IBM_HR_Decision_Tree.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/HR-Employee-Attrition.csv\")"
      ],
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      "outputs": []
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        "data.head()"
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              "   Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
              "0   41       Yes      Travel_Rarely       1102                   Sales   \n",
              "1   49        No  Travel_Frequently        279  Research & Development   \n",
              "2   37       Yes      Travel_Rarely       1373  Research & Development   \n",
              "3   33        No  Travel_Frequently       1392  Research & Development   \n",
              "4   27        No      Travel_Rarely        591  Research & Development   \n",
              "\n",
              "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
              "0                 1          2  Life Sciences              1               1   \n",
              "1                 8          1  Life Sciences              1               2   \n",
              "2                 2          2          Other              1               4   \n",
              "3                 3          4  Life Sciences              1               5   \n",
              "4                 2          1        Medical              1               7   \n",
              "\n",
              "   ...  RelationshipSatisfaction StandardHours  StockOptionLevel  \\\n",
              "0  ...                         1            80                 0   \n",
              "1  ...                         4            80                 1   \n",
              "2  ...                         2            80                 0   \n",
              "3  ...                         3            80                 0   \n",
              "4  ...                         4            80                 1   \n",
              "\n",
              "   TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  YearsAtCompany  \\\n",
              "0                  8                      0               1               6   \n",
              "1                 10                      3               3              10   \n",
              "2                  7                      3               3               0   \n",
              "3                  8                      3               3               8   \n",
              "4                  6                      3               3               2   \n",
              "\n",
              "  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  \n",
              "0                  4                        0                     5  \n",
              "1                  7                        1                     7  \n",
              "2                  0                        0                     0  \n",
              "3                  7                        3                     0  \n",
              "4                  2                        2                     2  \n",
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          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_column = ['Attrition', 'BusinessTravel', 'Department', 'Gender', 'JobRole', 'MaritalStatus', 'OverTime','EducationField']\n",
        "encoder=LabelEncoder()\n",
        "data[categorical_column]=data[categorical_column].apply(encoder.fit_transform)"
      ],
      "metadata": {
        "id": "hE8Sl8As5wfU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y=data['Attrition']\n",
        "X=data.drop(['EmployeeCount','Attrition','EmployeeNumber','Over18','StandardHours'],axis=1)"
      ],
      "metadata": {
        "id": "fn3h4Du25whS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=4)"
      ],
      "metadata": {
        "id": "C8Geo1aM5wkI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dtreeviz\n",
        "import graphviz\n",
        "from dtreeviz.trees import *\n",
        "from subprocess import check_call\n",
        "from IPython.display import Image\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_validate\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz, export_text, plot_tree, _tree\n",
        "from sklearn.metrics import make_scorer, accuracy_score, f1_score, precision_score, recall_score, roc_auc_score, confusion_matrix, log_loss\n",
        "import matplotlib.font_manager"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7742MjZ5wmL",
        "outputId": "98395539-a761-46c4-d0a3-a028034b5c71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: dtreeviz in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: graphviz>=0.9 in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (0.20.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (1.5.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (1.23.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (1.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (3.7.1)\n",
            "Requirement already satisfied: colour in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (0.1.5)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install dtreeviz\n",
        "import dtreeviz\n",
        "from dtreeviz.trees import *"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPR9qXz87sdn",
        "outputId": "6648bef5-32c8-4dc2-8134-5801c5e929a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: dtreeviz in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: graphviz>=0.9 in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (0.20.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (1.5.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (1.23.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (1.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (3.7.1)\n",
            "Requirement already satisfied: colour in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (0.1.5)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.10/dist-packages (from dtreeviz) (7.4.3)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dtreeviz) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dtreeviz) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dtreeviz) (4.44.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dtreeviz) (1.4.5)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dtreeviz) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->dtreeviz) (3.1.1)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def decision_tree_maker(X_train, Y_train, X_test, Y_test, my_depth = 5):\n",
        "  dt = DecisionTreeClassifier(random_state = 0, max_depth = my_depth, criterion='entropy')\n",
        "  dt.fit(X_train, Y_train)\n",
        "  Y_pred = dt.predict(X_test)\n",
        "  dt_accuracy = accuracy_score(Y_test, Y_pred)\n",
        "  print('Acuuracy = ', dt_accuracy)\n",
        "  dt_f1 = f1_score(Y_test, Y_pred)\n",
        "  print('F1 Score = ', dt_f1)\n",
        "  dt_precision = precision_score(Y_test, Y_pred)\n",
        "  print('Precision = ', dt_precision)\n",
        "  dt_recall = recall_score(Y_test, Y_pred)\n",
        "  print('Recall = ', dt_recall)\n",
        "  dt_confusion_matrix = confusion_matrix(Y_test, Y_pred)\n",
        "  print('Confusion Matrix:\\n', dt_confusion_matrix)\n",
        "\n",
        "  features = ['Age', 'BusinessTravel', 'DailyRate', 'Department', 'DistanceFromHome', 'Education', 'EducationField', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobRole',  'JobSatisfaction', 'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',  'RelationshipSatisfaction', 'StockOptionLevel', 'TotalWorkingYears',  'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager']\n",
        "  classes = ['No', 'Yes']\n",
        "\n",
        "  text_representation = export_text(dt, feature_names=features)\n",
        "  print('------------ Decision Tree ----------')\n",
        "  print(text_representation)\n",
        "\n",
        "  np.warnings.filterwarnings('ignore', category=np.VisibleDeprecationWarning)\n",
        "  dot_data = export_graphviz(dt, out_file ='tree.dot', feature_names=features, class_names=classes)\n",
        "  check_call(['dot','-Tpng','tree.dot','-o','decision_tree.png'])\n",
        "\n"
      ],
      "metadata": {
        "id": "Yrway3nc6W-C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree_maker(X_train, y_train, X_test, y_test, 4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kaiIiw-87S3d",
        "outputId": "120835ca-1aff-432b-a475-f04f30406452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Acuuracy =  0.8605442176870748\n",
            "F1 Score =  0.36923076923076914\n",
            "Precision =  0.5454545454545454\n",
            "Recall =  0.27906976744186046\n",
            "Confusion Matrix:\n",
            " [[241  10]\n",
            " [ 31  12]]\n",
            "------------ Decision Tree ----------\n",
            "|--- OverTime <= 0.50\n",
            "|   |--- TotalWorkingYears <= 2.50\n",
            "|   |   |--- HourlyRate <= 55.50\n",
            "|   |   |   |--- EnvironmentSatisfaction <= 3.50\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |   |--- EnvironmentSatisfaction >  3.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |--- HourlyRate >  55.50\n",
            "|   |   |   |--- MaritalStatus <= 0.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- MaritalStatus >  0.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |--- TotalWorkingYears >  2.50\n",
            "|   |   |--- NumCompaniesWorked <= 4.50\n",
            "|   |   |   |--- WorkLifeBalance <= 1.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- WorkLifeBalance >  1.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |--- NumCompaniesWorked >  4.50\n",
            "|   |   |   |--- Age <= 37.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- Age >  37.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|--- OverTime >  0.50\n",
            "|   |--- MonthlyIncome <= 2475.00\n",
            "|   |   |--- DailyRate <= 1002.00\n",
            "|   |   |   |--- Age <= 36.00\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |   |--- Age >  36.00\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |--- DailyRate >  1002.00\n",
            "|   |   |   |--- YearsInCurrentRole <= 2.50\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |   |--- YearsInCurrentRole >  2.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |--- MonthlyIncome >  2475.00\n",
            "|   |   |--- StockOptionLevel <= 0.50\n",
            "|   |   |   |--- JobRole <= 6.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- JobRole >  6.50\n",
            "|   |   |   |   |--- class: 1\n",
            "|   |   |--- StockOptionLevel >  0.50\n",
            "|   |   |   |--- YearsInCurrentRole <= 7.50\n",
            "|   |   |   |   |--- class: 0\n",
            "|   |   |   |--- YearsInCurrentRole >  7.50\n",
            "|   |   |   |   |--- class: 0\n",
            "\n"
          ]
        }
      ]
    }
  ]
}