIRMQyoduAoMyuBRx+Fzp3hhhvghBPgpZegW7e8o7ImrC79KD6TtAwwAugv6UNS72wzK6WPP4aTToI77oCOHVPT1623zjsqawZqLFFI+p2kJUnzVs8ETgIeA/4H/KY04ZkZEfCf/6ThN+68Mw3gN3ask4SVTKESxR+A60nzZd8JDI6IviWJysyS996DY46BBx6Arl3TMBybbJJ3VNbM1FiiiIi9gXWBJ0gz2k2TdJOkHUsVnFmzFQG33ZZeMT32GFx+OYwc6SRhuShYmR0Rn0dE34jYDdgQGAtcJ2lqSaIza46mTIFddkmd5zbZBMaPh9NOg8XrUqVo1vDq1OFO0grAb4HfAysC9xYzKLNmae5cuOYa2GijNAT4jTfCsGGw/vp5R2bNXKGxnpYB9gb2BzYFBpL6VAyPuky0bWZ1N3FiKkGMHAm77w433QTt2uUdlRlQuDL7TVIrpxtIFdmzSxKRWXMyaxZcdhlceCG0bg39+sEf/uBB/KysFEoU7SLi65JFYtbcVFamUsSLL8J++8G118Iqq+QdldmPFGr1VPQkIWlXSZMkTZZ0RjXHD5U0XdK4bDmy2DGZFd3XX8Ppp8OWW8JHH8GDD6b+EU4SVqZya0YhqQWpn8YvgWnAC5IGRsTEBU69OyKOK3mAZsXw1FNpQqHJk+GPf0zNXpdfPu+ozArKc07ELYDJETElImYBd5F6gZs1PZ9/nqYh3WmnNEXp0KHQq5eThDUKhVo9PUT1M9wBEBF7LuKz2wJV+2NMA7as5rx9JO0AvAacFBE/6sMhqQfQA2CttTxVhpWZRx6BP/0J3n0XTj4ZLrgAWrXKOyqzOitUovgHcCVpHoqvgVuy5UvSeE+l8BDQISI2BoYA1Q4hEhG9IqIiIipWXnnlEoVmVouPPoIDD4Q99oDlloPnnoMrr3SSsEan0FSoTwFIujIiqg4r/pCkygZ49jtA1Ybia2b7qsbwcZXN3sDlDfBcs+KKgLvvhuOPhxkz4Nxz4ayzYIkl8o7MbKHUpY5iaUk/nb8haW1g6QZ49gvAepLWlrQEsB+pU993JK1eZXNP4JUGeK5Z8bzzDuy1F+y/P6y9NoweDeed5yRhjVpdWj2dBAyXNAUQ0B44alEfHBFzJB1HGp22BXBbRLws6e9AZUQMBP4saU9gDvAJcOiiPtesKCKgd2849VSYPRv+8Q848URo0SLvyMwWmeoyGoeknwA/yzZfjYhvixrVIqioqIjKyoZ4M2ZWR//7X2rqOmxYatV0yy2w7rp5R2VWL5JGL1DN8J1aXz1JagWcBhwXEeOBtSTt0cAxmjU+c+fCVVelQfxGj07NXZ980knCmpy61FH8C5gFzJ9O6x3gwqJFZNYYTJgA22wDp5yS5queODGVKjxGkzVBdUkU60TE5cBsgIiYSaqrMGt+Zs2C88+HzTZL80bceWcagqNt27wjMyuaulRmz5K0FFnnO0nrAGVbR2FWNP/9bxrEb8KENMLrtddCmzZ5R2VWdHUpUZxLGm68naT+wFDg9KJGZVZOZs5Mr5i23ho+/RQeegj693eSsGajYIlC0mLA/NnttiK9cjohIj4qQWxm+Rs2LA3iN2VKGobj0ktTL2uzZqS2ObPnAadHxMcR8UhEPOwkYc3CjBnQowfsvDMsthgMH56mJnWSsGaoLq+enpB0qqR2klacvxQ9MrO8PPQQdOoEt94Kp50G48fDjjvmHZVZbupSmf377PPYKvsC+Gk155o1Xh9+CCecAHfdlfpGPPggVFTb/8isWak1UUTE2qUIxCw3EfDvf6ck8fnn8Pe/w1/+4vGZzDJ16pkt6a+SemXb67lntjUZU6fCb36ThgNfbz0YOxbOOcdJwqyK+vTM3ibbds9sa/zmzYObboLOnVPLpmuugWeeSdtm9gPumW3Nz+uvp9ZMRx8NW2wBL72UXjt5pFezatUlUbhntjUNc+bAFVfAxhvDuHGpVdOQIfBTt8swK6QurZ4W7Jm9LZ4Xwhqb8ePT8BujR0P37nDDDbDGGnlHZdYo1KXV0xBJY3DPbGuMvv0WLrww9ahecUW45x7Yd1+P8mpWDzUmCkmbLbDrvexzLUlrRcSY4oVl1gCefz6VIl55BQ46CK6+GlZaKe+ozBqdQiWKK7PPJYEKYDypRLExUMn381OYlZevvoKzz4brroM114RBg2C33fKOyqzRqrEyOyJ+HhE/J5UkNouIiojoCmxKaiJrVn6eeAI23DANAX7MMfDyy04SZouoLq2eNoiIl+ZvRMQEoGPxQjJbCJ9+ml4z/fKX0LIljBgBPXtC69Z5R2bW6NWl1dNLknoD/bLtA4AXixeSWT0NGJBKD9OnwxlnwN/+BkstlXdUZk1GXUoUhwIvAydky0TgsIZ4uKRdJU2SNFnSGdUc/4mku7PjoyR1aIjnVqd/f+jQIY0o3aFD2q7p2DHHfL/dpk1aJFh88R9+LrZY+vRSnGVVfcA9+j/47W8Z9/6qdJ07Cl16CWq1VO6x1WUxazQiosYFaAEMK3TOwi7Zvf9HGoV2CVJleacFzjkGuClb3w+4u7b7du3aNeqrX7+IVq0i0uhwaWnVKu2v7piXvJd5cRB942NWiG9YIs7kolicWWUQV/0Xs3IBVEZU/7ta8NVTRMyVNE/SchExo4Fz1BbA5IiYAiDpLqA7qcQyX3fgvGz9XqCnJGV/qAZz9tlptsuqZs5M++evW3lox9vczFHsxmM8yzYcwa1M4md5h2XWpNWljuJLUj3FEOCr+Tsj4s+L+Oy2wNQq29OALWs6JyLmSJoBrAT8oMOfpB5AD4C11lqr3oG8/Xb99lvpiXkczY1cyhmI4Hiu43qOJer09tTMFkVdEsX92VK2IqIX0AugoqKi3qWNtdaCt96qfj9Uf8xKZ30m0Zsj2Z5neJxf0oNevEWHvMMyazbqkijuBtbN1idHxDcN9Ox3gHZVttfkx/0z5p8zTdLiwHLAxw30/O9cdFGaHrnqK6ZWrdJ++PExK43Fmc0pXMl5nMfXLMWh/Iu+HAIevNispGost0taXNLlpFdCfYHbgamSLpfUsgGe/QKwnqS1JS1BqqweuMA5A4FDsvV9gScbun4C4IADoFcvaN8+tUZp3z5tH3BA9ceOPvr77ZVW+n5UiPmjVM//dMuWhdeFsYxiSy7lTB7h13TkFfpyKE0pSTT832Sz4lBNv7uSrgZaAydFxBfZvmWBfwBfR8QJi/xwaXfgGlILqNsi4iJJfyfVvg+UtCRwB6k3+CfAfvMrv2tSUVERlZWVixqa5eWbb+CCC+Cyy1K74+uvh332yTsqsyZP0uiIqHaS+EKJ4nVg/QX/BS+pBfBqRKzX4JE2ACeKRuzZZ1Pv6kmT4NBD4cor04ivZlZ0hRJFoSYjUd1rnoiYC7jQbA3nyy/hz3+G7bdPJYrBg+Ff/3KSMCsThRLFREkHL7hT0oHAq8ULyZqVwYPTIH49e8Jxx8GECbDLLnlHZWZVFGr1dCxwv6TDgdHZvgpgKWDvYgdmTdwnn8DJJ0PfvrDBBvD007DttnlHZWbVqDFRRMQ7wJaSdgY6Z7sHRcTQkkRmTdd998Gxx8JHH6Xu73/9Kyy5ZN5RmVkN6jIV6pPAkyWIxZq6995Lr5fuvx823RQeewy6dMk7KjOrhcc/sOKLgD59oFMneOSRNH/1f//rJGHWSNSlZ7bZwnvzzdS1fcgQ2G476N071UmYWaPhEoUVx9y5ac7qDTeE559PHeeeespJwqwRconCGt4rr8CRR8Jzz8Guu8JNN6UxT8ysUXKJwhrO7NlpJMUuXeDVV+H222HQICcJs0bOJQprGGPGwOGHw/jx8H//l147rbpq3lGZWQNwicIWzddfwxlnwBZbwAcfwIABcPfdThJmTYhLFLbwnn461UW89loazO+KK2CFFfKOyswamEsUVn+ff556Vu+wA8yalZq+9u7tJGHWRDlRWP08+mhq8nrjjXDiiWkQv27d8o7KzIrIicLq5uOP4eCDYffdoXXrNHfE1VfD0kvnHZmZFZkThRUWAffcAx07wp13wjnnpBZOW2+dd2RmViKuzLaavfsuHHMMPPggdO0KTzwBG2+cd1RmVmIuUdiPRcCtt6ZB/AYPhssvh5EjnSTMmimXKOyHpkyBP/4RnnwytWrq3RvWK8vp0c2sRHIpUUhaUdIQSa9nn9W2q5Q0V9K4bBlY6jiblblz4ZprYKON4IUXUqumYcOcJMwst1dPZwBDI2I9YGi2XZ2vI6JLtuxZuvCamZdfTtOQnnQS/PznaftPf4LF/GbSzPJLFN2Bvtl6X2CvnOJo3mbNggsuSLPNTZ4M/fvDQw9Bu3Z5R2ZmZSSvRLFqRLyXrb8P1DQw0JKSKiWNlFRjMpHUIzuvcvr06Q0ebJP0wgtQUQF/+xvss08aGvwPfwAp78jMrMwUrTJb0hPAatUcOrvqRkSEpKjhNu0j4h1JPwWelPRSRPxvwZMiohfQC6CioqKmexnAzJlw7rlw1VWw2mqp6euefqtnZjUrWqKIiBrHdZD0gaTVI+I9SasDH9Zwj3eyzymShgObAj9KFFZHw4enFk2TJ6fPK66A5ZbLOyozK3N5vXoaCBySrR8CPLjgCZJWkPSTbL0NsC0wsWQRNiUzZqTK6Z//HObNg6FDoVcvJwkzq5O8EsWlwC8lvQ50y7aRVCGpd3ZOR6BS0nhgGHBpRDhR1Ncjj0DnznDLLXDKKfDSS7DzznlHZWaNSC4d7iLiY+AX1eyvBI7M1p8DNipxaE3H9OlpdNd//zuN9nr//WlyITOzenJD+aYmIg3e16kT/Oc/cN55MHq0k4SZLTQP4dGUTJsGRx8NDz+cEsOtt6bShJnZInCJoimYNy9VTnfunCqqr7wSnnvOSZsuGCQAAAqcSURBVMLMGoRLFI3d/Kauw4enVk233ALrrJN3VGbWhLhE0VjNnZtKDhtvnCYSuuWWVJpwkjCzBuYSRWP00ktwxBFpGI7f/CaN9Nq2bd5RmVkT5RJFY/Ltt2n4jc02gzffhLvuSkNwOEmYWRG5RNFYjBqVShEvvwwHHJDmjmjTJu+ozKwZcImi3H31FZx8Mmy9dRqK4+GHoV8/JwkzKxmXKMrZk0+mFk1TpqSxmi67DJZdNu+ozKyZcYmiHH32WUoQv/hFmmVu+PBUYe0kYWY5cKIoNwMHpo5zt90Gp58OL74IO+6Yd1Rm1ow5UZSLDz+E/faD7t1hpZVS5fVll8FSS+UdmZk1c04UeYtIldMdO8KAAWkO68rKNE2pmVkZcGV2nqZOTZXUgwbBVlulQfw6dco7KjOzH3CJIg/z5qXK6c6dU0X1NdfAM884SZhZWXKJotReey21aBoxArp1S6O+rr123lGZmdXIJYpSmTMHLr8cNtkExo9Pr5kef9xJwszKnksUpTB+PBx+eBrlda+94PrrYY018o7KzKxOXKIopm+/hXPOSS2Ypk2De+5Jc1c7SZhZI+ISRbE8/3waxO+VV+Dgg+Gqq1L/CDOzRiaXEoWk30l6WdI8STV2GJC0q6RJkiZLOqOUMS60L7+EE0+EbbdNA/o9+ij07eskYWaNVl6vniYAvwVG1HSCpBbA9cBuQCdgf0nl3X50yBDYaCO49lo45hiYMAF23TXvqMzMFkkuiSIiXomISbWctgUwOSKmRMQs4C6ge/GjWwiffpoqq3fZBZZYIjV97dkTWrfOOzIzs0VWzpXZbYGpVbanZft+RFIPSZWSKqdPn16S4L4zYEDqKHf77XDGGamF0/bblzYGM7MiKlpltqQngNWqOXR2RDzYkM+KiF5AL4CKiopoyHvX6P334fjj4d57oUsXeOSRNEWpmVkTU7REERHdFvEW7wDtqmyvme3LVwTccUeqsJ45Ey6+GE49FVq2zDsyM7OiKOdXTy8A60laW9ISwH7AwFwjeust2G03OOSQNNrruHFw5plOEmbWpOXVPHZvSdOArYFHJA3O9q8haRBARMwBjgMGA68A90TEy3nEy7x5qXK6c+c0eN8//wlPPw0/+1ku4ZiZlVIuHe4iYgAwoJr97wK7V9keBAwqYWg/NmlS6jj37LOpVdPNN0OHDrmGZGZWSuX86ilfs2fDJZekQfwmToQ+feCxx5wkzKzZ8RAe1Rk7NpUixo6FffZJr51Wq64Bl5lZ0+cSRVXffANnnQWbbw7vvpuavt57r5OEmTVrLlHM98YbqUXTpElw2GFw5ZWwwgp5R2VmljsnivnatoV114XrrkuV1mZmBjhRfG+JJeDhh/OOwsys7LiOwszMCnKiMDOzgpwozMysICcKMzMryInCzMwKcqIwM7OCnCjMzKwgJwozMytIEaWZObRUJE0H3irCrdsAHxXhvk2Jv6Pa+Tuqnb+j2hXjO2ofEStXd6DJJYpikVQZERV5x1HO/B3Vzt9R7fwd1a7U35FfPZmZWUFOFGZmVpATRd31yjuARsDfUe38HdXO31HtSvoduY7CzMwKconCzMwKcqIwM7OCnCgWgqRTJIWkNnnHUm4kXSHpVUkvShogafm8YyoHknaVNEnSZEln5B1PuZHUTtIwSRMlvSzphLxjKleSWkgaK6lkM605UdSTpHbALsDbecdSpoYAG0bExsBrwJk5x5M7SS2A64HdgE7A/pI65RtV2ZkDnBIRnYCtgGP9HdXoBOCVUj7QiaL+rgZOB9wKoBoR8XhEzMk2RwJr5hlPmdgCmBwRUyJiFnAX0D3nmMpKRLwXEWOy9S9IP4Rt842q/EhaE/g10LuUz3WiqAdJ3YF3ImJ83rE0EocDj+YdRBloC0ytsj0N/wjWSFIHYFNgVL6RlKVrSP9QnVfKhy5eyoc1BpKeAFar5tDZwFmk107NWqHvKCIezM45m/Q6oX8pY7PGTdIywH3AiRHxed7xlBNJewAfRsRoSTuV8tlOFAuIiG7V7Ze0EbA2MF4SpFcqYyRtERHvlzDE3NX0Hc0n6VBgD+AX4Y46AO8A7apsr5ntsyoktSQlif4RcX/e8ZShbYE9Je0OLAksK6lfRBxY7Ae7w91CkvQmUBERHuWyCkm7AlcBO0bE9LzjKQeSFidV7P+ClCBeAP4QES/nGlgZUfrXV1/gk4g4Me94yl1Wojg1IvYoxfNcR2ENrSfQGhgiaZykm/IOKG9Z5f5xwGBSJe09ThI/si1wELBz9vdmXPYvZysDLlGYmVlBLlGYmVlBThRmZlaQE4WZmRXkRGFmZgU5UZiZWUFOFFa2JK1Upank+5LeydY/kzSxxLHsVXWQOkl/l1Sw42EN9+kgaULDRlev55+1wPZz2WeucVl5c6KwshURH0dEl4joAtwEXJ2td6EIY91kHeNqshdp5Nf5sf0tIp5o6BhK4AeJIiK2ySsQazycKKyxaiHplmzugsclLQUgaR1Jj0kaLelpST/L9neQ9GQ2T8ZQSWtl+/tIuknSKODy6q6XtA2wJ3BFVqJZJ7tu3+wem0t6TtJ4Sf+V1Dp73tOSxmRLwR9kJT2zOSuekDSoyv3fnD/3iaQKScOz9S0kPZ/NTfCcpA2y/YdKuj/7c7wu6fJs/6XAUtmfoX+278tqYmmhNK/IC9n3dVS2f3VJI7LrJ0jafhH/G1pjERFevJT9ApxHGrIAoANpwMEu2fY9wIHZ+lBgvWx9S+DJbP0h4JBs/XDggWy9D/Aw0KKW6/sA+1aJpw+wL7AEMAXYPNu/LGkMtVbAktm+9YDKKrFPqObP91vSXB4tgDWAz+Y/D3gTaJOtVwDDqz4rW+8G3JetH5rFtBxpTKC3gHbZsS8XeO6XC8YF9AD+mq3/BKgkjXN2CmngR7I4W+f998JLaRYPCmiN1RsRMS5bHw10yEYe3Qb4TzZwI6QfOoCtST/GAHcAl1e5138iYm4t19dkA+C9iHgBILIRTyUtDfSU1AWYC6xfy312AO6MiLnAu5KerOV8SImgr6T1SPOjtKxybGhEzMhimQi054dDnReyC7Dx/BJN9pz1SGNU3ZYN3vdAle/fmjgnCmusvq2yPhdYivQq9bNI9Rj18VX2ubDXV+ck4ANgk+y+3yzCvebw/WviJavsvwAYFhF7K83hMLzKsQW/n/r8vy7g+IgY/KMD0g6kiXP6SLoqIm6vx32tkXIdhTUZ2b/m35D0O/juvf8m2eHngP2y9QOAp+t5/RekwQ4XNAlYXdLm2TWts0rx5UgljXmkwe5a1BL+COD3Wf3A6sDPqxx7E+iare9TZf9yfD9c+aG13H++2VmJoJDBwNHzz5O0vqSlJbUHPoiIW0gzrG1Wx2daI+dEYU3NAcARksYDL/P9lKPHA4dJepH0w31CPa+/CzgtqzheZ/7JkaY2/T3wz+yaIaR/9d8AHJLt+xnfl1pqMgB4HZgI3A48X+XY+cC1kipJpYP5LgcukTSWupcYegEvzq/MrkHvLI4xWZPZm7P770Saj2Us6c98bR2faY2cR481K0OS+gAPR8S9ecdi5hKFmZkV5BKFmZkV5BKFmZkV5ERhZmYFOVGYmVlBThRmZlaQE4WZmRX0/10YdCcj7AXTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-OoCHQ6TwE_f"
      },
      "source": [
        "# **📚Section-6- Outlier detection**\n",
        "\n",
        "As most of the machine learning and deep learning algorithms are sensitive, it may take a longer time for training also gives us a less accurate model and poor results."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What are Outliers ?**"
      ],
      "metadata": {
        "id": "1xP1DC2HTR7v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Outliers are the datapoints which are significantly different from the rest of the datapoints in the dataset. Outliers increase the variance in the dataset which inturn results in the decrease in the statistical power. So it is very important to identify these outliers and treat them accordingly.[18]"
      ],
      "metadata": {
        "id": "PM6Fd-4zTWjf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8k2wkbevQ0KQ"
      },
      "source": [
        "**Defination:**\n",
        "\n",
        "Outlier is an observation point that is distant from other observations.Extreme values can be present in both dependent & independent variables, in the case of supervised learning methods.\n",
        "\n",
        "These extreme values need not necessarily impact the model performance or accuracy, but when they do they are called “Influential” points. Many machine learning models, like linear & logistic regression, are easily impacted by the outliers in the training data.Models like AdaBoost increase the weights of misclassified points on every iteration and therefore might put high weights on these outliers as they tend to be often misclassified. This can become an issue if that outlier is an error of some type, or if we want our model to generalize well and not care for extreme values.To overcome this issue, we can either change the model or metric, or we can make some changes in the data and use the same models [5]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1tZVHeBnhIVH2LkvLXFBFRev7jJl7nelQ)"
      ],
      "metadata": {
        "id": "eVDaNYq8TqiA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1gQ0YliVHDL9QJLcEhSTDlGKwINGB7lYn)"
      ],
      "metadata": {
        "id": "d2zxKv1mSrYZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Reasons for the occurrence of Outliers**\n",
        "\n",
        "Outliers can occur because of various reasons. Some of the most common reasons include:[18]\n",
        "- Error in data entry.\n",
        "- Inappropriate scaling of datapoints.\n",
        "- Errors caused during measurement.\n",
        "- Existence of genuine extreme data points."
      ],
      "metadata": {
        "id": "wR7k9X-eLgwQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importance of Outlier Detection**[18]\n",
        "\n",
        "\n",
        "Now that we know the reasons for the occurrence of the outliers, it is also important to know why is it important to identify these outliers. Simple reason is that some of the measures of central tendency and measures of variability are affected by outliers.\n",
        "- Mean: Since mean is the average of all the values present in the dataset, it is affected by the presence of outliers. The mean shifts towards the outlier.\n",
        "- Median: Median is the middle value of the dataset and is not affected the presence of outliers. So we should use median instead of mean when we are dealing with the datasets consisting of outliers.\n",
        "- Mode: Mode is the value that occurs maximum number of times in the dataset and is not affected by the outliers.\n",
        "- Variance & Standard Deviation: Since mean is used to calculate both variance and standard deviation, both get affected by the outliers.\n",
        "- Range: Since range is the difference between the minimum and maximum datapoints, range gets most affected by the presence of outliers.\n",
        "\n",
        "Therefore except the median and mode, most of the other important measures get affected by the presence of outliers. Apart from these reasons, outliers also cause problems while fitting models and increase the errors. So it is very important to identify these outliers."
      ],
      "metadata": {
        "id": "weJWkErdMB90"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdlYsfevSLSv"
      },
      "source": [
        "**Extreme Values in Independent Variables [5]**\n",
        "\n",
        "These are called points of “high leverage”. With a single predictor, an extreme value is simply one that is particularly high or low. With multiple predictors, extreme values may be particularly high or low for one or more predictors (univariate analysis — analysis of one variable at a time) or may be “unusual” combinations of predictor values (multivariate analysis)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6u5JcpQSqPR"
      },
      "source": [
        "**Extreme Values in Target Variables [5]**\n",
        "\n",
        "Regression — these extreme values are termed as “outliers”. They may or may not be influential points\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6Fa2YQ2S62A"
      },
      "source": [
        "Classification: Here, we have two types of extreme values:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hj0_azsUTQRx"
      },
      "source": [
        "1. **Outliers:** For example, in an image classification problem in which we’re trying to identify dogs/cats, one of the images in the training set has a gorilla (or any other category not part of the goal of the problem) by mistake. Here, the gorilla image is clearly noise. Detecting outliers here does not make sense because we already know which categories we want to focus on and which to discard"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q-8ezNNKTZJ3"
      },
      "source": [
        "2. **Novelties:** Many times we’re dealing with novelties, and the problem is often called supervised anomaly detection. In this case, the goal is not to remove outliers or reduce their impact, but we are interested in detecting anomalies in new observations. Therefore we won’t be discussing it in this post. It is especially used for fraud detection in credit-card transactions, fake calls, etc."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ModLlb4mTucn"
      },
      "source": [
        "Our major focus will be outliers (extreme values in target variable for further investigation and treatment). We’ll see the impact of these extreme values on the model’s performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htiwFPisfBNg"
      },
      "source": [
        "## **9.1 Methods to identify and remover the Outlier [5]**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2o137YJKT7TI"
      },
      "source": [
        "**Common Methods for Detecting Outliers [5]**\n",
        "\n",
        "When detecting outliers, we are either doing univariate analysis or multivariate analysis. When your linear model has a single predictor, then you can use univariate analysis. However, it can give misleading results if you use it for multiple predictors. One common way of performing outlier detection is to assume that the regular data come from a known distribution (e.g. data are Gaussian distributed). This assumption is discussed in the Z-Score method section below."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualization**[18]\n",
        "\n",
        "Presence of outliers can also be detected by using various visualization methods. Some of the famous plots include:\n",
        "- Scatter plot\n",
        "- Box and Whisker plot\n",
        "- Histogram\n",
        "- Distribution Plot\n",
        "- QQ plot"
      ],
      "metadata": {
        "id": "qPa-Kfk9OrW_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1-Box-Plot**"
      ],
      "metadata": {
        "id": "1aZQQLVZaNwB"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xr8JhIW3UQ2Y"
      },
      "source": [
        "1. **Box-Plot**\n",
        "\n",
        "The quickest and easiest way to identify outliers is by visualizing them using plots. If your dataset is not huge (approx. up to 10k observations & 100 features), I would highly recommend you build scatter plots & box-plots of variables. If there aren’t outliers, you’ll definitely gain some other insights like correlations, variability, or external factors like the impact of world war/recession on economic factors. However, this method is not recommended for high dimensional data where the power of visualization fails.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuqTpjBEHYhn"
      },
      "source": [
        "To know the outliers in column features with the help of box plot visualization. For this, I create a for loop in which I used columns that have the type of int64 and float64. We can also observe outliers from the describe() function. If there are differences between the 75% to the maximum value and the difference between the minimum and 25% then there are outliers.[13]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSU43eKUUhD2"
      },
      "source": [
        "The box plot uses inter-quartile range to detect outliers.\n",
        "\n",
        " Here, we first determine the quartiles Q1 and Q3.\n",
        "\n",
        "Interquartile range is given by, IQR = Q3 — Q1\n",
        "\n",
        "Upper limit = Q3+1.5*IQR\n",
        "\n",
        "Lower limit = Q1–1.5*IQR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40KHu8jfVU-2"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1Co72C3c03NmxMSXaLVOKb6HDlr10X6Mf)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "Sk3CS4J9j6zP",
        "outputId": "312f7835-37d4-4c84-9a37-1159fe7cd789",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "Train_data = pd.read_csv('/content/drive/MyDrive/Datasets (1)/Student field Recommendation /Placement_Data_Full_Class.csv',encoding = 'latin')"
      ],
      "metadata": {
        "id": "4Dj4Vo_Bj_Jm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4eCL6XlWHdBe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a8243b47-aa35-4c56-8b44-8d7fd6cb841d"
      },
      "source": [
        "#[13]\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#outliers with boxplot\n",
        "for column in Train_data:\n",
        "    if Train_data [column].dtype in ['int64', 'float64']:\n",
        "        plt.figure()\n",
        "        Train_data.boxplot(column = [column])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tfLDiEbWIIv8"
      },
      "source": [
        "These are some box plots but in actual it will show all column features box plots. We also need to know if there are any outliers in the target column or not.[13]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Train_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "4uF25tv5mBjt",
        "outputId": "e22273b7-03f4-45a4-9eef-4559ddd2549f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sl_no gender  ssc_p    ssc_b  hsc_p    hsc_b     hsc_s  degree_p  \\\n",
              "0        1      M  67.00   Others  91.00   Others  Commerce     58.00   \n",
              "1        2      M  79.33  Central  78.33   Others   Science     77.48   \n",
              "2        3      M  65.00  Central  68.00  Central      Arts     64.00   \n",
              "3        4      M  56.00  Central  52.00  Central   Science     52.00   \n",
              "4        5      M  85.80  Central  73.60  Central  Commerce     73.30   \n",
              "..     ...    ...    ...      ...    ...      ...       ...       ...   \n",
              "210    211      M  80.60   Others  82.00   Others  Commerce     77.60   \n",
              "211    212      M  58.00   Others  60.00   Others   Science     72.00   \n",
              "212    213      M  67.00   Others  67.00   Others  Commerce     73.00   \n",
              "213    214      F  74.00   Others  66.00   Others  Commerce     58.00   \n",
              "214    215      M  62.00  Central  58.00   Others   Science     53.00   \n",
              "\n",
              "      degree_t workex  etest_p specialisation  mba_p      status    salary  \n",
              "0     Sci&Tech     No     55.0         Mkt&HR  58.80      Placed  270000.0  \n",
              "1     Sci&Tech    Yes     86.5        Mkt&Fin  66.28      Placed  200000.0  \n",
              "2    Comm&Mgmt     No     75.0        Mkt&Fin  57.80      Placed  250000.0  \n",
              "3     Sci&Tech     No     66.0         Mkt&HR  59.43  Not Placed       NaN  \n",
              "4    Comm&Mgmt     No     96.8        Mkt&Fin  55.50      Placed  425000.0  \n",
              "..         ...    ...      ...            ...    ...         ...       ...  \n",
              "210  Comm&Mgmt     No     91.0        Mkt&Fin  74.49      Placed  400000.0  \n",
              "211   Sci&Tech     No     74.0        Mkt&Fin  53.62      Placed  275000.0  \n",
              "212  Comm&Mgmt    Yes     59.0        Mkt&Fin  69.72      Placed  295000.0  \n",
              "213  Comm&Mgmt     No     70.0         Mkt&HR  60.23      Placed  204000.0  \n",
              "214  Comm&Mgmt     No     89.0         Mkt&HR  60.22  Not Placed       NaN  \n",
              "\n",
              "[215 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-532f50ac-fc9c-4f58-b146-b7f671e81dce\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sl_no</th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>M</td>\n",
              "      <td>79.33</td>\n",
              "      <td>Central</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>M</td>\n",
              "      <td>56.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>M</td>\n",
              "      <td>85.80</td>\n",
              "      <td>Central</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>211</td>\n",
              "      <td>M</td>\n",
              "      <td>80.60</td>\n",
              "      <td>Others</td>\n",
              "      <td>82.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>77.60</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>91.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>74.49</td>\n",
              "      <td>Placed</td>\n",
              "      <td>400000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>212</td>\n",
              "      <td>M</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>60.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>72.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>74.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>53.62</td>\n",
              "      <td>Placed</td>\n",
              "      <td>275000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>213</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>Yes</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>69.72</td>\n",
              "      <td>Placed</td>\n",
              "      <td>295000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>214</td>\n",
              "      <td>F</td>\n",
              "      <td>74.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>66.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>60.23</td>\n",
              "      <td>Placed</td>\n",
              "      <td>204000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>215</td>\n",
              "      <td>M</td>\n",
              "      <td>62.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>53.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>89.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>60.22</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>215 rows × 15 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  </button>\n",
              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ec57729-ef73-480b-9709-5494d96ae7d6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_14ec47dc-f75f-4ac5-a76b-6612367234bf\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('Train_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_14ec47dc-f75f-4ac5-a76b-6612367234bf button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('Train_data');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Train_data",
              "summary": "{\n  \"name\": \"Train_data\",\n  \"rows\": 215,\n  \"fields\": [\n    {\n      \"column\": \"sl_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62,\n        \"min\": 1,\n        \"max\": 215,\n        \"num_unique_values\": 215,\n        \"samples\": [\n          201,\n          213,\n          139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssc_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.827205398231452,\n        \"min\": 40.89,\n        \"max\": 89.4,\n        \"num_unique_values\": 103,\n        \"samples\": [\n          74.0,\n          73.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssc_b\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Central\",\n          \"Others\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsc_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.89750915750298,\n        \"min\": 37.0,\n        \"max\": 97.7,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          82.0,\n          73.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsc_b\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Central\",\n          \"Others\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsc_s\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Commerce\",\n          \"Science\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"degree_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.35874328733944,\n        \"min\": 50.0,\n        \"max\": 91.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          71.72,\n          76.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"degree_t\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Sci&Tech\",\n          \"Comm&Mgmt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"etest_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.275956401653833,\n        \"min\": 50.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          93.4,\n          69.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"specialisation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Mkt&Fin\",\n          \"Mkt&HR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mba_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.833384580683801,\n        \"min\": 51.21,\n        \"max\": 77.89,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          64.66,\n          52.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Placed\",\n          \"Placed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 93457.45241958875,\n        \"min\": 200000.0,\n        \"max\": 940000.0,\n        \"num_unique_values\": 45,\n        \"samples\": [\n          255000.0,\n          336000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "dDwU0YAJILPT",
        "outputId": "76b914ea-4e17-45cb-bde6-65b18b6872ea"
      },
      "source": [
        "Train_data.boxplot(column = ['salary'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GpsOlGI0IiIZ"
      },
      "source": [
        "After checking all these, we don’t need to take all the features as the independent variable and the price will be the target variable. So, we will choose some features that are most important for house prediction and making new data for our model.[13]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xiSoTDtMVj-s"
      },
      "source": [
        "2. **Cook’s Distance**\n",
        "\n",
        "This is a multivariate approach for finding influential points. These points may or may not be outliers as explained above, but they have the power to influence the regression model. We will see their impact in the later part of the blog.\n",
        "This method is used only for linear regression and therefore has a limited application. Cook’s distance measures the effect of deleting a given observation. It’s represents the sum of all the changes in the regression model when observation “i” is removed from it.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2-Z-Score**"
      ],
      "metadata": {
        "id": "TAEcZWasaZ0Z"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LFq1ZvGiVr88"
      },
      "source": [
        "3. **Z-Score**\n",
        "\n",
        "This method assumes that the variable has a Gaussian distribution. It represents the number of standard deviations an observation is away from the mean:\n",
        "\n",
        "Here, we normally define outliers as points whose modulus of z-score is greater than a threshold value. This threshold value is usually greater than 2 (3 is a common value).\n",
        "\n",
        "All the above methods are good for initial analysis of data, but they don’t have much value in multivariate settings or with high dimensional data. For such datasets, we have to use advanced methods like PCA, LOF (Local Outlier Factor) & HiCS: High Contrast Subspaces for Density-Based Outlier Ranking.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Z-score tells us how many standard deviations above or below the mean a datapoint lies. It assumes that the datapoints follow a gaussian distribution[18]"
      ],
      "metadata": {
        "id": "6VP-j-nZOMy4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "-Z-Score = (X-mean) / Standard deviation"
      ],
      "metadata": {
        "id": "Qdr8K8AUOVaP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above normal distribtion figure we can we that:\n",
        "- 68% of the data lies within 1 standard deviation.\n",
        "- 95% of the data lies within 2 standard deviations.\n",
        "- 99.7% of the data lies within 3 standard deviations.\n",
        "\n",
        "Since majority of the datapoints (99.7%) lie within 3 standard deviations above or below the mean, any Z-score more than +3 or any Z-score less than -3 is considered as an outlier.[18]"
      ],
      "metadata": {
        "id": "3VSukzEnOaAX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NL0Q2JbrWw5s"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1E2H2NHulGaphunf9oVV_mVRCbgoS1I3e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7gu5ktGW74j"
      },
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1SQkD3XMh-VTNY2fe-mcDaVepK1IJNaP7)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](\n",
        "https://drive.google.com/uc?export=view&id=1GxzFoNg1XpORg9O8c46TvFvFsQ4WT3_e)"
      ],
      "metadata": {
        "id": "1JV1BQRdR_Mg"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tq6yjKx4jg8"
      },
      "source": [
        "Outliers can be dangerous as they can skew your model and give you predictions that are biased and erroneous.\n",
        "The best way to find outliers is to use the describe function and look at information such as maximum and mean."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier Dectection with z-score**"
      ],
      "metadata": {
        "id": "IA7C6KlpUEF-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import zscore\n",
        "# Calculate Z-scores for each data point\n",
        "z_scores = zscore(df)\n",
        "# Define a threshold for identifying outliers\n",
        "threshold = 3\n",
        "# Identify outliers\n",
        "outliers = (z_scores > threshold) | (z_scores < -threshold)"
      ],
      "metadata": {
        "id": "oiXI0M8jTnWN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outliers"
      ],
      "metadata": {
        "id": "-h13B1VZT2fb",
        "outputId": "ca7ac4d6-9076-4b60-f092-7a274fb1ddb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      cgpa  placement_exam_marks  placed\n",
              "0    False                 False   False\n",
              "1    False                 False   False\n",
              "2    False                 False   False\n",
              "3    False                 False   False\n",
              "4    False                 False   False\n",
              "..     ...                   ...     ...\n",
              "995   True                 False   False\n",
              "996   True                 False   False\n",
              "997   True                 False   False\n",
              "998  False                 False   False\n",
              "999   True                 False   False\n",
              "\n",
              "[1000 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b5e707a9-803c-400b-8a49-8efd2ebc76a5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5e707a9-803c-400b-8a49-8efd2ebc76a5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b5e707a9-803c-400b-8a49-8efd2ebc76a5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b5e707a9-803c-400b-8a49-8efd2ebc76a5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6c2eea5b-db56-479b-8bc9-3ade6f46209f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c2eea5b-db56-479b-8bc9-3ade6f46209f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6c2eea5b-db56-479b-8bc9-3ade6f46209f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f2900e3f-e943-4003-b8ff-e6640668e0b3\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('outliers')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
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              "      (() => {\n",
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              "        document.querySelector('#id_f2900e3f-e943-4003-b8ff-e6640668e0b3 button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('outliers');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "outliers",
              "summary": "{\n  \"name\": \"outliers\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placement_exam_marks\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Outlier removeal with z-score**"
      ],
      "metadata": {
        "id": "tvH8XUvJUJsc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "df = pd.read_csv('/content/drive/MyDrive/Courses /Data Science /Machine Learning/Python /Supervised Learning with scikit-learn/Dataset/placement_dataset.csv',encoding = 'latin')"
      ],
      "metadata": {
        "id": "B_Qx7igs6zgL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eF52Kqk97FGh",
        "outputId": "cc06332c-f5ce-4cec-b131-6d344398ad1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   cgpa  placement_exam_marks  placed\n",
              "0  7.19                  26.0       1\n",
              "1  7.46                  38.0       1\n",
              "2  7.54                  40.0       1\n",
              "3  6.42                   8.0       1\n",
              "4  7.23                  17.0       0"
            ],
            "text/html": [
              "\n",
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              "      <th>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.19</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e52ee7ef-86a0-49d5-be74-0f00a99eaec6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6158978751323896,\n        \"min\": 4.89,\n        \"max\": 9.12,\n        \"num_unique_values\": 261,\n        \"samples\": [\n          8.15,\n          7.03,\n          5.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placement_exam_marks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.13082233892112,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          6.0,\n          93.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ft6zgav37S8H",
        "outputId": "b186168a-7bb4-403b-9044-90c99bd28667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 3 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   cgpa                  1000 non-null   float64\n",
            " 1   placement_exam_marks  1000 non-null   float64\n",
            " 2   placed                1000 non-null   int64  \n",
            "dtypes: float64(2), int64(1)\n",
            "memory usage: 23.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36x4E42l7NWp",
        "outputId": "7ef98fd9-aab4-4338-be06-81e739eb93e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yZzB0q6v7kGu",
        "outputId": "4f3dd2e6-79bb-466a-e8af-d4a46e5d10c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed\n",
              "0    7.19                  26.0       1\n",
              "1    7.46                  38.0       1\n",
              "2    7.54                  40.0       1\n",
              "3    6.42                   8.0       1\n",
              "4    7.23                  17.0       0\n",
              "..    ...                   ...     ...\n",
              "995  8.87                  44.0       1\n",
              "996  9.12                  65.0       1\n",
              "997  4.89                  34.0       0\n",
              "998  8.62                  46.0       1\n",
              "999  4.90                  10.0       1\n",
              "\n",
              "[1000 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eba5d326-395e-4529-99bf-f3ee512126c2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.19</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.46</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.54</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.42</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.23</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>8.87</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>9.12</td>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>4.89</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>8.62</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>4.90</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eba5d326-395e-4529-99bf-f3ee512126c2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eba5d326-395e-4529-99bf-f3ee512126c2 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-eba5d326-395e-4529-99bf-f3ee512126c2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5c0ae380-4e7c-4e3c-86a3-f28143b1fe63\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5c0ae380-4e7c-4e3c-86a3-f28143b1fe63')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5c0ae380-4e7c-4e3c-86a3-f28143b1fe63 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c6fe746f-3405-4d35-9997-f8e743796925\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c6fe746f-3405-4d35-9997-f8e743796925 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6158978751323896,\n        \"min\": 4.89,\n        \"max\": 9.12,\n        \"num_unique_values\": 261,\n        \"samples\": [\n          8.15,\n          7.03,\n          5.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placement_exam_marks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.13082233892112,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          6.0,\n          93.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "CeMEJNaN7_jU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.subplot(1,2,1)\n",
        "sns.distplot(df['cgpa'])\n",
        "plt.subplot(1,2,2)\n",
        "sns.distplot(df['placement_exam_marks'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "pLIj6YFB7eqP",
        "outputId": "4a42aebb-cd47-4c66-ab6e-a38b83daa9de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='placement_exam_marks', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mean value of CGPA {}'.format(df['cgpa'].mean()))\n",
        "print('Min value of CGPA {}'.format(df['cgpa'].min()))\n",
        "print('Max value of CGPA {}'.format(df['cgpa'].max()))\n",
        "print('Standard deviation value of CGPA {}'.format(round(df['cgpa'].std(),2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDdD4MR3-vZD",
        "outputId": "d839029f-252e-4efd-a169-29ee0995acd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean value of CGPA 6.96124\n",
            "Min value of CGPA 4.89\n",
            "Max value of CGPA 9.12\n",
            "Standard deviation value of CGPA 0.62\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the boundary values are:\n",
        "print('Highest value of cgpa', round(df['cgpa'].mean()+3*df['cgpa'].std(),3))\n",
        "print('Lowest value of cgpa', round(df['cgpa'].mean()-3*df['cgpa'].std(),3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_SAHG9i-1Ja",
        "outputId": "936096dd-5a89-4a10-ae81-313553a53b90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highest value of cgpa 8.809\n",
            "Lowest value of cgpa 5.114\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['cgpa']>8.80)|(df['cgpa']<5.11)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hQjRdVX7_S2q",
        "outputId": "4f114718-5d1c-439a-d8a4-b45b9256392e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed\n",
              "485  4.92                  44.0       1\n",
              "995  8.87                  44.0       1\n",
              "996  9.12                  65.0       1\n",
              "997  4.89                  34.0       0\n",
              "999  4.90                  10.0       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-abc8225b-04b6-44b6-9512-4d6bd20730f4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>4.92</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>8.87</td>\n",
              "      <td>44.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>9.12</td>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>4.89</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>4.90</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abc8225b-04b6-44b6-9512-4d6bd20730f4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-abc8225b-04b6-44b6-9512-4d6bd20730f4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-abc8225b-04b6-44b6-9512-4d6bd20730f4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-882decdb-c26c-4454-a005-6f95b16a635b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-882decdb-c26c-4454-a005-6f95b16a635b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-882decdb-c26c-4454-a005-6f95b16a635b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[(df['cgpa']>8\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.242866469498351,\n        \"min\": 4.89,\n        \"max\": 9.12,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.87,\n          4.9,\n          9.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placement_exam_marks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.944924166313594,\n        \"min\": 10.0,\n        \"max\": 65.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          65.0,\n          10.0,\n          44.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cgpa_with_zscore'] = (df['cgpa']-df['cgpa'].mean())/(df['cgpa'].std())\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nhEJYZV9__n5",
        "outputId": "3769228b-1ede-4974-849d-dd58f12376e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   cgpa  placement_exam_marks  placed  cgpa_with_zscore\n",
              "0  7.19                  26.0       1          0.371425\n",
              "1  7.46                  38.0       1          0.809810\n",
              "2  7.54                  40.0       1          0.939701\n",
              "3  6.42                   8.0       1         -0.878782\n",
              "4  7.23                  17.0       0          0.436371"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64214ccf-e3af-4e11-b23f-cb2561ba479e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "      <th>cgpa_with_zscore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.19</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.371425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.46</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.809810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.54</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.939701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.42</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.878782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.23</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.436371</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64214ccf-e3af-4e11-b23f-cb2561ba479e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-64214ccf-e3af-4e11-b23f-cb2561ba479e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-64214ccf-e3af-4e11-b23f-cb2561ba479e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-356b124d-83fe-4a9f-8632-b2f2910d4ab8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-356b124d-83fe-4a9f-8632-b2f2910d4ab8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "    })();\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6158978751323896,\n        \"min\": 4.89,\n        \"max\": 9.12,\n        \"num_unique_values\": 261,\n        \"samples\": [\n          8.15,\n          7.03,\n          5.82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placement_exam_marks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.13082233892112,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          6.0,\n          93.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cgpa_with_zscore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0,\n        \"min\": -3.3629601328869327,\n        \"max\": 3.5050616135604713,\n        \"num_unique_values\": 261,\n        \"samples\": [\n          1.9301251847061038,\n          0.11164188541033679\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[(df['cgpa_with_zscore']<-3)|(df['cgpa_with_zscore']>3)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2o432k_6AGrg",
        "outputId": "4abd4094-1068-49fa-abf9-57843dbd969a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed  cgpa_with_zscore\n",
              "485  4.92                  44.0       1         -3.314251\n",
              "995  8.87                  44.0       1          3.099150\n",
              "996  9.12                  65.0       1          3.505062\n",
              "997  4.89                  34.0       0         -3.362960\n",
              "999  4.90                  10.0       1         -3.346724"
            ],
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              "\n",
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              "  }\n",
              "\n",
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              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[(df['cgpa_with_zscore']<-3)|(df['cgpa_with_zscore']>3)]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.242866469498351,\n        \"min\": 4.89,\n        \"max\": 9.12,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.87,\n          4.9,\n          9.12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placement_exam_marks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.944924166313594,\n        \"min\": 10.0,\n        \"max\": 65.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          65.0,\n          10.0,\n          44.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cgpa_with_zscore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.641620729761794,\n        \"min\": -3.3629601328869327,\n        \"max\": 3.5050616135604713,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.099150162824809,\n          -3.3467236748575053\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. IQR Method**"
      ],
      "metadata": {
        "id": "F8aIWuRlM8_y"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FseYShGQwc1n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3afbe738-b060-42da-f2c9-86f33a56bf75"
      },
      "source": [
        "# Detecting outliers with IQR\n",
        "Q1 = Train_data.quantile(0.25)\n",
        "Q3 = Train_data.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "print(IQR)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sl_no         107.00\n",
            "ssc_p          15.10\n",
            "hsc_p          12.10\n",
            "degree_p       11.00\n",
            "etest_p        23.50\n",
            "mba_p           8.31\n",
            "salary      60000.00\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLYT9hGawiaP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12f91ca5-e317-4372-ea7e-50635317fcb4"
      },
      "source": [
        "# Removing outliers from the dataset\n",
        "Train_data = Train_data[~((Train_data < (Q1 - 1.5 * IQR)) |(Train_data > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "Train_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(192, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iQL0D8K24wse"
      },
      "source": [
        "You can also plot a histogram and look at the distribution of your data.\n",
        "\n",
        "In this histogram, you can see that most of the data is around 0 to 5000."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "c34xlum141Cl",
        "outputId": "7ac89b06-72b1-4bda-feb9-eafac6232c54"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "Train_data['salary'].hist(bins=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqyFTeET5TZu"
      },
      "source": [
        "A boxplot is also useful in detecting outliers.\n",
        "\n",
        "As you can see, the price column has multiple data points that are outliers (above of the maximum in the boxplot)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MhlqVJ75ZEF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "8ce3a942-20b7-4738-80a3-a410ceeca171"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "Train_data.boxplot(column=['salary'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qx-t0PnF527E"
      },
      "source": [
        "For categorical data, you can plot a bar chart to see whether a particular category to view the count of the categories.\n",
        "\n",
        "Outliers in categorical data is tricky, because you have to determine whether it’s appropriate to call it an outlier based on context.\n",
        "\n",
        "Some outliers are more obvious. Let’s say there’s an experiment done where 1000 people choose between a glass of water and a glass of milk. If the final result is 1 person who chose a glass of water, and 999 people choosing a glass of milk, that 1 person can be considered an outlier.\n",
        "\n",
        "However, in some cases, outliers depend on context. In my example, you see that Manhattan and Brooklyn has significantly more data than Staten Island. This doesn’t count as an outlier, since Manhattan and Brooklyn has a higher housing density as compared to Staten Island."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "7SU6nEUR6H83",
        "outputId": "3f3cd45d-5d7f-4eac-809e-de0722591ae0"
      },
      "source": [
        "Train_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sl_no gender  ssc_p    ssc_b  hsc_p    hsc_b     hsc_s  degree_p  \\\n",
              "0        1      M  67.00   Others  91.00   Others  Commerce     58.00   \n",
              "1        2      M  79.33  Central  78.33   Others   Science     77.48   \n",
              "2        3      M  65.00  Central  68.00  Central      Arts     64.00   \n",
              "3        4      M  56.00  Central  52.00  Central   Science     52.00   \n",
              "5        6      M  55.00   Others  49.80   Others   Science     67.25   \n",
              "..     ...    ...    ...      ...    ...      ...       ...       ...   \n",
              "209    210      M  62.00  Central  72.00  Central  Commerce     65.00   \n",
              "211    212      M  58.00   Others  60.00   Others   Science     72.00   \n",
              "212    213      M  67.00   Others  67.00   Others  Commerce     73.00   \n",
              "213    214      F  74.00   Others  66.00   Others  Commerce     58.00   \n",
              "214    215      M  62.00  Central  58.00   Others   Science     53.00   \n",
              "\n",
              "      degree_t workex  etest_p specialisation  mba_p      status    salary  \n",
              "0     Sci&Tech     No     55.0         Mkt&HR  58.80      Placed  270000.0  \n",
              "1     Sci&Tech    Yes     86.5        Mkt&Fin  66.28      Placed  200000.0  \n",
              "2    Comm&Mgmt     No     75.0        Mkt&Fin  57.80      Placed  250000.0  \n",
              "3     Sci&Tech     No     66.0         Mkt&HR  59.43  Not Placed       NaN  \n",
              "5     Sci&Tech    Yes     55.0        Mkt&Fin  51.58  Not Placed       NaN  \n",
              "..         ...    ...      ...            ...    ...         ...       ...  \n",
              "209  Comm&Mgmt     No     67.0        Mkt&Fin  56.49      Placed  216000.0  \n",
              "211   Sci&Tech     No     74.0        Mkt&Fin  53.62      Placed  275000.0  \n",
              "212  Comm&Mgmt    Yes     59.0        Mkt&Fin  69.72      Placed  295000.0  \n",
              "213  Comm&Mgmt     No     70.0         Mkt&HR  60.23      Placed  204000.0  \n",
              "214  Comm&Mgmt     No     89.0         Mkt&HR  60.22  Not Placed       NaN  \n",
              "\n",
              "[192 rows x 15 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-06444a38-5b55-4c51-a5a3-03391daa3223\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sl_no</th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>M</td>\n",
              "      <td>79.33</td>\n",
              "      <td>Central</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>M</td>\n",
              "      <td>56.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>52.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>59.43</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>M</td>\n",
              "      <td>55.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>49.80</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>67.25</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>51.58</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>210</td>\n",
              "      <td>M</td>\n",
              "      <td>62.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>72.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>56.49</td>\n",
              "      <td>Placed</td>\n",
              "      <td>216000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>212</td>\n",
              "      <td>M</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>60.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>72.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>74.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>53.62</td>\n",
              "      <td>Placed</td>\n",
              "      <td>275000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>213</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>Yes</td>\n",
              "      <td>59.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>69.72</td>\n",
              "      <td>Placed</td>\n",
              "      <td>295000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>214</td>\n",
              "      <td>F</td>\n",
              "      <td>74.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>66.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>60.23</td>\n",
              "      <td>Placed</td>\n",
              "      <td>204000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>215</td>\n",
              "      <td>M</td>\n",
              "      <td>62.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>53.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>89.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>60.22</td>\n",
              "      <td>Not Placed</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>192 rows × 15 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06444a38-5b55-4c51-a5a3-03391daa3223')\"\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-06444a38-5b55-4c51-a5a3-03391daa3223 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-06444a38-5b55-4c51-a5a3-03391daa3223');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9247025f-f612-43a4-b03e-8fec734cd17c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_43de8690-4a02-4ca0-a0aa-d0f40b965bd8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('Train_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_43de8690-4a02-4ca0-a0aa-d0f40b965bd8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('Train_data');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Train_data",
              "summary": "{\n  \"name\": \"Train_data\",\n  \"rows\": 192,\n  \"fields\": [\n    {\n      \"column\": \"sl_no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62,\n        \"min\": 1,\n        \"max\": 215,\n        \"num_unique_values\": 192,\n        \"samples\": [\n          52,\n          153,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssc_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.483977781122688,\n        \"min\": 40.89,\n        \"max\": 88.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          84.86,\n          66.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssc_b\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Central\",\n          \"Others\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsc_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.582074535137163,\n        \"min\": 44.0,\n        \"max\": 91.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          87.6,\n          91.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsc_b\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Central\",\n          \"Others\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hsc_s\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Commerce\",\n          \"Science\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"degree_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.001789837362479,\n        \"min\": 50.0,\n        \"max\": 84.0,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          66.89,\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"degree_t\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Sci&Tech\",\n          \"Comm&Mgmt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"etest_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.873821015505342,\n        \"min\": 50.0,\n        \"max\": 98.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          63.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"specialisation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Mkt&Fin\",\n          \"Mkt&HR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mba_p\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.578812005017329,\n        \"min\": 51.29,\n        \"max\": 77.89,\n        \"num_unique_values\": 182,\n        \"samples\": [\n          56.7,\n          62.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Placed\",\n          \"Placed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40618.970631883305,\n        \"min\": 200000.0,\n        \"max\": 380000.0,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          276000.0,\n          278000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cbqHnJMEnCzK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hObhvoi6AUb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "outputId": "e2aef644-549e-47cb-eb9b-1c5f26553b9c"
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "Train_data['salary'].value_counts().plot.bar()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inter Quartile Range (IQR) is the middle 50% of the dataset. In other words, it is the difference between the third quartile(75th percentile) and first quartile(25th percentile) value of the dataset.\n",
        "\n",
        "- IQR = Q3 -Q1\n",
        "- Lower Bound = Q1–1.5*IQR\n",
        "- Upper Bound = Q3+1.5*IQR\n",
        "The IQR finds the lower and upper bound to identify the outliers. Any value which is 1.5 times above or below these thresholds is identified as an outlier [18]"
      ],
      "metadata": {
        "id": "8yrjC6zVNCk6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1NfCaTr8uAB_e7EtnO0H5N8EAkSl9m-Ke)"
      ],
      "metadata": {
        "id": "IjmzKuTpNPZ6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **3-Isolation Forest:**"
      ],
      "metadata": {
        "id": "Dhv3mu-fBe2b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "# Create an Isolation Forest model\n",
        "iso_forest = IsolationForest(contamination=0.05) # Adjust contamination based on your dataset\n",
        "# Fit the model to your data\n",
        "iso_forest.fit(df)\n",
        "# Predict outliers\n",
        "outliers = iso_forest.predict(df)\n"
      ],
      "metadata": {
        "id": "gGVMYOk5Brht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load your dataset (replace 'your_data.csv' with your file)\n",
        "data = pd.read_csv('/content/drive/MyDrive/Courses /Data Science /Machine Learning/Python /Supervised Learning with scikit-learn/Dataset/placement_dataset.csv',encoding = 'latin')\n",
        "\n",
        "# Drop non-numeric columns or handle them appropriately\n",
        "numeric_data = data.select_dtypes(include=[np.number])\n"
      ],
      "metadata": {
        "id": "flL247FRCqPt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLLXOP0GB_Zl",
        "outputId": "80eb18ba-d9e0-4f36-8d7c-c2a282cfb163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)**\n",
        "\n",
        "DBSCAN is a clustering method that is used to separate clusters of high denstiy from the clusters of low density. It basically divides the datapoints into Core Points, Border Points or Noise Points. Here Noise points are are the outliers.[18]"
      ],
      "metadata": {
        "id": "9XAI544_O-jN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1qf6z8JdPyAK7-EqLqnNHiGkvyMlnRj0R)"
      ],
      "metadata": {
        "id": "GA5P0wnCRtT6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import Libraries:\n",
        "\n",
        "Start by importing the necessary libraries."
      ],
      "metadata": {
        "id": "b1HXf4IRHU0n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "Ca3VrXeJGb85"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Data:\n",
        "\n",
        "Load your dataset and preprocess it if necessary. Ensure that the data is numeric, as DBSCAN relies on distance measures."
      ],
      "metadata": {
        "id": "eWKcLE35HbAg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load your dataset (replace 'your_data.csv' with your file)\n",
        "data = pd.read_csv('/content/drive/MyDrive/Courses /Data Science /Machine Learning/Python /Supervised Learning with scikit-learn/Dataset/placement_dataset.csv',encoding = 'latin')\n",
        "\n",
        "# Drop non-numeric columns or handle them appropriately\n",
        "numeric_data = data.select_dtypes(include=[np.number])\n"
      ],
      "metadata": {
        "id": "BOTPVsHoGfwH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standardize Data:\n",
        "\n",
        "It's often a good idea to standardize the data to bring all features to the same scale."
      ],
      "metadata": {
        "id": "Z4EsFJwlHgQ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaled_data = scaler.fit_transform(numeric_data)\n"
      ],
      "metadata": {
        "id": "ClrLox70G6zm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adjust the parameters (eps and min_samples) based on your data characteristics\n",
        "eps = 0.5  # maximum distance between two samples for one to be considered as in the neighborhood of the other\n",
        "min_samples = 5  # minimum number of samples in a neighborhood for a data point to be considered a core point\n",
        "\n",
        "dbscan_model = DBSCAN(eps=eps, min_samples=min_samples)\n",
        "dbscan_labels = dbscan_model.fit_predict(scaled_data)\n"
      ],
      "metadata": {
        "id": "rLhOpoaMG_Tf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outliers_mask = dbscan_labels == -1\n",
        "outliers = data[outliers_mask]\n"
      ],
      "metadata": {
        "id": "i4OHMVK0HEVH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization (example for 2D data, modify for 3D)\n",
        "plt.scatter(scaled_data[:, 0], scaled_data[:, 1], c=dbscan_labels, cmap='viridis', marker='o', s=10)\n",
        "plt.title('DBSCAN Clustering')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "c-7qCrOoHIZv",
        "outputId": "a709ef98-7986-4fad-ce04-9e00e0e383b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **4-Outlier Treatment**"
      ],
      "metadata": {
        "id": "zkZjO5MSWNJV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Trimming"
      ],
      "metadata": {
        "id": "nYMnzzuwWdso"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_new = df[(df['cgpa']>5.11) & (df['cgpa']<8.80)]\n"
      ],
      "metadata": {
        "id": "d_GsQtl6WZjy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_new"
      ],
      "metadata": {
        "id": "QybKptN6W58f",
        "outputId": "59ab784d-e514-4b38-af9e-d345e20702bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     cgpa  placement_exam_marks  placed\n",
              "0    7.19                  26.0       1\n",
              "1    7.46                  38.0       1\n",
              "2    7.54                  40.0       1\n",
              "3    6.42                   8.0       1\n",
              "4    7.23                  17.0       0\n",
              "..    ...                   ...     ...\n",
              "991  7.04                  57.0       0\n",
              "992  6.26                  12.0       0\n",
              "993  6.73                  21.0       1\n",
              "994  6.48                  63.0       0\n",
              "998  8.62                  46.0       1\n",
              "\n",
              "[995 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c61a5148-36fe-4508-9e64-898a69ba93df\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cgpa</th>\n",
              "      <th>placement_exam_marks</th>\n",
              "      <th>placed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.19</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.46</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.54</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.42</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.23</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>991</th>\n",
              "      <td>7.04</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>992</th>\n",
              "      <td>6.26</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>993</th>\n",
              "      <td>6.73</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>994</th>\n",
              "      <td>6.48</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>8.62</td>\n",
              "      <td>46.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>995 rows × 3 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_new')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_new",
              "summary": "{\n  \"name\": \"df_new\",\n  \"rows\": 995,\n  \"fields\": [\n    {\n      \"column\": \"cgpa\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.600081641278003,\n        \"min\": 5.23,\n        \"max\": 8.62,\n        \"num_unique_values\": 256,\n        \"samples\": [\n          6.14,\n          6.69,\n          7.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placement_exam_marks\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19.130297648512528,\n        \"min\": 0.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          6.0,\n          93.0,\n          55.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"placed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Capping**"
      ],
      "metadata": {
        "id": "q4Mt6IVvZ6WJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "upper_limit = 8.8\n",
        "lower_limit = 5.11\n",
        "\n",
        "df['cgpa'] = np.where(df['cgpa']>upper_limit, upper_limit , df['cgpa'])\n",
        "df['cgpa'] = np.where(df['cgpa']<lower_limit , lower_limit , df['cgpa'])\n",
        "df['cgpa'].min()"
      ],
      "metadata": {
        "id": "xlUzmgNUZ3k_",
        "outputId": "1cae38e7-6039-44fa-8bcb-d9bd2843b7f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.11"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['cgpa'].max()"
      ],
      "metadata": {
        "id": "gOrejS2taC-V",
        "outputId": "08bd7cf1-48b7-4a59-b415-1fc82dbde252",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.8"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **5-Hypothesis Testing**"
      ],
      "metadata": {
        "id": "0Aljqd8PazpJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hypothesis Testing** [18]\n",
        "\n",
        "We can also use hypothesis testing to identify the outliers in the dataset. Some of the famous hypothesis tests for outlier detection include:\n",
        "- Grubbs’ test\n",
        "- Chi –square test\n",
        "- Dixon’s Q test"
      ],
      "metadata": {
        "id": "WS2tIUWzSi9g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each of these above mentioned tests use different methods to identify outliers. In Grubb’s test, we assume that:\n",
        "- Null hypothesis: All datapoints in the sample were drawn from a single population that follows the same normal distribution.\n",
        "- Alternate hypothesis: One datapoint in the sample was not drawn from the same normally distributed population as the other datapoints.\n",
        "- If the p-value is less than the significance level, we can reject the null hypothesis and conclude that one of the values is an outlier."
      ],
      "metadata": {
        "id": "RUtPUk7wSuhf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fOVRcZP6aTT"
      },
      "source": [
        "**Dealing with outliers**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So now that we know how and why to detect outliers, the next question that arises is that what to do with these outliers.\n",
        "One simple way is to just drop the outlier, but this is not correct for all scenarios. based on the use case we need to decide whether to drop an outlier or not.[18]\n"
      ],
      "metadata": {
        "id": "ghhnkz2gUuBX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZfdOd6I6ccB"
      },
      "source": [
        "Dealing with outliers is similar to removing missing values, the only difference is the way you find outliers.\n",
        "\n",
        "To categorize numerical values as outliers, there are statistical techniques like using the standard deviation and the Interquartile range. You can refer to this article for code examples to do that.\n",
        "\n",
        "For categorical values, if they have very low frequency (like Staten island in the example above), it still may become a problem for your model. Depending on context and nature of the data, you can choose to group them into one category, for example “Others”. This way your model will be less biased and you’re not losing any information."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**When to drop an outlier?**[18]\n",
        "\n",
        "- When we know for sure that the outlier is completely wrong.\n",
        "- When we have large amount of data.\n",
        "- When we can revert back to the original data, if in later stage we find that the dropping of the outlier wasn’t a good idea."
      ],
      "metadata": {
        "id": "b8S-YwBRU29K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**When not to drop an outlier?**[18]\n",
        "\n",
        "- When there a lot of outliers.\n",
        "- When dealing with a sensitive critical use case."
      ],
      "metadata": {
        "id": "Y7bAfCPEVHZX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What to do with the undroppable outliers?**[18]\n",
        "\n",
        "- Impuation: We can replace the outlier values with the mean, median or mode value based on the use case.\n",
        "- Quantile-based Flooring and Capping: In this technique,we can do the flooring (e.g., replacing with the 10th percentile) for the lower values and capping (e.g.,replacing with the 90th percentile) for the higher values."
      ],
      "metadata": {
        "id": "lK7qPLVUVQKc"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAFiOSxhxeYd"
      },
      "source": [
        "# **📚Section 7-Data spliting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHMtSzwOGSWe"
      },
      "source": [
        "![](https://drive.google.com/uc?export=view&id=168Enbhun0wZ6d9cCr6ef_zUz-P5m3L9v)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **7.1-Train-Test Split(Simple Random Split)**"
      ],
      "metadata": {
        "id": "P0PCrA0xv8O3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "j7Z9xW49wiG4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate some example data\n",
        "X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])  # Features\n",
        "y = np.array([0, 1, 0, 1])  # Target variable"
      ],
      "metadata": {
        "id": "raEXII0uwqGu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Print the shapes of the resulting datasets\n",
        "print(\"Shape of X_train:\", X_train.shape)\n",
        "print(\"Shape of X_test:\", X_test.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1p9pcMQGwvLG",
        "outputId": "2d95782c-aa85-4f7e-d2f2-a67495f45fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (3, 2)\n",
            "Shape of X_test: (1, 2)\n",
            "Shape of y_train: (3,)\n",
            "Shape of y_test: (1,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.2. Stratified Sampling:**"
      ],
      "metadata": {
        "id": "tSIrxiT-xdp7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "import numpy as np\n",
        "\n",
        "# Generate some example data\n",
        "X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])  # Features\n",
        "y = np.array([0, 1, 0, 1, 1])  # Target variable\n",
        "\n",
        "# Create an instance of StratifiedShuffleSplit\n",
        "sss = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=42)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "for train_index, test_index in sss.split(X, y):\n",
        "    X_train, X_test = X[train_index], X[test_index]\n",
        "    y_train, y_test = y[train_index], y[test_index]\n",
        "\n",
        "# Print the shapes of the resulting datasets\n",
        "print(\"Shape of X_train:\", X_train.shape)\n",
        "print(\"Shape of X_test:\", X_test.shape)\n",
        "print(\"Shape of y_train:\", y_train.shape)\n",
        "print(\"Shape of y_test:\", y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMitqTsIxrbY",
        "outputId": "6cc86aa4-0a99-47a1-fe77-711b9fd38ed9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (3, 2)\n",
            "Shape of X_test: (2, 2)\n",
            "Shape of y_train: (3,)\n",
            "Shape of y_test: (2,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.3- K-fold cross-validation**"
      ],
      "metadata": {
        "id": "40b5ppo5yn-L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "import numpy as np\n",
        "\n",
        "# Generate some example data\n",
        "X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])  # Features\n",
        "y = np.array([0, 1, 0, 1, 1])  # Target variable"
      ],
      "metadata": {
        "id": "d3I2sA-Gy67G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import KFold, cross_val_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "model = RandomForestClassifier(random_state=42)\n",
        "kf = KFold(n_splits=5, random_state=42, shuffle=True)\n",
        "scores = cross_val_score(model, X, y, cv=kf)\n",
        "print(\"Cross-validation scores:\", scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7k7kibFy_Xe",
        "outputId": "6935faf9-ce07-483d-9e60-37447bce3be9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validation scores: [0. 1. 0. 0. 0.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.4-Stratified K-Fold Cross-Validation**"
      ],
      "metadata": {
        "id": "_SEgCJ3mzhy1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "import numpy as np\n",
        "\n",
        "# Generate some example data\n",
        "X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])  # Features\n",
        "y = np.array([0, 1, 0, 1, 1])  # Target variable\n",
        "\n",
        "# Create an instance of StratifiedKFold\n",
        "skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)\n",
        "\n",
        "# Split the data into training and testing sets using cross-validation\n",
        "for train_index, test_index in skf.split(X, y):\n",
        "    X_train, X_test = X[train_index], X[test_index]\n",
        "    y_train, y_test = y[train_index], y[test_index]\n",
        "\n",
        "    # Print the shapes of the resulting datasets for each fold\n",
        "    print(\"Shape of X_train:\", X_train.shape)\n",
        "    print(\"Shape of X_test:\", X_test.shape)\n",
        "    print(\"Shape of y_train:\", y_train.shape)\n",
        "    print(\"Shape of y_test:\", y_test.shape)\n",
        "    print()  # For separating fold outputs\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X37ECA5D1Lnv",
        "outputId": "1411aaaa-ba99-4be1-eb3b-06338c4491f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (3, 2)\n",
            "Shape of X_test: (2, 2)\n",
            "Shape of y_train: (3,)\n",
            "Shape of y_test: (2,)\n",
            "\n",
            "Shape of X_train: (3, 2)\n",
            "Shape of X_test: (2, 2)\n",
            "Shape of y_train: (3,)\n",
            "Shape of y_test: (2,)\n",
            "\n",
            "Shape of X_train: (4, 2)\n",
            "Shape of X_test: (1, 2)\n",
            "Shape of y_train: (4,)\n",
            "Shape of y_test: (1,)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:700: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.5-Cross-validation example: parameter tuning**"
      ],
      "metadata": {
        "id": "xOjyQXZoomaj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "import numpy as np\n",
        "\n",
        "# Generate some example data\n",
        "X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])  # Features\n",
        "y = np.array([0, 1, 0, 1, 1])  # Target variable"
      ],
      "metadata": {
        "id": "SiiFdixIo37c"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "# 10-fold cross-validation with K=5 for KNN (the n_neighbors parameter)\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "scores = cross_val_score(knn, X, y, cv=3, scoring='accuracy')\n",
        "print(scores)"
      ],
      "metadata": {
        "id": "zbxVKid0pBFq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# use average accuracy as an estimate of out-of-sample accuracy\n",
        "print(scores.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdcfKxCzp7w-",
        "outputId": "e45a666b-aac8-48e5-e203-8365bad78257"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# search for an optimal value of K for KNN\n",
        "k_range = list(range(1, 31))\n",
        "k_scores = []\n",
        "for k in k_range:\n",
        " knn = KNeighborsClassifier(n_neighbors=k)\n",
        " scores = cross_val_score(knn, X, y, cv=3, scoring='accuracy')\n",
        " k_scores.append(scores.mean())\n",
        "print(k_scores)"
      ],
      "metadata": {
        "id": "Y4wYCN-RqCcM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "# plot the value of K for KNN (x-axis) versus the cross-validated accuracy (y-axis)\n",
        "plt.plot(k_range, k_scores)\n",
        "plt.xlabel('Value of K for KNN')\n",
        "plt.ylabel('Cross-Validated Accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "z1ADMd03qfrp",
        "outputId": "8ccbc60c-8890-48b3-b121-5b5be1069f47"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cross-Validated Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.6 Cross-validation example: model selection**"
      ],
      "metadata": {
        "id": "Hk8NgzhvsFD0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "import numpy as np\n",
        "\n",
        "# Generate some example data\n",
        "X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])  # Features\n",
        "y = np.array([0, 1, 0, 1])  # Target variable\n"
      ],
      "metadata": {
        "id": "SK6tgsvesOcF"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 10-fold cross-validation with the best KNN model\n",
        "knn = KNeighborsClassifier(n_neighbors=20)\n",
        "print(cross_val_score(knn, X, y, cv=2, scoring='accuracy').mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hv_6KxsSsTtR",
        "outputId": "c64c9af5-2429-4dea-e497-91ed7c621f29"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:794: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 115, in __call__\n",
            "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 276, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 73, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py\", line 234, in predict\n",
            "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_base.py\", line 810, in kneighbors\n",
            "    raise ValueError(\n",
            "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 5, n_neighbors = 20\n",
            "\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:794: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 115, in __call__\n",
            "    score = scorer._score(cached_call, estimator, *args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 276, in _score\n",
            "    y_pred = method_caller(estimator, \"predict\", X)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_scorer.py\", line 73, in _cached_call\n",
            "    return getattr(estimator, method)(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_classification.py\", line 234, in predict\n",
            "    neigh_ind = self.kneighbors(X, return_distance=False)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/neighbors/_base.py\", line 810, in kneighbors\n",
            "    raise ValueError(\n",
            "ValueError: Expected n_neighbors <= n_samples,  but n_samples = 5, n_neighbors = 20\n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10-fold cross-validation with logistic regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression(solver='liblinear')\n",
        "print(cross_val_score(logreg, X, y, cv=3, scoring='accuracy').mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXSkgFdYtOLk",
        "outputId": "67b22ac5-74c4-4c49-eee0-8478d46ba0db"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8888888888888888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2.7-Cross -Validation example: feature selection**"
      ],
      "metadata": {
        "id": "oxNfhvtCuwlX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "metadata": {
        "id": "XPW_5PrTv_Sf"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wget"
      ],
      "metadata": {
        "id": "ozF_vi6fwT7D",
        "outputId": "4f642762-fb7d-4306-beeb-fae28b8e49dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=5d2dabd0c22661a5d7e8f4393f1885b917e5f4e7e39632cdaed7c1cfce76c681\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import wget\n",
        "wget.download('https://raw.githubusercontent.com/hussain0048/Machine-Learning/master/Advertising.csv')\n"
      ],
      "metadata": {
        "id": "4ctZChyMwat9",
        "outputId": "1444208b-c6b2-4bbb-ee34-4437642c78be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Advertising.csv'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read in the advertising dataset\n",
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('Advertising.csv', index_col=0)"
      ],
      "metadata": {
        "id": "ZMiJmgUPwFHl"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a Python list of three feature names\n",
        "feature_cols = ['TV', 'Radio', 'Newspaper']\n",
        "# use the list to select a subset of the DataFrame (X)\n",
        "X = data[feature_cols]\n",
        "# select the Sales column as the response (y)\n",
        "y = data.Sales"
      ],
      "metadata": {
        "id": "AcnJBudVwrHo"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 10-fold cross-validation with all three features\n",
        "lm = LinearRegression()\n",
        "scores = cross_val_score(lm, X, y, cv=10, scoring='neg_mean_squared_error')\n",
        "print(scores)"
      ],
      "metadata": {
        "id": "e4gxoI4dwwCD",
        "outputId": "50298975-24a6-4be2-f8cb-7658587a46d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-3.56038438 -3.29767522 -2.08943356 -2.82474283 -1.3027754  -1.74163618\n",
            " -8.17338214 -2.11409746 -3.04273109 -2.45281793]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fix the sign of MSE scores\n",
        "mse_scores = -scores\n",
        "print(mse_scores)"
      ],
      "metadata": {
        "id": "b2Jr7KHaw1fS",
        "outputId": "cf0f7894-c936-4898-f5fa-f2a5b7f36585",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3.56038438 3.29767522 2.08943356 2.82474283 1.3027754  1.74163618\n",
            " 8.17338214 2.11409746 3.04273109 2.45281793]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert from MSE to RMSE\n",
        "rmse_scores = np.sqrt(mse_scores)\n",
        "print(rmse_scores)"
      ],
      "metadata": {
        "id": "jfne29-0w5Zj",
        "outputId": "149c9e90-9696-4ef6-d5b8-8206c3633491",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.88689808 1.81595022 1.44548731 1.68069713 1.14139187 1.31971064\n",
            " 2.85891276 1.45399362 1.7443426  1.56614748]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the average RMSE\n",
        "print(rmse_scores.mean())"
      ],
      "metadata": {
        "id": "yROjwedmw9Q_",
        "outputId": "15816893-584a-410f-c085-bb56aab7c7cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.6913531708051797\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10-fold cross-validation with two features (excluding Newspaper)\n",
        "feature_cols = ['TV', 'Radio']\n",
        "X = data[feature_cols]\n",
        "print(np.sqrt(-cross_val_score(lm, X, y, cv=10, scoring='neg_mean_squared_error')).mean())"
      ],
      "metadata": {
        "id": "S9i8idu0xDSA",
        "outputId": "1d19bd75-ef39-4d58-f72d-b05e8508b760",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.6796748419090761\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMZ3vXkAHyst"
      },
      "source": [
        "# **📚References**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOu2ttBhIqPr"
      },
      "source": [
        "\n",
        "\n",
        "[1-Fill Missing Values in a Dataset using Python](https://thecleverprogrammer.com/2021/05/29/fill-missing-values-in-a-dataset-using-python/)\n",
        "\n",
        "[2- Data Cleaning with Python](https://medium.com/bitgrit-data-science-publication/data-cleaning-with-python-f6bc3da64e45)\n",
        "\n",
        "[3- SMOTE using Python](https://towardsdatascience.com/applying-smote-for-class-imbalance-with-just-a-few-lines-of-code-python-cdf603e58688)\n",
        "\n",
        "[4-Protecting your Money: Detecting Credit Card Fraud with ML/DL](https://towardsdatascience.com/protecting-your-money-detecting-credit-card-fraud-with-ml-dl-2c4a9b9a0779)\n",
        "\n",
        "[5-How to Make Your Machine Learning Models Robust to Outliers](https://heartbeat.comet.ml/how-to-make-your-machine-learning-models-robust-to-outliers-44d404067d07)\n",
        "\n",
        "[6-What Is Encoding? And Its Importance in Data Science!](https://medium.datadriveninvestor.com/what-is-encoding-and-its-importance-in-data-science-6a2b0cce8e8e)\n",
        "\n",
        "[7-Nominal And Ordinal Encoding In Data Science!](https://medium.com/nerd-for-tech/nominal-and-ordinal-encoding-in-data-science-c93872601f16)\n",
        "\n",
        "[8-imbalanced-learn documentation](https://imbalanced-learn.org/stable/user_guide.html)\n",
        "\n",
        "[Feature Engineering for Categorical Data](https://medium.com/geekculture/feature-engineering-for-categorical-data-a77a04b3308)\n",
        "\n",
        "[Handling Imbalanced Datasets With Oversampling Techniques. It’s Pros & Cons.](https://medium.com/analytics-vidhya/handling-imbalanced-datasets-with-oversampling-techniques-its-pros-cons-ba9f36ac5b71)\n",
        "\n",
        "[8-Overcoming Imbalanced Dataset](https://xzz201920.medium.com/imbalanced-dataset-26732a333cf7)\n",
        "\n",
        "[9-How to handle Categorical variables?](https://medium.com/geekculture/how-to-handle-categorical-variables-7c1ee198c55c)\n",
        "\n",
        "[10-Five Cool Python Libraries for Data Science](https://pub.towardsai.net/five-cool-python-libraries-for-data-science-7f1fce402b90)\n",
        "\n",
        "[11-Scikit-Learn (Python): 6 Useful Tricks for Data Scientists](https://levelup.gitconnected.com/scikit-learn-python-6-useful-tricks-for-data-scientists-1a0a502a6aa3)\n",
        "\n",
        "[12-Feature Scaling with Python’s Scikit-learn](https://pub.towardsai.net/feature-scaling-with-pythons-scikit-learn-10ab42119ae0)\n",
        "\n",
        "[13-Regression Algorithm to Predict House Prices in Python](https://pub.towardsai.net/regression-algorithm-to-predict-house-prices-in-python-7722f2d76548)\n",
        "\n",
        "[14-One Hot Encode in Machine Learning](https://debuggercafe.com/one-hot-encode-in-machine-learning/)\n",
        "\n",
        "[15-What is skewness in data? How to fix skewed data in python?](https://yashowardhanshinde.medium.com/what-is-skewness-in-data-how-to-fix-skewed-data-in-python-a792e98c0fa6)\n",
        "\n",
        "[16- 9 Seaborn Datasets for Data Science + ML Beginners](https://python.plainenglish.io/9-datasets-for-data-science-ml-beginners-cfb57df53fda)\n",
        "\n",
        "[17-How to Deal with an Imbalanced Dataset in Machine Learning](https://towardsdatascience.com/how-to-deal-with-an-imbalanced-dataset-in-machine-learning-4f89a1967868)\n",
        "\n",
        "[18- Outlier Detection in Machine Learning](https://medium.com/@listontellis/outlier-detection-in-machine-learning-382557c775aa)\n",
        "\n",
        "\n",
        "[19- Essential guide to Improve Imbalanced Data Classification Model Performance](https://medium.com/geekculture/essential-guide-to-improve-imbalanced-data-classification-model-performance-c8cf0125b731)\n",
        "\n",
        "[20-Python Tricks for Data Science\n",
        "](https://medium.com/analytics-vidhya/python-tricks-for-data-science-fd038ab838a)\n",
        "\n",
        "[21-10 Fantastic Python Packages](https://levelup.gitconnected.com/10-fantastic-python-packages-af2a16a1183a)\n",
        "\n",
        "[22-Skewness — Everything you need to know about Skewness](https://aayushmaan1306.medium.com/skewness-everything-you-need-to-know-about-skewness-d94e88508e92)\n",
        "\n",
        "[23-Different Ways To Handle Imbalanced Dataset In Machine Learning](https://medium.com/geekculture/different-ways-to-handl-imbalanced-dataset-in-machine-learning-32c7bc350257)\n",
        "\n",
        "[5 Obscure Python Libraries Every Data Scientist Should Know](https://medium.com/analytics-vidhya/5-obscure-python-libraries-every-data-scientist-should-know-3651bf5d3be3)\n",
        "\n",
        "\n",
        "\n",
        "[Descriptive Statistics in data science -with illustrations in ‘python’](https://medium.com/analytics-vidhya/descriptive-statistics-in-data-science-with-illustrations-in-python-efd5ccc152f1)\n",
        "\n",
        "[The Ultimate Python Package to Pre-Process Data for Machine Learning](https://medium.com/swlh/the-ultimate-python-package-to-pre-process-data-for-machin-learning-c87bcc39fa66)\n",
        "\n",
        "[23 great Pandas codes for Data Scientists](https://towardsdatascience.com/23-great-pandas-codes-for-data-scientists-cca5ed9d8a38)\n",
        "\n",
        "[24-Day 13–60 days of Data Science and Machine Learning](https://medium.datadriveninvestor.com/day-13-60-days-of-data-science-and-machine-learning-52df0d8d88f)\n",
        "\n",
        "[What Is Data Preprocessing in Machine Learning, and Its Importance?](https://www.analytixlabs.co.in/blog/data-preprocessing-in-machine-learning/?fbclid=IwAR0Xg83-Uu9Ha9s1mwkJsr-QiyfjOkIiGqn7mAxFSiNs1w5B0ohD-_x5cnI#sub3.2)\n",
        "\n",
        "[3 Pandas Functions That Will Make Your Life Easier](https://towardsdatascience.com/3-pandas-functions-that-will-make-your-life-easier-4d0ce57775a1)\n",
        "\n",
        "[My top 4 functions to style the Pandas Dataframe](https://towardsdatascience.com/my-top-4-functions-to-style-the-pandas-dataframe-932cdc79be39)\n",
        "\n",
        "[How to Clean and Trim String Values in a Pandas DataFrame with Python, Pandas, and the Strip() method](https://deallen7.medium.com/how-to-clean-and-trim-string-values-in-a-pandas-dataframe-with-python-pandas-and-the-strip-c75169f0ebf2)\n",
        "\n",
        "[7 Uncommon But Useful Pandas Functions](https://towardsdatascience.com/7-uncommon-but-useful-pandas-functions-f9f772858859)\n",
        "\n",
        "[5 Obscure Python Libraries Every Data Scientist Should Know](https://medium.com/analytics-vidhya/5-obscure-python-libraries-every-data-scientist-should-know-3651bf5d3be3)\n",
        "\n",
        "[Life Cycle Of Data Science Projects!](https://medium.com/mlearning-ai/life-cycle-of-data-science-projects-a4739f6e2419)\n",
        "\n",
        "\n",
        "[3 Pandas Functions That Will Make Your Life Easier](https://towardsdatascience.com/3-pandas-functions-that-will-make-your-life-easier-4d0ce57775a1)\n",
        "\n",
        "[Different Ways To Handle Imbalanced Dataset In Machine Learning](https://medium.com/geekculture/different-ways-to-handl-imbalanced-dataset-in-machine-learning-32c7bc350257)\n",
        "\n",
        "[Cleaning a messy dataset using Python](https://medium.com/well-red/cleaning-a-messy-dataset-using-python-7d7ab0bf199b)\n",
        "\n",
        "[6.3. Preprocessing data](https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing)\n",
        "\n",
        "[scikit-learn](https://scikit-learn.org/stable/)\n",
        "\n",
        "[How to Combine Multiple Datasets](https://pub.towardsai.net/how-to-combine-multiple-datasets-e78c7e1433b3)\n",
        "\n",
        "[The Ultimate Python Package to Pre-Process Data for Machine Learning](https://medium.com/swlh/the-ultimate-python-package-to-pre-process-data-for-machin-learning-c87bcc39fa66)\n",
        "\n",
        "[Data-Preprocessing with Python](https://medium.com/dataseries/data-preprocessing-with-python-3914d3e9dd30)\n",
        "\n",
        "[How to Deal with Missing Values?](https://medium.com/analytics-vidhya/how-to-deal-with-missing-values-600b70b79918)\n",
        "\n",
        "[Google Colab for Data Science Projects](https://benjaminobi.medium.com/google-colab-for-data-science-projects-7c59e45e9d32)\n",
        "\n",
        "\n",
        "[How to Deal with Categorical Features for Machine Learning](https://medium.com/analytics-vidhya/how-to-deal-with-categorical-features-for-machine-learning-17c6c160ea1)\n",
        "\n",
        "[5 pandas functions I found to be useful for specific operations](https://soulsinporto.medium.com/5-pandas-functions-i-found-to-be-useful-for-specific-transformations-a5a2af3b69c7)\n",
        "\n",
        "[7 Python Libraries to Make Automation Easy](https://python.plainenglish.io/7-python-libraries-to-make-automation-easy-aea8fb6562a7)\n",
        "\n",
        "[Z-Distribution or Z-Score Application in Machine Learning](https://medium.com/pythoneers/z-distribution-or-z-score-application-in-machine-learning-fbba081cd9fe)\n",
        "\n",
        "[How to Generate Synthetic Tabular Dataset](https://www.kdnuggets.com/2022/03/generate-tabular-synthetic-dataset.html)\n"
      ]
    }
  ]
}