Boston_HousePrice_Prediction.ipynb

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        "**Problem Statement**\n",
        "\n",
        "This Boston House Price Prediction model is a regression model trained to predict the price of houses in the Boston area based on various parameters such as crime rate, number of rooms, and other relevant features. By analyzing these input variables, the model learns the relationship between them and the corresponding house prices, allowing it to make accurate predictions. This predictive capability is valuable for real estate professionals, homeowners, and potential buyers, aiding in pricing decisions, property valuation, and investment analysis in the Boston housing market."
      ],
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      "source": [
        "# Importing Dependencies\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "from xgboost import XGBRegressor"
      ],
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        "id": "ZmwQe50UlI3v"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "# Loading the dataset as pandas DataFrame\n",
        "house_dataset = pd.read_csv('/content/BostonHousing.csv')"
      ],
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        "id": "e1rNrN3xmZUr"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "# Checking the first 5 rows of the DataFrame\n",
        "house_dataset.head()"
      ],
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        "outputId": "21d52776-7760-41fd-fc07-4314baf68610"
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              "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
              "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
              "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
              "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
              "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
              "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
              "\n",
              "        b  lstat  price  \n",
              "0  396.90   4.98   24.0  \n",
              "1  396.90   9.14   21.6  \n",
              "2  392.83   4.03   34.7  \n",
              "3  394.63   2.94   33.4  \n",
              "4  396.90   5.33   36.2  "
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          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the number of rows and columns in the DataFrame\n",
        "house_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3kB09BNm0Ut",
        "outputId": "ab98f2b8-e9b8-404c-ca5c-96edcbeb2833"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checkning the statistical measures\n",
        "house_dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "OeLOEd7vnaBr",
        "outputId": "a983993b-4946-4c4d-dcf2-3bb9e692406a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             crim          zn       indus        chas         nox          rm  \\\n",
              "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
              "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
              "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
              "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
              "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
              "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
              "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
              "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
              "\n",
              "              age         dis         rad         tax     ptratio           b  \\\n",
              "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
              "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
              "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
              "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
              "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
              "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
              "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
              "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
              "\n",
              "            lstat       price  \n",
              "count  506.000000  506.000000  \n",
              "mean    12.653063   22.532806  \n",
              "std      7.141062    9.197104  \n",
              "min      1.730000    5.000000  \n",
              "25%      6.950000   17.025000  \n",
              "50%     11.360000   21.200000  \n",
              "75%     16.955000   25.000000  \n",
              "max     37.970000   50.000000  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>crim</th>\n",
              "      <th>zn</th>\n",
              "      <th>indus</th>\n",
              "      <th>chas</th>\n",
              "      <th>nox</th>\n",
              "      <th>rm</th>\n",
              "      <th>age</th>\n",
              "      <th>dis</th>\n",
              "      <th>rad</th>\n",
              "      <th>tax</th>\n",
              "      <th>ptratio</th>\n",
              "      <th>b</th>\n",
              "      <th>lstat</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.613524</td>\n",
              "      <td>11.363636</td>\n",
              "      <td>11.136779</td>\n",
              "      <td>0.069170</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>22.532806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.601545</td>\n",
              "      <td>23.322453</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.253994</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.082045</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.256510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.677083</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>50.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"house_dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"crim\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.21241273856964,\n        \"min\": 0.00632,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.613523557312254,\n          0.25651,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 174.65631992520622,\n        \"min\": 0.0,\n        \"max\": 506.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          506.0,\n          11.363636363636363,\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"indus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 175.10046881853455,\n        \"min\": 0.46,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.13677865612648,\n          9.69,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chas\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 178.83151296515905,\n        \"min\": 0.0,\n        \"max\": 506.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.0691699604743083,\n          1.0,\n          0.25399404134041037\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"nox\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 178.71946937975397,\n        \"min\": 0.11587767566755595,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5546950592885376,\n          0.538,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 176.99257138815915,\n        \"min\": 0.7026171434153233,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.284634387351779,\n          6.2085,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 161.29423343904304,\n        \"min\": 2.9,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          68.57490118577076,\n          77.5,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dis\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 177.4338019618181,\n        \"min\": 1.1296,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.795042687747036,\n          3.2074499999999997,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 175.26272292595036,\n        \"min\": 1.0,\n        \"max\": 506.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          506.0,\n          9.549407114624506,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 205.93933614417858,\n        \"min\": 168.53711605495903,\n        \"max\": 711.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          408.2371541501976,\n          330.0,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ptratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173.36059244426346,\n        \"min\": 2.1649455237144406,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          18.455533596837945,\n          19.05,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"b\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 173.23587641654387,\n        \"min\": 0.32,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          356.6740316205534,\n          391.44,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lstat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 174.45535325169888,\n        \"min\": 1.73,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          12.653063241106722,\n          11.36,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 171.852511161592,\n        \"min\": 5.0,\n        \"max\": 506.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          22.532806324110677,\n          21.2,\n          506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking if there are any null values\n",
        "house_dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VyihPtevngLT",
        "outputId": "354c9dc3-b6ef-43fa-d698-360113f15330"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "crim       0\n",
              "zn         0\n",
              "indus      0\n",
              "chas       0\n",
              "nox        0\n",
              "rm         0\n",
              "age        0\n",
              "dis        0\n",
              "rad        0\n",
              "tax        0\n",
              "ptratio    0\n",
              "b          0\n",
              "lstat      0\n",
              "price      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separating the features from the labels\n"
      ],
      "metadata": {
        "id": "d6Q3oE3vnp0s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = house_dataset.drop(columns='price',axis=1)\n",
        "y = house_dataset['price']"
      ],
      "metadata": {
        "id": "ne2o9cuunnpa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the training and testing data"
      ],
      "metadata": {
        "id": "0Xn3CHRGn68r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=2)"
      ],
      "metadata": {
        "id": "JTdqlo5Wn5Zs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model building and Training\n"
      ],
      "metadata": {
        "id": "glyl6obmoMXA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "XGBoost Regressor"
      ],
      "metadata": {
        "id": "AxEfOKNeoggW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = XGBRegressor()"
      ],
      "metadata": {
        "id": "vvDkoBD0oLY3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model with training data\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "_qtLXd72oryd",
        "outputId": "60c5bb7b-3c57-4abb-ec52-1ed3e6cfdcd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training Accuracy\n",
        "training_prediction = model.predict(x_train)\n",
        "score_1 = metrics.r2_score(y_train,training_prediction)"
      ],
      "metadata": {
        "id": "skruw9YDowuB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Model's accuracy on training data is:\", score_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6Il_uDMozFG",
        "outputId": "c61d5bf2-01c7-4d08-f2f9-2aba0bd89063"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model's accuracy on training data is: 0.9999980039471451\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing Accuracy\n",
        "testing_prediction = model.predict(x_test)\n",
        "score_2 = metrics.r2_score(y_test,testing_prediction)"
      ],
      "metadata": {
        "id": "0uoehZFWpNfc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Model's accuracy on testing data is:\", score_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1imSEUh-pjr0",
        "outputId": "4edfde45-4c3f-4d4d-caac-263f314d9ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model's accuracy on testing data is: 0.9051721149855378\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making our own Prediction System"
      ],
      "metadata": {
        "id": "s9MR0jskqFEv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Taking input of user for Prediction System\n",
        "input = [\t0.00632,\t18.0,\t2.31,\t0,\t0.538,\t6.575,\t65.2,\t4.0900,\t1,\t296,\t15.3,\t396.90,\t4.98]\n",
        "input_array = np.asarray(input)\n",
        "input_array_reshaped = input_array.reshape(1,-1)"
      ],
      "metadata": {
        "id": "w3CCA-zCpned"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating prediction\n",
        "prediction = model.predict(input_array_reshaped)\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4L4RfQTkqeWO",
        "outputId": "4786c319-93c0-4d83-8ebb-0a472466ac6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[23.99494]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DswSOnbTq5l9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}