ML_L1_Assignment_ipynb.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sanyam8055/100-Days-of-ML/blob/master/ML_L1_Assignment_ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "9KG8r9uMHT9w",
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      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from xgboost import XGBClassifier\n",
        "import xgboost as xgb\n",
        "from sklearn.ensemble import AdaBoostClassifier, RandomForestRegressor, RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV \n",
        "from sklearn.metrics import mean_absolute_error, accuracy_score, confusion_matrix, classification_report, roc_auc_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import roc_curve "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3nD6HvHHqU7",
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      "source": [
        "data_url = \"https://raw.githubusercontent.com/shala2020/shala2020.github.io/master/Lecture_Materials/Assignments/MachineLearning/L1/attrition.csv\"\n",
        "attrition_data = pd.read_csv(data_url)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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        "outputId": "942e3fc4-4394-4872-a626-feac8aec8177"
      },
      "source": [
        "attrition_data.head()"
      ],
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Attrition</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>EmployeeCount</th>\n",
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              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>OverTime</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "      <th>ID</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>41</td>\n",
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              "      <td>1102</td>\n",
              "      <td>Sales</td>\n",
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              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>4</td>\n",
              "      <td>Single</td>\n",
              "      <td>5993</td>\n",
              "      <td>19479</td>\n",
              "      <td>8</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Research &amp; Development</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>Male</td>\n",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>5130</td>\n",
              "      <td>24907</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>23</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
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              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>4</td>\n",
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              "      <td>92</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>3</td>\n",
              "      <td>Single</td>\n",
              "      <td>2090</td>\n",
              "      <td>2396</td>\n",
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              "      <td>15</td>\n",
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              "      <td>2</td>\n",
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              "      <td>40</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>3468</td>\n",
              "      <td>16632</td>\n",
              "      <td>9</td>\n",
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              "      <td>12</td>\n",
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              "      <td>1</td>\n",
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              "      <td>4</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "   Age  Attrition  ... YearsWithCurrManager  ID\n",
              "0   41          1  ...                    5   0\n",
              "1   49          0  ...                    7   1\n",
              "2   37          1  ...                    0   2\n",
              "3   33          0  ...                    0   3\n",
              "4   27          0  ...                    2   4\n",
              "\n",
              "[5 rows x 34 columns]"
            ]
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        "id": "ADTtNm1HHbXc",
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      },
      "source": [
        "y = attrition_data['Attrition']\n",
        "X = attrition_data.drop(['Attrition'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uklIwVi3Kkxx",
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          "height": 609
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        "outputId": "96fd743b-fc2f-4283-a5fe-d170b4054570"
      },
      "source": [
        "X"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</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",
              "      <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>1023</th>\n",
              "      <td>56</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1255</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>1441</td>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>90</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Research Scientist</td>\n",
              "      <td>1</td>\n",
              "      <td>Married</td>\n",
              "      <td>2066</td>\n",
              "      <td>10494</td>\n",
              "      <td>2</td>\n",
              "      <td>No</td>\n",
              "      <td>22</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024</th>\n",
              "      <td>47</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>359</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>1443</td>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>82</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>Research Director</td>\n",
              "      <td>3</td>\n",
              "      <td>Married</td>\n",
              "      <td>17169</td>\n",
              "      <td>26703</td>\n",
              "      <td>3</td>\n",
              "      <td>No</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>20</td>\n",
              "      <td>17</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1025</th>\n",
              "      <td>24</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>1476</td>\n",
              "      <td>Sales</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Medical</td>\n",
              "      <td>1</td>\n",
              "      <td>1445</td>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>3</td>\n",
              "      <td>Married</td>\n",
              "      <td>4162</td>\n",
              "      <td>15211</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>32</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>601</td>\n",
              "      <td>Sales</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>Marketing</td>\n",
              "      <td>1</td>\n",
              "      <td>1446</td>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>97</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>Sales Executive</td>\n",
              "      <td>4</td>\n",
              "      <td>Married</td>\n",
              "      <td>9204</td>\n",
              "      <td>23343</td>\n",
              "      <td>4</td>\n",
              "      <td>No</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>34</td>\n",
              "      <td>Travel_Rarely</td>\n",
              "      <td>401</td>\n",
              "      <td>Research &amp; Development</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Life Sciences</td>\n",
              "      <td>1</td>\n",
              "      <td>1447</td>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>86</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>Laboratory Technician</td>\n",
              "      <td>2</td>\n",
              "      <td>Married</td>\n",
              "      <td>3294</td>\n",
              "      <td>3708</td>\n",
              "      <td>5</td>\n",
              "      <td>No</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1027</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1028 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Age     BusinessTravel  ...  YearsWithCurrManager    ID\n",
              "0      41      Travel_Rarely  ...                     5     0\n",
              "1      49  Travel_Frequently  ...                     7     1\n",
              "2      37      Travel_Rarely  ...                     0     2\n",
              "3      33  Travel_Frequently  ...                     0     3\n",
              "4      27      Travel_Rarely  ...                     2     4\n",
              "...   ...                ...  ...                   ...   ...\n",
              "1023   56      Travel_Rarely  ...                     0  1023\n",
              "1024   47      Travel_Rarely  ...                     6  1024\n",
              "1025   24      Travel_Rarely  ...                     3  1025\n",
              "1026   32      Travel_Rarely  ...                     3  1026\n",
              "1027   34      Travel_Rarely  ...                     2  1027\n",
              "\n",
              "[1028 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AEXe-b8PdQE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=X.apply(LabelEncoder().fit_transform)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CU-pGo0ZMkH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "b1204dba-6887-4119-c6b5-2dff486e9bde"
      },
      "source": [
        "print(attrition_data.isna().sum())"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Age                         0\n",
            "Attrition                   0\n",
            "BusinessTravel              0\n",
            "DailyRate                   0\n",
            "Department                  0\n",
            "DistanceFromHome            0\n",
            "Education                   0\n",
            "EducationField              0\n",
            "EmployeeCount               0\n",
            "EmployeeNumber              0\n",
            "EnvironmentSatisfaction     0\n",
            "Gender                      0\n",
            "HourlyRate                  0\n",
            "JobInvolvement              0\n",
            "JobLevel                    0\n",
            "JobRole                     0\n",
            "JobSatisfaction             0\n",
            "MaritalStatus               0\n",
            "MonthlyIncome               0\n",
            "MonthlyRate                 0\n",
            "NumCompaniesWorked          0\n",
            "OverTime                    0\n",
            "PercentSalaryHike           0\n",
            "PerformanceRating           0\n",
            "RelationshipSatisfaction    0\n",
            "StockOptionLevel            0\n",
            "TotalWorkingYears           0\n",
            "TrainingTimesLastYear       0\n",
            "WorkLifeBalance             0\n",
            "YearsAtCompany              0\n",
            "YearsInCurrentRole          0\n",
            "YearsSinceLastPromotion     0\n",
            "YearsWithCurrManager        0\n",
            "ID                          0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XG9oxpbYWn9n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "92d1fc2d-24a9-4e6a-db5c-d46276a22aed"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Age</th>\n",
              "      <th>BusinessTravel</th>\n",
              "      <th>DailyRate</th>\n",
              "      <th>Department</th>\n",
              "      <th>DistanceFromHome</th>\n",
              "      <th>Education</th>\n",
              "      <th>EducationField</th>\n",
              "      <th>EmployeeCount</th>\n",
              "      <th>EmployeeNumber</th>\n",
              "      <th>EnvironmentSatisfaction</th>\n",
              "      <th>Gender</th>\n",
              "      <th>HourlyRate</th>\n",
              "      <th>JobInvolvement</th>\n",
              "      <th>JobLevel</th>\n",
              "      <th>JobRole</th>\n",
              "      <th>JobSatisfaction</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>MonthlyRate</th>\n",
              "      <th>NumCompaniesWorked</th>\n",
              "      <th>OverTime</th>\n",
              "      <th>PercentSalaryHike</th>\n",
              "      <th>PerformanceRating</th>\n",
              "      <th>RelationshipSatisfaction</th>\n",
              "      <th>StockOptionLevel</th>\n",
              "      <th>TotalWorkingYears</th>\n",
              "      <th>TrainingTimesLastYear</th>\n",
              "      <th>WorkLifeBalance</th>\n",
              "      <th>YearsAtCompany</th>\n",
              "      <th>YearsInCurrentRole</th>\n",
              "      <th>YearsSinceLastPromotion</th>\n",
              "      <th>YearsWithCurrManager</th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>498</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>573</td>\n",
              "      <td>717</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>88</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>494</td>\n",
              "      <td>951</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19</td>\n",
              "      <td>2</td>\n",
              "      <td>649</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>45</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>660</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>231</td>\n",
              "      <td>878</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>243</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>292</td>\n",
              "      <td>603</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age  BusinessTravel  ...  YearsWithCurrManager  ID\n",
              "0   23               2  ...                     5   0\n",
              "1   31               1  ...                     7   1\n",
              "2   19               2  ...                     0   2\n",
              "3   15               1  ...                     0   3\n",
              "4    9               2  ...                     2   4\n",
              "\n",
              "[5 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GvLn_xLoCOn",
        "colab_type": "text"
      },
      "source": [
        "# **Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejMxrHhELCiT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7070fb89-d07a-440f-c69b-53bbde8d64d9"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state=123)\n",
        " \n",
        "rfc = RandomForestClassifier(n_estimators=1,\n",
        "                            random_state=1111,\n",
        "                            max_features=32)\n",
        "\n",
        "rfc.fit(X_train, y_train)\n",
        "\n",
        "print('The training error is {0:.3f}'.format(mean_absolute_error(y_train, rfc.predict(X_train))))\n",
        "print('The testing error is {0:.3f}'.format(mean_absolute_error(y_test, rfc.predict(X_test))))"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The training error is 0.096\n",
            "The testing error is 0.226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7d9sUp4fT65",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9f6fda30-9b87-4754-a294-21379593a555"
      },
      "source": [
        "predictions = rfc.predict(X_train)\n",
        "accuracy = accuracy_score(y_train, predictions)\n",
        "print(np.round(accuracy*100, 2), '%')"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90.4 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-y18xSh4nYJl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "54c51ad7-b965-41bf-b6d4-78bd85085e86"
      },
      "source": [
        "predictions = rfc.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "print(np.round(accuracy*100, 2), '%')"
      ],
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "77.43 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DSRvc6uLnZjk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "7f8c259a-7d18-4056-ada5-4acb3642f2cd"
      },
      "source": [
        "print(confusion_matrix(y_test, predictions))\n",
        "print(\"=\"*60)\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[178  32]\n",
            " [ 26  21]]\n",
            "============================================================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.85      0.86       210\n",
            "           1       0.40      0.45      0.42        47\n",
            "\n",
            "    accuracy                           0.77       257\n",
            "   macro avg       0.63      0.65      0.64       257\n",
            "weighted avg       0.79      0.77      0.78       257\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTzabXS1MigT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "873a8b36-213c-494c-a1f5-9472922888de"
      },
      "source": [
        " plt.plot(y_test, 'o')\n",
        " plt.plot(rfr.predict(X_test))"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe018de8e80>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoBiVNLVnnHK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "df0e1f41-36be-4b41-a83d-7429f2c2c925"
      },
      "source": [
        " \n",
        "probs = rfc.predict_proba(X_test)  \n",
        "probs = probs[:, 1]  \n",
        "fper, tper, thresholds = roc_curve(y_test, probs) \n",
        "plt.plot(fper, tper)\n",
        "plt.plot([0,1], [0,1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Random Forest Classifier ROC curve')\n",
        "plt.show()"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8apE1VwkC4y",
        "colab_type": "text"
      },
      "source": [
        "# **Extreme Gradient Boosting** "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUVbsi4OY12Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "0d294fd9-784c-437f-e800-19945f3f65bd"
      },
      "source": [
        "xgb_clf = xgb.XGBClassifier(learning_rate=0.01)\n",
        "xgb_clf.fit(X_train, y_train)"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "              learning_rate=0.01, max_delta_step=0, max_depth=3,\n",
              "              min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='binary:logistic', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbVBt-cSbKwF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ba131b7a-9538-406f-9743-4e9fee1feb39"
      },
      "source": [
        "predictions = xgb_clf.predict(X_train)\n",
        "# accuracy score\n",
        "accuracy = accuracy_score(y_train, predictions)\n",
        "print(np.round(accuracy*100, 2), '%')"
      ],
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "88.2 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Em4Izb3ebudC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7d20b9ed-e1ad-4b2c-e230-395b256481b1"
      },
      "source": [
        "predictions = xgb_clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "print(np.round(accuracy*100, 2), '%')"
      ],
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "83.66 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJ-avltpbySR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "95a4a77e-79e8-41b8-e1e5-2a84f4dec711"
      },
      "source": [
        "print(confusion_matrix(y_test, predictions))\n",
        "print(\"=\"*60)\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[206   4]\n",
            " [ 38   9]]\n",
            "============================================================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.98      0.91       210\n",
            "           1       0.69      0.19      0.30        47\n",
            "\n",
            "    accuracy                           0.84       257\n",
            "   macro avg       0.77      0.59      0.60       257\n",
            "weighted avg       0.82      0.84      0.80       257\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vvh7VTjJcPHC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f1e14a31-ab6d-4add-bb8d-56835d1f92b4"
      },
      "source": [
        "plt.plot(y_test, 'o')\n",
        "plt.plot(xgb_clf.predict(X_test))"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe018ea39e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_2VLRqhVb1AM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "eda2b302-a6fe-4c14-fcd7-e537e27789d2"
      },
      "source": [
        "probs = xgb_clf.predict_proba(X_test)  \n",
        "probs = probs[:, 1]  \n",
        "fper, tper, thresholds = roc_curve(y_test, probs) \n",
        "plt.plot(fper, tper)\n",
        "plt.plot([0,1], [0,1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title(' RFC ROC curve')\n",
        "plt.show()"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpnFeLPIj10s",
        "colab_type": "text"
      },
      "source": [
        "# **Gradient Boosting Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BMF2rEnb7qY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "6b20e12b-d7fb-4ad9-c3ba-f39393f8e7dd"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.3, max_depth=1, random_state=0)\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
              "                           learning_rate=0.3, loss='deviance', max_depth=1,\n",
              "                           max_features=None, max_leaf_nodes=None,\n",
              "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                           min_samples_leaf=1, min_samples_split=2,\n",
              "                           min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                           n_iter_no_change=None, presort='deprecated',\n",
              "                           random_state=0, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgE_8SmdiRwG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "37d76212-990d-491d-bb86-789324cdaad1"
      },
      "source": [
        "predictions = clf.predict(X_train)\n",
        "accuracy = accuracy_score(y_train, predictions)\n",
        "print(np.round(accuracy*100, 2), '%')"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "90.27 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1QDLAx0ib0q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9c45f241-8dbb-456d-f0ec-657e82b2c980"
      },
      "source": [
        "predictions = clf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "print(np.round(accuracy*100, 2), '%')"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "85.99 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJtBJB7AifKY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "c62da094-8219-4bc9-bc33-573a659341a7"
      },
      "source": [
        "print(confusion_matrix(y_test, predictions))\n",
        "print(\"=\"*60)\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[208   2]\n",
            " [ 34  13]]\n",
            "============================================================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.99      0.92       210\n",
            "           1       0.87      0.28      0.42        47\n",
            "\n",
            "    accuracy                           0.86       257\n",
            "   macro avg       0.86      0.63      0.67       257\n",
            "weighted avg       0.86      0.86      0.83       257\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHrpHytfizcN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "2f02a1d8-0b95-4cd5-e36a-842955af9d46"
      },
      "source": [
        "plt.plot(y_test, 'o')\n",
        "plt.plot(clf.predict(X_test))"
      ],
      "execution_count": 274,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe018d3ac18>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 274
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAdx0lEQVR4nO3df5RcZZ3n8fe3OpXQHVg6Ma0TmsSARNwIQqQPxGUcddThxziBdeRHznAWZzmyZ88w6yonHlg4COqsg1lR9sjOwsw4jjiAyGA2YjSymJl1GEEaws9AJPyQpAOmgXRE0pBO93f/qFvdt6vrx62qW+mq535e5+Sk697nPs9z76369O373LrX3B0REel8udnugIiIpEOBLiISCAW6iEggFOgiIoFQoIuIBGLObDW8aNEiX7Zs2Ww1LyLSkR588MGX3b2v3LxZC/Rly5YxODg4W82LiHQkM/tVpXk65SIiEggFuohIIBToIiKBUKCLiARCgS4iEoiaV7mY2TeBjwG73f24MvMNuB44E9gHfNLdH0q7o/Vav2WIdZu2sWtklCN6u/nQu/rY/NTw5Ou1px3L2Sv7E9d1zQ+eYM++MQB6u/NcvfrdiZev1r+hkVHMoHiPtNK64+W6zBh3pz/qPzC5jod35zGDkX1jda9f0r7W2876LUNcveEJRkYL223+3C7yXTlGRsdmrEu1OuL7sVLZeLnenjxvjI0zOjYxrd29o/Vtm6Rt11NPb08ed6b1BWiondLtC9PfP5X638h6VVum3Gft+w8N8fr+8Wl1mMGfnLKUL519fKK6a223NN7fje7jWstduf4xbr1/B+Oxmx/Weq+nwWrdbdHMfg/4LfDtCoF+JvDnFAL9FOB6dz+lVsMDAwPeqssW128Z4vI7H2N0bLxime58F1/++PGJ3shr73iEsfHp2ymfM9adc0LDH/Bq/SvWDVQsl+8ycBibKL//kq5fs32t1M76LUOs/d4jFfuXtI7StsuVTbK/k7TXSNu11NzXZfZj0v5V2r75nHHeyUv4xweHZvT/j0/qLzu9WnvVtgVUfo9WcsGqqVCvVHe5fsal8f5udB/XWu7K9Y/xnfteaFm/zexBdx8oN6/mKRd3/3/Aq1WKnEUh7N3d7wN6zWxxY11t0IuPwo4HJl+u27SN3NhvOSv3L5WXGXudF374FfjXb8AzPy1Me3k7PPvPMDEOD90Mr73ELzZ+Kwpz55yufyLPgcLiE866Tdsq1//y0/Dcz+D5e2H3U/Db3fDkXZP9q/YBON638f0f/bhiuT728AEfLPthXm47GbCnGB0br96/hGr1tVI76zZtSxTmteoobbtc2Vp9TNpeI23XUqtvY+M+Yzsl7V+l7Ts24dx6/46y/a80vVp71bZFvdse4Nb7d9Ssu1w/6+lzEo3u41rLxdevVFqfy0rS+GJRPxBfg53RtBdLC5rZxcDFAEuXLk2h6ciN7y/8f/VeAHaNjPLV/N/x8a5/4fk3f4dH/JgZi1wz5+8598A/w0+YWvYbJxV+/sOvwg8vBeC/A3fx17w/9xjr8jexxHZz3YFzJ9up6Bslv0DfugJ2b4UrXqq+HHDnvKthPxz1xi1l53937hc5OvcSR7/xHSZKfiffPe9zACx745aa7SSRpI5yZeptu546Sqc3sp61lknadrPtNLpcrfnjFf7yrjS9Wn1pbYtyfahUR6V+ptF+reWbfW/U6nsan8tKDuqgqLvf5O4D7j7Q11f2m6upOKK3m8XRHxU99mbZMr9jVf7o2Dd9Xo4JDrfXAVjEb6a1k9ie5wv/+0Ti5SqVe7v9uqnl65GkjnJl6m27njpKpzeynrWWSdp2s+00ulyt+V1mdU2vVl+1bdHI+sX7UGn5Sv1M0q+kGt3HtZar1fc0PpeVpBHoQ8CS2Osjo2mzZu1px5KrsWa5XO03TFG+a2bZfM4mB7Tqtfa0Y+nOdzVdLl9lHbrzXQ33r54+VGpn7WnHVu1f0jpK2y5XNun2rNVeI23XUnMfdtmM7ZS0f5W2bz5nrDllSdn+V5perb1q26LebQ+w5pSpuKhUd7l+1tPnJBrdx7WWi69fqbQ+l5WkccplA3CJmd1GYVB0r7vPON1yMJ29sp/hew+Flwuv+8tc5bL8sMNgOFl9V68+ji0/HoQKV6I00j+g4lUuxXZKyxWvDMEMcK79xHtY95Pt064+oXBhRyoDoqV9qOcql+K0Zq5yKW27Unul5dK4yiVp27WU61saV7mU274w/b058PaFZeutNL2ZbdHoVS7V6o73sxVXuTS6j2stV1y/dr3K5Vbgg8Ai4NfA54E8gLv/7+iyxW8Ap1O4bPFP3b3m5SupXuVy9eHR/3unpn3rY/D8z+DCH8BRvzdzmZv//dRgaHHZYj0fugI2/8XUvM89B1vXw12fgZM+CX90ffI+FeV7YGwf/LddMHd+/esTd80C8Am46lXIlRzF1FpWRDpatatcah6hu/uaGvMd+LMG+ybN0AO+RSRG3xQVEQmEAl1EJBAK9I6mUy4iMkWBLiISCAV6J9OgqIjEKNBFRAKhQBcRCYQCvaPplIuITFGgi4gEIruBPlsDimm2q0FREYnJbqCLiAQmu4Ge4H7LIiKdJLuBPmvSPE2iUy4iMkWBLiISiHADvThgWGngsNEBxWYHIjUoKiItEm6gi4hkTLiBXhz0rDT42eigqAZTRaRNhRvobUuDoiLSGgp0EZFAKNBFRAKhQE+iXa9M0VUuIhKjQBcRCUR2A33Wjm41KCoirZHdQK+LglNE2l92A13Xk4tIYLIb6PVo14FMDYqKSIwCXUQkEOEGervenCtV7dQXEZlt4QZ6qhScItL+wg103ZxLRDIm3ECvRQ+JFpHAJAp0MzvdzLaZ2XYzu6zM/KVmttnMtpjZo2Z2ZvpdnUUKThHpADUD3cy6gBuAM4AVwBozW1FS7ErgdndfCZwP/K+0O5o6nToRkcAkOUI/Gdju7s+6+37gNuCskjIO/Jvo58OBXel1sR2069f19ZeDiExJEuj9wI7Y653RtLirgQvMbCewEfjzchWZ2cVmNmhmg8PDww10V0REKklrUHQN8C13PxI4E7jZzGbU7e43ufuAuw/09fWl1HSDNCgqIoFJEuhDwJLY6yOjaXEXAbcDuPvPgUOARWl0sC0oOEWkAyQJ9AeA5WZ2lJnNpTDouaGkzAvAhwHM7N9SCPT2PqeiQVERCUzNQHf3A8AlwCbgSQpXszxhZl8ws9VRsUuBT5nZI8CtwCfd2/ywtq7uaSBTRNrfnCSF3H0jhcHO+LSrYj9vBU5Nt2siIlKPcL8pWuvmXM3WO1vLt6ouEel44QZ6mhScItIBwg103ZxLRDIm3ECvRQ+JFpHAZDfQ66LgFJH2l91A16kTEQlMdgO9Hu16ZYoGa0UkRoEuIhKI7Aa6BkVFJDDZDfS6KDhFpP1lN9A1KCoigcluoJeecjlYp2A0KCoiLZLdQK+HglNEOkC4gV7vzbnSLle5giaXb1VdItLpwg30VCk4RaT9hRvoujmXiGRMuIFey4xTJxoUFZHOlt1Ar4eCU0Q6QHYDXadORCQw2Q30uq5Db9crU/SXg4hMyW6gi4gERoE+SYOiItLZFOhJKDhFpANkN9A1KCoigcluoM/Wzbk0KCoiLZLdQBcRCUy4gV7vzbmSHu02eySvQVERaZFwAz1NCk4R6QDhBnqtm3M1W6+ISJsJN9DrpUFREelwiQLdzE43s21mtt3MLqtQ5lwz22pmT5jZLel2c7YpOEWk/c2pVcDMuoAbgI8CO4EHzGyDu2+NlVkOXA6c6u57zOytrepwYhoUFZGMSXKEfjKw3d2fdff9wG3AWSVlPgXc4O57ANx9d7rdFBGRWpIEej+wI/Z6ZzQt7p3AO83sXjO7z8xOL1eRmV1sZoNmNjg8PNxYj5NKc1A0fiSsQVERaVNpDYrOAZYDHwTWAH9tZr2lhdz9JncfcPeBvr6+lJpOSTy0O+ZURqf0U0QOhiSBPgQsib0+MpoWtxPY4O5j7v4c8EsKAR8IBaeItL8kgf4AsNzMjjKzucD5wIaSMuspHJ1jZosonIJ5NsV+pq/qqRMFuIh0npqB7u4HgEuATcCTwO3u/oSZfcHMVkfFNgGvmNlWYDOw1t1faVWnU6GHRItIYGpetgjg7huBjSXTror97MBno3/hUXCKSAcI95ui9V6HnnRQtOlw1zdFRaQ1wg30pszS6RgRkSaEG+itul5c16GLSJsKN9AnpfzV/2ZpUFREWiQDgZ4CBaeIdIBwAz0Tg6IiIlPCDXQRkYwJN9BTHbzUzblEpP2FG+iTGhkUbeFpEQ2KikiLZCDQU6DgFJEOoEAvRwEuIh0o/EBv5CqXltJX/0WkNcIP9FQoOEWk/YUb6JNH3CkPiuoh0SLSpsINdBGRjAk30NO8XlwPiRaRDhBuoBclPuNysB4SrUFREWmN8AM9FQpOEWl/4QZ63YOi0xZOUG+DNCgqIi0SbqCLiGRMuIGuQVERyZhwA70o7fuhN02DoiLSGuEHeioUnCLS/hToZSnARaTzZCDQ9ZBoEcmGDAR6ChScItIBwg30TDwkWr9oRGRKuIEuIpIx4Qa6HhItIhkTbqBPyuhDonXeXyRzMhDoKVA2ikgHSBToZna6mW0zs+1mdlmVcn9sZm5mA+l1sUFZHxTVEbpI5tQMdDPrAm4AzgBWAGvMbEWZcocBnwbuT7uTIiJSW5Ij9JOB7e7+rLvvB24DzipT7ovAtcAbKfavcRoUFZGMSRLo/cCO2Oud0bRJZvZeYIm7/7BaRWZ2sZkNmtng8PBw3Z1tTFa/KapTLiJZ0/SgqJnlgOuAS2uVdfeb3H3A3Qf6+vqabfoga/bIPM0je/2VICIzJQn0IWBJ7PWR0bSiw4DjgH8ys+eBVcCGthgYTYs7zR/x6huiItJaSQL9AWC5mR1lZnOB84ENxZnuvtfdF7n7MndfBtwHrHb3wZb0uF5ZvR+6rnIRyZyage7uB4BLgE3Ak8Dt7v6EmX3BzFa3uoPtQeEoIu1vTpJC7r4R2Fgy7aoKZT/YfLdSUPdDohN+U7Tph0Q3t/j0ujQoKiJT9E1REZFAhBvoeki0iGRMuIFepEFREcmI8AM9FQpHEWl/4QZ62w6K6puiItIa4Qa6iEjGhBvoGhQVkYwJN9CLNCgqIhkRfqA3ouxRuG7OJSLtLdxAb2ZQdMbRbezmXA0f+SZcPlH9Pu2/qmVEJDPCDfSmKAxFpPOEG+hpD4oWj5w1KCoibSrcQO/Uh0TXVb8GRUVkSriBLiKSMeEGeuoPidYpFxFpb+EG+qSUv/rfrJqnQupoW1/9F5GYDAR6I1pxFK7r0EWktcIK9HIDm40MipYe3cavcmn2OvRUBkUT1KVBUZHMCSvQ06IwFJEOFG6gpz4o2op6RUTSE1agN3VkrUFREelsYQV6ajQoKiKdJ7BA16BoffWISEgCC/SUKAxFpAOFG+j6pqiIZExYgV72BluNHG3P5kOiNSgqIo0JK9BFRDIs3EDX/dBFJGMCC/Qypxk67SHRuh+6iDQosEBPSUuOwnUduoi0VqJAN7PTzWybmW03s8vKzP+smW01s0fN7B4ze3v6XU2gqUHRNnlIdKL+JqlLR+giWVMz0M2sC7gBOANYAawxsxUlxbYAA+7+HuAO4Ctpd/TgUhiKSOdJcoR+MrDd3Z919/3AbcBZ8QLuvtnd90Uv7wOOTLebDdCgqIhkTJJA7wd2xF7vjKZVchHwo3IzzOxiMxs0s8Hh4eHkvUxMg6KN1SMiIUh1UNTMLgAGgHXl5rv7Te4+4O4DfX19aTadLg2KikgHmpOgzBCwJPb6yGjaNGb2EeAK4APu/mY63atTXYOi5QY+y9QzOa/TBkVFJGuSHKE/ACw3s6PMbC5wPrAhXsDMVgI3AqvdfXf63TzYFJQi0nlqBrq7HwAuATYBTwK3u/sTZvYFM1sdFVsHHAp8z8weNrMNFao7eGqeNqk2v8q8hk/H6JSLiLRWklMuuPtGYGPJtKtiP38k5X41qFX3Q2+gvop90/3QRaQ19E3RcpSFItKBwg30pq5UqTIoquvQRaRNhRXoZU8zpPHV/xTpfugi0iJhBXpb06CoiLRWYIGuh0TXV4+IhCSwQE+JwlBEOlC4ga5BURHJmLACvSMGRZsuECuqQVERmRJWoLc1DYqKSGsFFuj1DIqWGfisNk+DoiLS5gIL9JQoDEWkA4Ub6E3dnKuZelNur+V1iUgowgr0tB4SXe0ql465H7r+yhDJmrACPS065SIiHSjcQE/zOnQ9JFpEOkBgga6HRDdWj4iEILBAb2caFBWR1gor0NN6SPSsDorWU1eCMiKSGWEFelp0ukJEOlC4ga7r0EUkYwIL9JQGRatd5dI0DYqKSGsEFugpURiKSAcKK9BTGxQtV1bfFBWR9hZWoKdFR+gi0oHCDXQNiopIxoQb6KnfD71cubo6VL7uGcV0P3QRaUy4gd4MhaGIdKDOD/TU7sGS8JuizUplUDRJXfqlJJI1nR/oraAjdBHpQOEGugZFRSRjOj/QKw1m1j0oWm2eHhItIu1vTpJCZnY6cD3QBfyNu/9lyfx5wLeBk4BXgPPc/fl0u1qwfssQV3z/MV7fP15omwmeO6Qw771fvJvRfC9vHpjglvwrrMrBVRse59vf/+GMem7Ov8z7u6Zen3vjv3J79HrzU7/mQ7F5//kfHmK5DfHZPNzyixdY9/BPeGNsnNGxCQAW9OT5w/cs5q5HXmRkdAyA5w+Z3t6+/eP0GPynmx9k08T036NmcEzffLYPv84iH+GBaNlll83s94KePA964TfxRX//APdMTDB/bhf5rhx7R8cmt8XvfmUzO70vwRad0mXGeIJfBDmDeXNyjI5NYFT/FXXqOxay9cXX2LNvLFEf8jkYd5jw6e2U9q3LjDWnLOFLZx8/Oe3K9Y9x6/07GHeny4xVRy/g+VdGGRoZrbhuOSu0VZzf39vN2tOOBWDdpm3sGhnl8O48+w+Msy/a38VlimXPXtlfsf3Sde/J5zCzyfdvb3eeq1e/G4BrfvDEjO00b06ONw9MTJvWZYVtFNeTz+Ew+Z4st10PPSTPnn1jk+tabt/F1z/en97uPB87YTGbnxpm18goR/R286F39U2+7u3J4w57R8c4osw2LJaPf0bMCscdtd5D5RS32+CvXp3c5nHFfdSMBT35aZ/zSuZ2GfvHvex7bE7OOBB1pNw2jL9/0lAz0M2sC7gB+CiwE3jAzDa4+9ZYsYuAPe5+jJmdD1wLnJdaLyPrtwxx6fceYbzKnipufPfqpyW85LTFmwcmCr+uarIZH7o9+8b4zn0v1GivyjyHp3e/nqRx9uwbY2Je4Q1bVAiH8UTLV5MkzKHwQZnczjXK3vvMq3X1If7ZibdT2rdx98lt/qWzj+fK9Y9N2wfj7tParrRuxbdScf7QyChr73gEHMaimcUAKl1maGSUy+98DIDBX71atf2ifSXhMDI6xmdvf3havXGlYV6oe2a50npLjU0w+b4trmu5LVJc//EJn9afkdHp7/GhkdFpr+OfiXLbsLQ8xP7ordrz8kZGx/jMdx+uuGyzYQ4kPgjZH+2Qcu+xA7GOlNuGxfdPWqFuXuNDbGbvA65299Oi15cDuPuXY2U2RWV+bmZzgJeAPq9S+cDAgA8ODtbV2VP/8qf8u9d+xKe6po5cDVieGwLgmYnFjEdnkd4ZTRv2w9njh86oqzi/aMdEH0tywwC87vOYb29OznvRFzKXMd5ir/Eb7+ElX1Czr6X1F+3yhfzWuysuN4dxjs69BMAvJ8rv5GLd5eoqzntu4m2MRb+vx8lxxv5ra/a5E3WZ8cyXz+Qdl29M/Aspbf293by0941Za186W39vN/de9vuJy5vZg+4+UG5eklMu/cCO2OudwCmVyrj7ATPbC7wFeLmkIxcDFwMsXbo0Uefjdo2MMpI7lKd9etDtm5jHPMZ4xhdPTts+3s/7c4/xi4ljy9a1fbyfD+e2MIcDvMhbeNSPYmhiEYt5hcd9GR/IPcrPJ97NQG4bD00cA8AHco/ys4njEvV1z8RhvI09jJPjVQ5jxA/lpNwv2RLVVc3YxBxGmcuQLyo7/4Xxt7Iyt71sXa9N9HA4r7PNj5yc5gEMlVRSDNHZDNNdI6O6SFQatmtkNLW6Ep1DT4u73wTcBIUj9HqXP6K3m7tHBrh7ouwvJ8mgruiqo6Tn/1vhCB2hSxOO6K38F3u9khy6DQFLYq+PjKaVLROdcjmcwuBoqtaedixdOV2yJ1PWnLJk2v9pyHcZ+YTvs+58F2tPO7ap9nM2fUxktuW7rOn+1LMNG9VGm6xhxfdPWpIE+gPAcjM7yszmAucDG0rKbAAujH7+BPDTaufPG3X2yn6+es4JzJ87c/SyeKTWnc/V9Wa8YNVSvn7eiXTnk5+WWNCTn1Z+QU+eC1Ytpbc7n7zhGDNY/tb5iS5xL9fW/Lld9HbnMQoj6T11rEtcV8Jr7HPG5PrXWuLUdyxkQU/y7ZLPTYVbvJ3SvnWZccGqpZNXuXzp7OO5YNXSaUfsp75jIf3R0U+ldSu2VZzf39vNuk+cwLpzTqC/t7vsNi0u09/bzZc/fjxnr+yv2H7puvfkc9Pev73dea4790SuO/fEsttp3pyZ+7KrzKr05HNV38P5HJP1F/tYbosU17+0P73dhfddcZv093ZPe72gJz/5Hiy3DYvl4+/b4i5pJJh7u/N87bwTp23zuDR+l5R+ziuZ2zW1z0vNiXWk3DYsvn/SUnNQFMDMzgS+TuE6kG+6+1+Y2ReAQXffYGaHADcDK4FXgfPd/dlqdTYyKCoiknXNDori7huBjSXTror9/AZwTjOdFBGR5oR7+YOISMYo0EVEAqFAFxEJhAJdRCQQia5yaUnDZsPArxpcfBEl30INXJbWV+sariytbyvX9e3u5e+8N2uB3gwzG6x02U6IsrS+WtdwZWl9Z2tddcpFRCQQCnQRkUB0aqDfNNsdOMiytL5a13BlaX1nZV078hy6iIjM1KlH6CIiUkKBLiISiI4LdDM73cy2mdl2M7tstvvTLDNbYmabzWyrmT1hZp+Opi80s7vN7Ono/wXRdDOz/xmt/6Nm9t7ZXYP6mVmXmW0xs7ui10eZ2f3ROn03uk0zZjYver09mr9sNvvdCDPrNbM7zOwpM3vSzN4X6r41s89E7+HHzexWMzskpH1rZt80s91m9nhsWt370swujMo/bWYXlmurUR0V6LEHVp8BrADWmNmK2e1V0w4Al7r7CmAV8GfROl0G3OPuy4F7otdQWPfl0b+Lgb86+F1u2qeBJ2OvrwW+5u7HAHsoPHQcYg8fB74Wles01wM/dvd3ASdQWO/g9q2Z9QP/BRhw9+Mo3Gq7+MD4UPbtt4DTS6bVtS/NbCHweQqP8TwZ+Hzxl0Aq3L1j/gHvAzbFXl8OXD7b/Up5Hf8P8FFgG7A4mrYY2Bb9fCOwJlZ+slwn/KPwxKt7gN8H7qLwfIOXgTml+xjYBLwv+nlOVM5mex3qWNfDgedK+xzivmXqucILo311F3BaaPsWWAY83ui+BNYAN8amTyvX7L+OOkKn/AOr03vcxyyL/uxcCdwPvM3dX4xmvQS8Lfq507fB14HPARPR67cAI+5+IHodX59pDx8Hig8f7xRHAcPA30WnmP7GzOYT4L519yHgfwAvAC9S2FcPEu6+Lap3X7Z0H3daoAfLzA4F/hH4r+7+m/g8L/wq7/jrS83sY8Bud39wtvtykMwB3gv8lbuvBF5n6k9yIKh9uwA4i8IvsSOA+cw8PRG0dtiXnRboSR5Y3XHMLE8hzP/B3e+MJv/azBZH8xcDu6PpnbwNTgVWm9nzwG0UTrtcD/RGDxeH6etzUB4+3kI7gZ3ufn/0+g4KAR/ivv0I8Jy7D7v7GHAnhf0d6r4tqndftnQfd1qgJ3lgdUcxMwP+FnjS3a+LzYo/ePtCCufWi9P/QzSKvgrYG/uTr625++XufqS7L6Ow737q7n8CbKbwcHGYua4tf/h4q7j7S8AOMys+1v3DwFYC3LcUTrWsMrOe6D1dXNcg921MvftyE/AHZrYg+qvmD6Jp6ZjtQYYGBiXOBH4JPANcMdv9SWF9fpfCn2mPAg9H/86kcD7xHuBp4P8CC6PyRuFKn2eAxyhcVTDr69HAen8QuCv6+WjgF8B24HvAvGj6IdHr7dH8o2e73w2s54nAYLR/1wMLQt23wDXAU8DjFB4aPy+kfQvcSmF8YIzCX18XNbIvgf8Yrfd24E/T7KO++i8iEohOO+UiIiIVKNBFRAKhQBcRCYQCXUQkEAp0EZFAKNBFRAKhQBcRCcT/B0TiHLuwWmkkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HuuiYUIjB-i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "2f295b98-9552-4319-e326-715e7f5fa4d0"
      },
      "source": [
        "probs = clf.predict_proba(X_test)  \n",
        "probs = probs[:, 1]  \n",
        "fper, tper, thresholds = roc_curve(y_test, probs) \n",
        "plt.plot(fper, tper)\n",
        "plt.plot([0,1], [0,1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Gradient Boosting ROC curve')\n",
        "plt.show()"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIx4TU_tuk3S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}