AutoViML.ipynb

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  "metadata": {
    "colab": {
      "name": "AutoViML.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/srivatsan88/YouTubeLI/blob/master/AutoViML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ydtu2LgJT7RP",
        "colab_type": "text"
      },
      "source": [
        "Link to supporting video for below notebook walkhthrough - https://www.youtube.com/watch?v=RqhTzEVkfBQ\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbHRfwu_CTJP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install autoviml"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNEGwU3oU00R",
        "colab_type": "code",
        "outputId": "709bc468-efff-4c16-ca7d-c22f5b764903",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "!pip install shap"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: shap in /usr/local/lib/python3.6/dist-packages (0.34.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from shap) (0.21.3)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.6/dist-packages (from shap) (4.28.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from shap) (1.3.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from shap) (0.25.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from shap) (1.17.4)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->shap) (0.14.1)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->shap) (2.6.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->shap) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->shap) (1.12.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYsohgWlDB8X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import sys,tempfile, urllib, os\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elkIXKcHC06h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BASE_DIR = '/tmp'\n",
        "OUTPUT_FILE = os.path.join(BASE_DIR, 'churn_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjq6fldrDI5b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "churn_data=urllib.request.urlretrieve('https://raw.githubusercontent.com/srivatsan88/YouTubeLI/master/dataset/WA_Fn-UseC_-Telco-Customer-Churn.csv', OUTPUT_FILE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esk0QhacDQEj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "churn_df = pd.read_csv(OUTPUT_FILE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwaOT3fCDVR8",
        "colab_type": "code",
        "outputId": "c40316d1-dcf8-43d9-fa63-fb2cfbac4612",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "churn_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn\n",
              "0  7590-VHVEG  Female              0  ...          29.85        29.85     No\n",
              "1  5575-GNVDE    Male              0  ...          56.95       1889.5     No\n",
              "2  3668-QPYBK    Male              0  ...          53.85       108.15    Yes\n",
              "3  7795-CFOCW    Male              0  ...          42.30      1840.75     No\n",
              "4  9237-HQITU  Female              0  ...          70.70       151.65    Yes\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAkXBnJkDYoB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "size = int(0.7*churn_df.shape[0])\n",
        "train_df = churn_df[:size]\n",
        "test_df = churn_df[size:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJEeNCNQEn2c",
        "colab_type": "code",
        "outputId": "2ade54e8-3b80-40a4-9fb7-064ba4760b08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "from autoviml.Auto_ViML import Auto_ViML"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Imported Auto_ViML version: 0.1.470. Call using: \n",
            "             m, feats, trainm, testm = Auto_ViML(train, target, test, \n",
            "                                    sample_submission='',\n",
            "                                    scoring_parameter='',\n",
            "                                    hyper_param='GS',feature_reduction=True,\n",
            "                                     Boosting_Flag=None,Binning_Flag=False,\n",
            "                                    Add_Poly=0, Stacking_Flag=False,Imbalanced_Flag=False, \n",
            "                                    verbose=0)\n",
            "            \n",
            "To remove previous versions, perform 'pip uninstall autoviml'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T3tKIdKGFUpQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target='Churn'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CT3wE4N7FNvf",
        "colab_type": "code",
        "outputId": "bd1e14b0-490a-4612-a7cc-fb9f4e30ff5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model, features, trainm, testm = Auto_ViML(train_df, target, test_df, sample_submission='',\n",
        "                                    scoring_parameter='',\n",
        "                                    hyper_param='RS',feature_reduction=True,\n",
        "                                     Boosting_Flag=True,Binning_Flag=False,\n",
        "                                    Add_Poly=0, Stacking_Flag=False, \n",
        "                                    Imbalanced_Flag=True, \n",
        "                                    verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train (Size: 4930,21) has Single_Label with target: ['Churn']\n",
            "\"\n",
            " ################### Binary-Class ##################### \" \n",
            "Shuffling the data set before training\n",
            " Class  -> Counts -> Percent\n",
            "   Yes:    1289  ->   26.1%\n",
            "    No:    3641  ->   73.9%\n",
            "Selecting Binary_Classification Classifier...\n",
            "    Using RandomizedSearchCV for Hyper Parameter Tuning. This will take time...\n",
            "String or Multi Class target: Churn transformed as follows: {'No': 0, 'Yes': 1}\n",
            "Classifying variables in data set...\n",
            "    Number of Numeric Columns =  1\n",
            "    Number of Integer-Categorical Columns =  1\n",
            "    Number of String-Categorical Columns =  10\n",
            "    Number of Factor-Categorical Columns =  0\n",
            "    Number of String-Boolean Columns =  5\n",
            "    Number of Numeric-Boolean Columns =  1\n",
            "    Number of Discrete String Columns =  1\n",
            "    Number of NLP String Columns =  0\n",
            "    Number of Date Time Columns =  0\n",
            "    Number of ID Columns =  1\n",
            "    Number of Columns to Delete =  0\n",
            "    20 Predictors classified...\n",
            "        This does not include the Target column(s)\n",
            "    1 variables removed since they were ID or low-information variables\n",
            "Test data has no missing values...\n",
            "Number of numeric variables = 3\n",
            "    No variables were removed since no highly correlated variables found in data\n",
            "    Feature Selection begins: currently 19 predictors\n",
            "\n",
            "Data Ready for Modeling with Churn as Target...\n",
            "Linear feature selection: out of 16 categorical features...\n",
            "    Selecting 14 categorical features\n",
            "Number of numeric variables = 3\n",
            "    No variables were removed since no highly correlated variables found in data\n",
            "    Adding 14 categorical variables to reduced numeric variables  of 3\n",
            "Current number of predictors = 17 \n",
            "    Finding Important Features using Boosted Trees algorithm...\n",
            "        using 17 variables...\n",
            "        using 14 variables...\n",
            "        using 11 variables...\n",
            "        using 8 variables...\n",
            "        using 5 variables...\n",
            "        using 2 variables...\n",
            "Found 17 important features\n",
            "    Features list: ['Contract', 'TechSupport', 'OnlineSecurity', 'InternetService', 'tenure', 'PaymentMethod', 'PaperlessBilling', 'MonthlyCharges', 'StreamingMovies', 'OnlineBackup', 'MultipleLines', 'StreamingTV', 'SeniorCitizen', 'TotalCharges', 'Dependents', 'DeviceProtection', 'Partner']\n",
            "Starting Feature Engineering now...\n",
            "    No Entropy Binning specified or there are no numeric vars in data set to Bin\n",
            "\n",
            "Rows in Train data set = 4437\n",
            "  Features in Train data set = 17\n",
            "    Rows in held-out data set = 493\n",
            "Finding Best Model and Hyper Parameters for Target: Churn...\n",
            "    Baseline Accuracy Needed for Model = 73.85%\n",
            "\n",
            "Using XGBoost Model, Estimated Training time = 2.5 mins\n",
            "\n",
            "Imbalanced Class Training using Majority Class Downsampling method...\n",
            "Rare Class = 1\n",
            "    Pct of Rare Class in data = 26.32%\n",
            "    Number of iterations for training =  1\n",
            "This is not an Imbalanced data set. Training in one batch...\n",
            "    Model training time (in seconds): 25\n",
            "5-fold Cross Validation  Accuracy = 58.4%\n",
            "    Best Parameters for Model = {'n_estimators': 700, 'max_depth': 2, 'learning_rate': 0.1, 'gamma': 0}\n",
            "Finding Best Threshold for Highest F1 Score...\n",
            "    Using threshold=0.5. However, 0.35 provides better F1=0.68 for rare class...\n",
            "XGBoost Model Prediction Results on Held Out CV Data Set:\n",
            "    Regular Accuracy Score = 82.6%\n",
            "    Balanced Accuracy Score = 72.3%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.92      0.89       372\n",
            "           1       0.69      0.52      0.59       121\n",
            "\n",
            "    accuracy                           0.83       493\n",
            "   macro avg       0.77      0.72      0.74       493\n",
            "weighted avg       0.82      0.83      0.82       493\n",
            "\n",
            "[[344  28]\n",
            " [ 58  63]]\n",
            "    Time taken for Ensembling: 2.3 seconds\n",
            "########################################################\n",
            "Completed Ensemble predictions on held out data\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4hIQEypYty9lnn02jRo0YO3YsPXr00MiZFDqacSCEbI5P4sXFmzmUkk7LGrrv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\nRKH/AUQYxkcqL/VXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Average precision-recall score: 0.77\n",
            "Macro F1 score, averaged over all classes: 0.74\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zqZP2hvZ5zy1bKpetv4zL1l921q+7euPVZahGkqqfIU1axdY0rql0CZKkORjS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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "After multiple models, Ensemble Model Results:\n",
            "    Balanced Accuracy Score = 73.116%\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.91      0.88       372\n",
            "           1       0.66      0.55      0.60       121\n",
            "\n",
            "    accuracy                           0.82       493\n",
            "   macro avg       0.76      0.73      0.74       493\n",
            "weighted avg       0.81      0.82      0.82       493\n",
            "\n",
            "[[338  34]\n",
            " [ 54  67]]\n",
            "\n",
            "Ensembling Models is better than Single Model for this data set.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k2Ck5qyqRUdDudLrnLyPrQD4rt2aHOyIRERE5ASk5q0rJQ4nbt5Hm7OEb3e9M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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "    Time taken for this Target (in seconds) = 31\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BLKbwfUU2B37cI5Fw26QyIcQ8YjFFGFpa05Ci60dXVTQqpjzqp9CesWToXgE1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EHTK7SwHZGytvGQppe1XZf9oJUlRaTcFdUt8tIn6QUkRSc/LuihdR9l7733KmA0Ir2q526RP21m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\ntg6wBfBY8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            "text/plain": [
              "<Figure size 576x597.6 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Plotting SHAP (SHapley Additive exPlanations) values to explain the output of model\n",
            "\n",
            "###################################################################################\n",
            "Training model on complete Train data and Predicting using give Test Data...\n",
            "    No Entropy Binning specified or there are no numeric vars in data set to Bin\n",
            "\n",
            "Imbalanced Class Training using Majority Class Downsampling method...\n",
            "Rare Class = 1\n",
            "    Pct of Rare Class in data = 26.15%\n",
            "    Number of iterations for training =  1\n",
            "This is not an Imbalanced data set. Training in one batch...\n",
            "Model Training time taken in seconds = 1\n",
            "Test Data predictions using Threshold = 0.5\n",
            "QuickML_Ensembling completed.\n",
            "    Time taken for Ensembling: 2.3 seconds\n",
            "########################################################\n",
            "Completed Ensemble predictions on held out data\n",
            "    Saving predictions to ./Churn/Churn_Binary_Classification_test_modified.csv\n",
            "    Saving predictions to ./Churn/Churn_Binary_Classification_submission.csv\n",
            "    Saving predictions to ./Churn/Churn_Binary_Classification_train_modified.csv\n",
            "Time Taken in mins = 0.7 for the Entire Process\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7N8KAMX07xuCpGuIUiTOrwwnaIUN6v9n56EHeFkGbd2mYU4KjBE1SU1QStCne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\nTQghhBDCZv4P7UFwPpQ1yxsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ8yj2fCK8yJ",
        "colab_type": "text"
      },
      "source": [
        "hyper_param: Tuning options are GridSearch ('GS') and RandomizedSearch ('RS'). Default is 'GS'.\n",
        "\n",
        "feature_reduction: Default = 'True' but it can be set to False if you don't want automatic    \n",
        "\n",
        "Boosting Flag: you have 4 possible choices (default is False):                               \n",
        "  None = This will build a Linear Model                                                  \n",
        "  False = This will build a Random Forest or Extra Trees model (also known as Bagging)        \n",
        "  True = This will build an XGBoost model                                                     \n",
        "  CatBoost = THis will build a CatBoost model (provided you have CatBoost installed)          \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjcoyRn-KGe4",
        "colab_type": "code",
        "outputId": "9e6f73c9-b541-412a-a488-4b67c407f35b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "features"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "['Contract', 'TechSupport', 'OnlineSecurity', 'InternetService', 'tenure', 'PaymentMethod', 'PaperlessBilling', 'MonthlyCharges', 'StreamingMovies', 'OnlineBackup', 'MultipleLines', 'StreamingTV', 'SeniorCitizen', 'TotalCharges', 'Dependents', 'DeviceProtection', 'Partner']"
            ],
            "text/plain": [
              "['Contract',\n",
              " 'TechSupport',\n",
              " 'OnlineSecurity',\n",
              " 'InternetService',\n",
              " 'tenure',\n",
              " 'PaymentMethod',\n",
              " 'PaperlessBilling',\n",
              " 'MonthlyCharges',\n",
              " 'StreamingMovies',\n",
              " 'OnlineBackup',\n",
              " 'MultipleLines',\n",
              " 'StreamingTV',\n",
              " 'SeniorCitizen',\n",
              " 'TotalCharges',\n",
              " 'Dependents',\n",
              " 'DeviceProtection',\n",
              " 'Partner']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wg-qo8naI9pU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmKtpTtbKmgL",
        "colab_type": "code",
        "outputId": "cae1aedd-0c67-4ba4-9479-b3108a21c364",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "source": [
        "testm"
      ],
      "execution_count": 0,
      "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>Contract</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Churn_proba_No</th>\n",
              "      <th>Churn_proba_Yes</th>\n",
              "      <th>Churn_Logistic Regression_predictions</th>\n",
              "      <th>Churn_Linear Discriminant_predictions</th>\n",
              "      <th>Churn_Naive Bayes_predictions</th>\n",
              "      <th>Churn_Bagging_predictions</th>\n",
              "      <th>Churn_XGBoost_predictions</th>\n",
              "      <th>Churn_Ensembled_predictions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4930</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.261692</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2190</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.436480</td>\n",
              "      <td>0.563520</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4931</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.847761</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4654</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.348952</td>\n",
              "      <td>0.651048</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4932</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>27</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.620896</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4655</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.901374</td>\n",
              "      <td>0.098626</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4933</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.387562</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4656</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.749450</td>\n",
              "      <td>0.250550</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4934</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>72</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.920398</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4657</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.918669</td>\n",
              "      <td>0.081331</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.662189</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6526</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936400</td>\n",
              "      <td>0.063600</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>72</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.845274</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6527</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.869285</td>\n",
              "      <td>0.130715</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.112935</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6528</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.789141</td>\n",
              "      <td>0.210859</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.558706</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6529</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.394023</td>\n",
              "      <td>0.605977</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.869652</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6530</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.920923</td>\n",
              "      <td>0.079077</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2113 rows × 25 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Contract  ...  Churn_Ensembled_predictions\n",
              "4930         0  ...                          Yes\n",
              "4931         0  ...                          Yes\n",
              "4932         2  ...                           No\n",
              "4933         0  ...                           No\n",
              "4934         1  ...                           No\n",
              "...        ...  ...                          ...\n",
              "7038         2  ...                           No\n",
              "7039         2  ...                           No\n",
              "7040         0  ...                           No\n",
              "7041         0  ...                          Yes\n",
              "7042         1  ...                           No\n",
              "\n",
              "[2113 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoqsH4LeLD8D",
        "colab_type": "code",
        "outputId": "66ecebca-37c9-4bc3-bb62-3b3cc3403900",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        " print(confusion_matrix(test_df[target].values,testm['Churn_XGBoost_predictions'].values))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1408  125]\n",
            " [ 307  273]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2rkWfPvOWUs",
        "colab_type": "code",
        "outputId": "a02b448b-359a-4133-8cea-d098890a9bc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        " print(confusion_matrix(test_df[target].values,testm['Churn_Logistic Regression_predictions'].values))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1405  128]\n",
            " [ 299  281]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWEcJG20TbcF",
        "colab_type": "code",
        "outputId": "dd3cbd23-0de5-4dc8-d784-059c7f2969a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        " print(confusion_matrix(test_df[target].values,testm['Churn_Ensembled_predictions'].values))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1376  157]\n",
            " [ 278  302]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IO2oYoHPLjWc",
        "colab_type": "code",
        "outputId": "ab46d2a0-bd99-4d0a-ecf6-7a896027edcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "print(classification_report(test_df[target].values,testm['Churn_XGBoost_predictions'].values))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          No       0.82      0.92      0.87      1533\n",
            "         Yes       0.69      0.47      0.56       580\n",
            "\n",
            "    accuracy                           0.80      2113\n",
            "   macro avg       0.75      0.69      0.71      2113\n",
            "weighted avg       0.78      0.80      0.78      2113\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TmezM_STsYq",
        "colab_type": "code",
        "outputId": "481efb4d-a01d-4de0-b24f-67d9758b58ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "print(classification_report(test_df[target].values,testm['Churn_Ensembled_predictions'].values))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          No       0.83      0.90      0.86      1533\n",
            "         Yes       0.66      0.52      0.58       580\n",
            "\n",
            "    accuracy                           0.79      2113\n",
            "   macro avg       0.74      0.71      0.72      2113\n",
            "weighted avg       0.78      0.79      0.79      2113\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}