Customer Churn Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.10"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 88.531396,
      "end_time": "2021-07-31T18:47:24.083065",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-07-31T18:45:55.551669",
      "version": "2.3.3"
    },
    "colab": {
      "name": "telco-customer-churn-7-model.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:05.253323Z",
          "iopub.status.busy": "2021-07-31T18:46:05.252639Z",
          "iopub.status.idle": "2021-07-31T18:46:05.264361Z",
          "shell.execute_reply": "2021-07-31T18:46:05.264969Z",
          "shell.execute_reply.started": "2021-07-31T18:45:19.155370Z"
        },
        "papermill": {
          "duration": 0.101058,
          "end_time": "2021-07-31T18:46:05.265233",
          "exception": false,
          "start_time": "2021-07-31T18:46:05.164175",
          "status": "completed"
        },
        "tags": [],
        "id": "d79981a3"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import seaborn as sns"
      ],
      "id": "d79981a3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4D9kRsI6Pry",
        "outputId": "f5cd9a5a-84a7-45ab-d498-9508d1c883ed"
      },
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive')"
      ],
      "id": "i4D9kRsI6Pry",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:05.935559Z",
          "iopub.status.busy": "2021-07-31T18:46:05.934817Z",
          "iopub.status.idle": "2021-07-31T18:46:06.174222Z",
          "shell.execute_reply": "2021-07-31T18:46:06.173681Z",
          "shell.execute_reply.started": "2021-07-31T18:45:19.176579Z"
        },
        "papermill": {
          "duration": 0.355576,
          "end_time": "2021-07-31T18:46:06.174387",
          "exception": false,
          "start_time": "2021-07-31T18:46:05.818811",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "59a35b70",
        "outputId": "3aa0e176-511e-415a-adcb-cfe3ce80784f"
      },
      "source": [
        "main_df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/WA_Fn-UseC_-Telco-Customer-Churn.csv')\n",
        "df = main_df.copy()\n",
        "df.head()"
      ],
      "id": "59a35b70",
      "execution_count": null,
      "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>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": {},
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "wTLjaxPI9H9U",
        "outputId": "ad1fab1c-0557-4952-fbd9-da1725930f9b"
      },
      "source": [
        "sns.heatmap(main_df.isnull())"
      ],
      "id": "wTLjaxPI9H9U",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f641769d810>"
            ]
          },
          "metadata": {},
          "execution_count": 340
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:06.622577Z",
          "iopub.status.busy": "2021-07-31T18:46:06.621978Z",
          "iopub.status.idle": "2021-07-31T18:46:09.305665Z",
          "shell.execute_reply": "2021-07-31T18:46:09.305094Z",
          "shell.execute_reply.started": "2021-07-31T18:45:19.350045Z"
        },
        "papermill": {
          "duration": 2.799052,
          "end_time": "2021-07-31T18:46:09.305809",
          "exception": false,
          "start_time": "2021-07-31T18:46:06.506757",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "4772c7be",
        "outputId": "fcffa55d-d1be-4364-c798-076a0e170d3c"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from plotly.offline import plot, iplot, init_notebook_mode\n",
        "init_notebook_mode(connected=True)\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "id": "4772c7be",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:09.982735Z",
          "iopub.status.busy": "2021-07-31T18:46:09.982057Z",
          "iopub.status.idle": "2021-07-31T18:46:09.985318Z",
          "shell.execute_reply": "2021-07-31T18:46:09.985804Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.449613Z"
        },
        "papermill": {
          "duration": 0.121798,
          "end_time": "2021-07-31T18:46:09.985979",
          "exception": false,
          "start_time": "2021-07-31T18:46:09.864181",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fed9cb69",
        "outputId": "cac18f3a-5924-4345-9643-77767b62b9d8"
      },
      "source": [
        "# Shape of dataframe\n",
        "\n",
        "df.shape"
      ],
      "id": "fed9cb69",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 342
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:10.216319Z",
          "iopub.status.busy": "2021-07-31T18:46:10.215536Z",
          "iopub.status.idle": "2021-07-31T18:46:10.220595Z",
          "shell.execute_reply": "2021-07-31T18:46:10.219914Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.456308Z"
        },
        "papermill": {
          "duration": 0.122844,
          "end_time": "2021-07-31T18:46:10.220736",
          "exception": false,
          "start_time": "2021-07-31T18:46:10.097892",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "76c0ff85",
        "outputId": "7b4fe44d-ccc0-4264-848e-c34ed90fa095"
      },
      "source": [
        "# List of all columns\n",
        "\n",
        "cols = list(df.columns)\n",
        "print(cols)"
      ],
      "id": "76c0ff85",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:10.463795Z",
          "iopub.status.busy": "2021-07-31T18:46:10.451164Z",
          "iopub.status.idle": "2021-07-31T18:46:10.485890Z",
          "shell.execute_reply": "2021-07-31T18:46:10.485194Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.473462Z"
        },
        "papermill": {
          "duration": 0.152065,
          "end_time": "2021-07-31T18:46:10.486234",
          "exception": false,
          "start_time": "2021-07-31T18:46:10.334169",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "33e6af58",
        "outputId": "e1b8f399-3828-4247-fd6b-0a1862ecd582"
      },
      "source": [
        "# Basic Information about the dataframe\n",
        "\n",
        "df.info()"
      ],
      "id": "33e6af58",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   object \n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(1), int64(2), object(18)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yjvdu9D37RAh",
        "outputId": "072dd329-32ca-4ee6-b8da-97ef5af8955d"
      },
      "source": [
        "for col in cols:\n",
        "  print('********************************************Description of the', col, 'column*******************************************')\n",
        "  display(df[col].describe())"
      ],
      "id": "yjvdu9D37RAh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the customerID column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count           7043\n",
              "unique          7043\n",
              "top       4654-GGUII\n",
              "freq               1\n",
              "Name: customerID, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the gender column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       2\n",
              "top       Male\n",
              "freq      3555\n",
              "Name: gender, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the SeniorCitizen column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count    7043.000000\n",
              "mean        0.162147\n",
              "std         0.368612\n",
              "min         0.000000\n",
              "25%         0.000000\n",
              "50%         0.000000\n",
              "75%         0.000000\n",
              "max         1.000000\n",
              "Name: SeniorCitizen, dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the Partner column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       2\n",
              "top         No\n",
              "freq      3641\n",
              "Name: Partner, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the Dependents column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       2\n",
              "top         No\n",
              "freq      4933\n",
              "Name: Dependents, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the tenure column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count    7043.000000\n",
              "mean       32.371149\n",
              "std        24.559481\n",
              "min         0.000000\n",
              "25%         9.000000\n",
              "50%        29.000000\n",
              "75%        55.000000\n",
              "max        72.000000\n",
              "Name: tenure, dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the PhoneService column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       2\n",
              "top        Yes\n",
              "freq      6361\n",
              "Name: PhoneService, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the MultipleLines column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       3\n",
              "top         No\n",
              "freq      3390\n",
              "Name: MultipleLines, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the InternetService column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count            7043\n",
              "unique              3\n",
              "top       Fiber optic\n",
              "freq             3096\n",
              "Name: InternetService, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the OnlineSecurity column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       3\n",
              "top         No\n",
              "freq      3498\n",
              "Name: OnlineSecurity, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the OnlineBackup column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       3\n",
              "top         No\n",
              "freq      3088\n",
              "Name: OnlineBackup, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the DeviceProtection column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       3\n",
              "top         No\n",
              "freq      3095\n",
              "Name: DeviceProtection, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the TechSupport column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       3\n",
              "top         No\n",
              "freq      3473\n",
              "Name: TechSupport, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the StreamingTV column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       3\n",
              "top         No\n",
              "freq      2810\n",
              "Name: StreamingTV, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the StreamingMovies column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       3\n",
              "top         No\n",
              "freq      2785\n",
              "Name: StreamingMovies, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the Contract column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count               7043\n",
              "unique                 3\n",
              "top       Month-to-month\n",
              "freq                3875\n",
              "Name: Contract, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the PaperlessBilling column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       2\n",
              "top        Yes\n",
              "freq      4171\n",
              "Name: PaperlessBilling, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the PaymentMethod column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count                 7043\n",
              "unique                   4\n",
              "top       Electronic check\n",
              "freq                  2365\n",
              "Name: PaymentMethod, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the MonthlyCharges column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count    7043.000000\n",
              "mean       64.761692\n",
              "std        30.090047\n",
              "min        18.250000\n",
              "25%        35.500000\n",
              "50%        70.350000\n",
              "75%        89.850000\n",
              "max       118.750000\n",
              "Name: MonthlyCharges, dtype: float64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the TotalCharges column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique    6531\n",
              "top           \n",
              "freq        11\n",
              "Name: TotalCharges, dtype: object"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "********************************************Description of the Churn column*******************************************\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "count     7043\n",
              "unique       2\n",
              "top         No\n",
              "freq      5174\n",
              "Name: Churn, dtype: object"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:10.636510Z",
          "iopub.status.busy": "2021-07-31T18:46:10.635982Z",
          "iopub.status.idle": "2021-07-31T18:46:10.683660Z",
          "shell.execute_reply": "2021-07-31T18:46:10.684026Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.516132Z"
        },
        "papermill": {
          "duration": 0.123461,
          "end_time": "2021-07-31T18:46:10.684165",
          "exception": false,
          "start_time": "2021-07-31T18:46:10.560704",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1d79963",
        "outputId": "7b211b23-fc7c-4d20-c3c3-2bd4583eb5e5"
      },
      "source": [
        "# List of all features with number of unique values present in them\n",
        "\n",
        "df.nunique()"
      ],
      "id": "e1d79963",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID          7043\n",
              "gender                 2\n",
              "SeniorCitizen          2\n",
              "Partner                2\n",
              "Dependents             2\n",
              "tenure                73\n",
              "PhoneService           2\n",
              "MultipleLines          3\n",
              "InternetService        3\n",
              "OnlineSecurity         3\n",
              "OnlineBackup           3\n",
              "DeviceProtection       3\n",
              "TechSupport            3\n",
              "StreamingTV            3\n",
              "StreamingMovies        3\n",
              "Contract               3\n",
              "PaperlessBilling       2\n",
              "PaymentMethod          4\n",
              "MonthlyCharges      1585\n",
              "TotalCharges        6531\n",
              "Churn                  2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 346
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:10.843013Z",
          "iopub.status.busy": "2021-07-31T18:46:10.840228Z",
          "iopub.status.idle": "2021-07-31T18:46:10.862965Z",
          "shell.execute_reply": "2021-07-31T18:46:10.863567Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.550536Z"
        },
        "papermill": {
          "duration": 0.103759,
          "end_time": "2021-07-31T18:46:10.863749",
          "exception": false,
          "start_time": "2021-07-31T18:46:10.759990",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "32146e93",
        "outputId": "e7f08217-12a7-42a5-d64e-ef209b4c8ed9"
      },
      "source": [
        "# Statistical measure of dataset\n",
        "\n",
        "df.describe()"
      ],
      "id": "32146e93",
      "execution_count": null,
      "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>SeniorCitizen</th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.162147</td>\n",
              "      <td>32.371149</td>\n",
              "      <td>64.761692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.368612</td>\n",
              "      <td>24.559481</td>\n",
              "      <td>30.090047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>35.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>70.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>89.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>118.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       SeniorCitizen       tenure  MonthlyCharges\n",
              "count    7043.000000  7043.000000     7043.000000\n",
              "mean        0.162147    32.371149       64.761692\n",
              "std         0.368612    24.559481       30.090047\n",
              "min         0.000000     0.000000       18.250000\n",
              "25%         0.000000     9.000000       35.500000\n",
              "50%         0.000000    29.000000       70.350000\n",
              "75%         0.000000    55.000000       89.850000\n",
              "max         1.000000    72.000000      118.750000"
            ]
          },
          "metadata": {},
          "execution_count": 347
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:11.119577Z",
          "iopub.status.busy": "2021-07-31T18:46:11.118835Z",
          "iopub.status.idle": "2021-07-31T18:46:11.123918Z",
          "shell.execute_reply": "2021-07-31T18:46:11.123273Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.573527Z"
        },
        "papermill": {
          "duration": 0.14591,
          "end_time": "2021-07-31T18:46:11.124053",
          "exception": false,
          "start_time": "2021-07-31T18:46:10.978143",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "81a570e5",
        "outputId": "386d5586-6274-40ad-ebb9-d653926c14e4"
      },
      "source": [
        "# We are dropping CustomerID because it will not make any contribution in prediction.\n",
        "\n",
        "df = df.drop('customerID', axis='columns')\n",
        "df.head()"
      ],
      "id": "81a570e5",
      "execution_count": null,
      "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>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>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>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>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>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>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": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0  Female              0     Yes  ...          29.85         29.85    No\n",
              "1    Male              0      No  ...          56.95        1889.5    No\n",
              "2    Male              0      No  ...          53.85        108.15   Yes\n",
              "3    Male              0      No  ...          42.30       1840.75    No\n",
              "4  Female              0      No  ...          70.70        151.65   Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:11.359677Z",
          "iopub.status.busy": "2021-07-31T18:46:11.359020Z",
          "iopub.status.idle": "2021-07-31T18:46:11.365447Z",
          "shell.execute_reply": "2021-07-31T18:46:11.364787Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.598924Z"
        },
        "papermill": {
          "duration": 0.126527,
          "end_time": "2021-07-31T18:46:11.365586",
          "exception": false,
          "start_time": "2021-07-31T18:46:11.239059",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc0d6596",
        "outputId": "f2b672c9-c68a-4a6d-e802-1ad78711f86f"
      },
      "source": [
        "# Here totalcharges are object types so we need to change it into numeric format\n",
        "df['TotalCharges'].dtype"
      ],
      "id": "cc0d6596",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:11.603894Z",
          "iopub.status.busy": "2021-07-31T18:46:11.603418Z",
          "iopub.status.idle": "2021-07-31T18:46:11.606494Z",
          "shell.execute_reply": "2021-07-31T18:46:11.606923Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.614258Z"
        },
        "papermill": {
          "duration": 0.125602,
          "end_time": "2021-07-31T18:46:11.607059",
          "exception": false,
          "start_time": "2021-07-31T18:46:11.481457",
          "status": "completed"
        },
        "tags": [],
        "id": "07d23c24"
      },
      "source": [
        "# Changing in numeric format\n",
        "\n",
        "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors=\"coerce\")"
      ],
      "id": "07d23c24",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:11.776141Z",
          "iopub.status.busy": "2021-07-31T18:46:11.771799Z",
          "iopub.status.idle": "2021-07-31T18:46:11.780337Z",
          "shell.execute_reply": "2021-07-31T18:46:11.779692Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.631140Z"
        },
        "papermill": {
          "duration": 0.099065,
          "end_time": "2021-07-31T18:46:11.780478",
          "exception": false,
          "start_time": "2021-07-31T18:46:11.681413",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93920a1d",
        "outputId": "080d65b4-2a59-4d82-ffab-c307f45a3c05"
      },
      "source": [
        "# Checking for null values \n",
        "\n",
        "df.isnull().sum()"
      ],
      "id": "93920a1d",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender               0\n",
              "SeniorCitizen        0\n",
              "Partner              0\n",
              "Dependents           0\n",
              "tenure               0\n",
              "PhoneService         0\n",
              "MultipleLines        0\n",
              "InternetService      0\n",
              "OnlineSecurity       0\n",
              "OnlineBackup         0\n",
              "DeviceProtection     0\n",
              "TechSupport          0\n",
              "StreamingTV          0\n",
              "StreamingMovies      0\n",
              "Contract             0\n",
              "PaperlessBilling     0\n",
              "PaymentMethod        0\n",
              "MonthlyCharges       0\n",
              "TotalCharges        11\n",
              "Churn                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 351
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:12.020965Z",
          "iopub.status.busy": "2021-07-31T18:46:12.020303Z",
          "iopub.status.idle": "2021-07-31T18:46:12.026946Z",
          "shell.execute_reply": "2021-07-31T18:46:12.026350Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.652732Z"
        },
        "papermill": {
          "duration": 0.130602,
          "end_time": "2021-07-31T18:46:12.027090",
          "exception": false,
          "start_time": "2021-07-31T18:46:11.896488",
          "status": "completed"
        },
        "tags": [],
        "id": "1c81bab0"
      },
      "source": [
        "# Dropping all the rows in which value in not known\n",
        "\n",
        "df.drop(df[df['TotalCharges'].isnull()].index, inplace=True)\n",
        "df.reset_index(drop=True, inplace=True)"
      ],
      "id": "1c81bab0",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:12.282651Z",
          "iopub.status.busy": "2021-07-31T18:46:12.281778Z",
          "iopub.status.idle": "2021-07-31T18:46:12.286437Z",
          "shell.execute_reply": "2021-07-31T18:46:12.285946Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.665487Z"
        },
        "papermill": {
          "duration": 0.14452,
          "end_time": "2021-07-31T18:46:12.286559",
          "exception": false,
          "start_time": "2021-07-31T18:46:12.142039",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "5a7f414d",
        "outputId": "3fd390c2-2d67-4e7c-fed5-a4e9aeccf2af"
      },
      "source": [
        "df.head()"
      ],
      "id": "5a7f414d",
      "execution_count": null,
      "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>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>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>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>1889.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>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>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": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0  Female              0     Yes  ...          29.85         29.85    No\n",
              "1    Male              0      No  ...          56.95       1889.50    No\n",
              "2    Male              0      No  ...          53.85        108.15   Yes\n",
              "3    Male              0      No  ...          42.30       1840.75    No\n",
              "4  Female              0      No  ...          70.70        151.65   Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 353
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:12.439855Z",
          "iopub.status.busy": "2021-07-31T18:46:12.439378Z",
          "iopub.status.idle": "2021-07-31T18:46:12.444704Z",
          "shell.execute_reply": "2021-07-31T18:46:12.445096Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.703026Z"
        },
        "papermill": {
          "duration": 0.083615,
          "end_time": "2021-07-31T18:46:12.445239",
          "exception": false,
          "start_time": "2021-07-31T18:46:12.361624",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c3052e1",
        "outputId": "a5bba167-eca4-49fe-a194-83bf316323d4"
      },
      "source": [
        "df.shape"
      ],
      "id": "6c3052e1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7032, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 354
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.074698,
          "end_time": "2021-07-31T18:46:12.595131",
          "exception": false,
          "start_time": "2021-07-31T18:46:12.520433",
          "status": "completed"
        },
        "tags": [],
        "id": "a321f45f"
      },
      "source": [
        "## Visualization"
      ],
      "id": "a321f45f"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:12.804577Z",
          "iopub.status.busy": "2021-07-31T18:46:12.804079Z",
          "iopub.status.idle": "2021-07-31T18:46:13.546845Z",
          "shell.execute_reply": "2021-07-31T18:46:13.546459Z",
          "shell.execute_reply.started": "2021-07-31T18:45:21.711067Z"
        },
        "papermill": {
          "duration": 0.834144,
          "end_time": "2021-07-31T18:46:13.546954",
          "exception": false,
          "start_time": "2021-07-31T18:46:12.712810",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "0cee6981",
        "outputId": "bb543006-7a0d-413f-c413-788b15ad22d3"
      },
      "source": [
        "# Visualization of null values\n",
        "\n",
        "sns.heatmap(df.isnull())"
      ],
      "id": "0cee6981",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f641754f5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 355
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:13.704031Z",
          "iopub.status.busy": "2021-07-31T18:46:13.702462Z",
          "iopub.status.idle": "2021-07-31T18:46:13.715973Z",
          "shell.execute_reply": "2021-07-31T18:46:13.715561Z",
          "shell.execute_reply.started": "2021-07-31T18:45:22.287717Z"
        },
        "papermill": {
          "duration": 0.093917,
          "end_time": "2021-07-31T18:46:13.716078",
          "exception": false,
          "start_time": "2021-07-31T18:46:13.622161",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "9acbf311",
        "outputId": "c28b577a-f618-46e5-b43d-b687ae8fd45d"
      },
      "source": [
        "# Checking Correlation \n",
        "\n",
        "df.corr()"
      ],
      "id": "9acbf311",
      "execution_count": null,
      "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>SeniorCitizen</th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015683</td>\n",
              "      <td>0.219874</td>\n",
              "      <td>0.102411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tenure</th>\n",
              "      <td>0.015683</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.246862</td>\n",
              "      <td>0.825880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <td>0.219874</td>\n",
              "      <td>0.246862</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.651065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TotalCharges</th>\n",
              "      <td>0.102411</td>\n",
              "      <td>0.825880</td>\n",
              "      <td>0.651065</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                SeniorCitizen    tenure  MonthlyCharges  TotalCharges\n",
              "SeniorCitizen        1.000000  0.015683        0.219874      0.102411\n",
              "tenure               0.015683  1.000000        0.246862      0.825880\n",
              "MonthlyCharges       0.219874  0.246862        1.000000      0.651065\n",
              "TotalCharges         0.102411  0.825880        0.651065      1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:13.876223Z",
          "iopub.status.busy": "2021-07-31T18:46:13.873808Z",
          "iopub.status.idle": "2021-07-31T18:46:14.165421Z",
          "shell.execute_reply": "2021-07-31T18:46:14.164856Z",
          "shell.execute_reply.started": "2021-07-31T18:45:22.302288Z"
        },
        "papermill": {
          "duration": 0.372312,
          "end_time": "2021-07-31T18:46:14.165563",
          "exception": false,
          "start_time": "2021-07-31T18:46:13.793251",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "8e37a258",
        "outputId": "b270fd34-127f-4a07-9655-e5e1a6dd3f60"
      },
      "source": [
        "# Visualizing correalation\n",
        "\n",
        "sns.heatmap(df.corr(), annot=True)"
      ],
      "id": "8e37a258",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f64174389d0>"
            ]
          },
          "metadata": {},
          "execution_count": 357
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:14.413556Z",
          "iopub.status.busy": "2021-07-31T18:46:14.412770Z",
          "iopub.status.idle": "2021-07-31T18:46:14.810333Z",
          "shell.execute_reply": "2021-07-31T18:46:14.809687Z",
          "shell.execute_reply.started": "2021-07-31T18:45:22.527412Z"
        },
        "papermill": {
          "duration": 0.526001,
          "end_time": "2021-07-31T18:46:14.810465",
          "exception": false,
          "start_time": "2021-07-31T18:46:14.284464",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "654bb2a1",
        "outputId": "8c0124da-15fe-4208-ac16-c07c585b0693"
      },
      "source": [
        "plt.figure(figsize=(5, 5))\n",
        "sns.barplot(data = df, y=\"TotalCharges\", x=\"Churn\")"
      ],
      "id": "654bb2a1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6417438cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 358
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:15.058881Z",
          "iopub.status.busy": "2021-07-31T18:46:15.058002Z",
          "iopub.status.idle": "2021-07-31T18:46:15.479860Z",
          "shell.execute_reply": "2021-07-31T18:46:15.479302Z",
          "shell.execute_reply.started": "2021-07-31T18:45:22.732002Z"
        },
        "papermill": {
          "duration": 0.547968,
          "end_time": "2021-07-31T18:46:15.480008",
          "exception": false,
          "start_time": "2021-07-31T18:46:14.932040",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "6fb17785",
        "outputId": "b81dcaf2-cce1-495e-eb4a-803731a956b2"
      },
      "source": [
        "sns.barplot(data = df, x=\"tenure\", y=\"Churn\")"
      ],
      "id": "6fb17785",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6417342f50>"
            ]
          },
          "metadata": {},
          "execution_count": 359
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.279702,
          "end_time": "2021-07-31T18:46:21.146571",
          "exception": false,
          "start_time": "2021-07-31T18:46:20.866869",
          "status": "completed"
        },
        "tags": [],
        "id": "1c2a9f11"
      },
      "source": [
        "### Preprocessing Data"
      ],
      "id": "1c2a9f11"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:21.713247Z",
          "iopub.status.busy": "2021-07-31T18:46:21.712640Z",
          "iopub.status.idle": "2021-07-31T18:46:21.717818Z",
          "shell.execute_reply": "2021-07-31T18:46:21.717241Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.417642Z"
        },
        "papermill": {
          "duration": 0.294362,
          "end_time": "2021-07-31T18:46:21.717965",
          "exception": false,
          "start_time": "2021-07-31T18:46:21.423603",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9d6e6408",
        "outputId": "84449c4e-cbcd-4a0e-9bb6-27bef3a6c010"
      },
      "source": [
        "print (df['Partner'].value_counts(ascending=True))"
      ],
      "id": "9d6e6408",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Yes    3393\n",
            "No     3639\n",
            "Name: Partner, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:22.278590Z",
          "iopub.status.busy": "2021-07-31T18:46:22.277902Z",
          "iopub.status.idle": "2021-07-31T18:46:22.299444Z",
          "shell.execute_reply": "2021-07-31T18:46:22.298796Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.432920Z"
        },
        "papermill": {
          "duration": 0.304227,
          "end_time": "2021-07-31T18:46:22.299578",
          "exception": false,
          "start_time": "2021-07-31T18:46:21.995351",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cf544d0",
        "outputId": "f5f48384-c6d3-4924-e8ea-375f471228ae"
      },
      "source": [
        "for i in df.columns:\n",
        "    if df[i].dtypes==\"object\":\n",
        "        print(f'{i} : {df[i].unique()}')\n",
        "        print(\"****************************************************\")"
      ],
      "id": "9cf544d0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender : ['Female' 'Male']\n",
            "****************************************************\n",
            "Partner : ['Yes' 'No']\n",
            "****************************************************\n",
            "Dependents : ['No' 'Yes']\n",
            "****************************************************\n",
            "PhoneService : ['No' 'Yes']\n",
            "****************************************************\n",
            "MultipleLines : ['No phone service' 'No' 'Yes']\n",
            "****************************************************\n",
            "InternetService : ['DSL' 'Fiber optic' 'No']\n",
            "****************************************************\n",
            "OnlineSecurity : ['No' 'Yes' 'No internet service']\n",
            "****************************************************\n",
            "OnlineBackup : ['Yes' 'No' 'No internet service']\n",
            "****************************************************\n",
            "DeviceProtection : ['No' 'Yes' 'No internet service']\n",
            "****************************************************\n",
            "TechSupport : ['No' 'Yes' 'No internet service']\n",
            "****************************************************\n",
            "StreamingTV : ['No' 'Yes' 'No internet service']\n",
            "****************************************************\n",
            "StreamingMovies : ['No' 'Yes' 'No internet service']\n",
            "****************************************************\n",
            "Contract : ['Month-to-month' 'One year' 'Two year']\n",
            "****************************************************\n",
            "PaperlessBilling : ['Yes' 'No']\n",
            "****************************************************\n",
            "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "****************************************************\n",
            "Churn : ['No' 'Yes']\n",
            "****************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.278497,
          "end_time": "2021-07-31T18:46:22.856568",
          "exception": false,
          "start_time": "2021-07-31T18:46:22.578071",
          "status": "completed"
        },
        "tags": [],
        "id": "51915dd1"
      },
      "source": [
        "* Above cell shows us the list of all features with their respective categorical variables"
      ],
      "id": "51915dd1"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:23.413537Z",
          "iopub.status.busy": "2021-07-31T18:46:23.412893Z",
          "iopub.status.idle": "2021-07-31T18:46:23.432476Z",
          "shell.execute_reply": "2021-07-31T18:46:23.432968Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.467449Z"
        },
        "papermill": {
          "duration": 0.298434,
          "end_time": "2021-07-31T18:46:23.433146",
          "exception": false,
          "start_time": "2021-07-31T18:46:23.134712",
          "status": "completed"
        },
        "tags": [],
        "id": "bf18be4a"
      },
      "source": [
        "df.replace('No internet service', 'No', inplace=True)\n",
        "df.replace('No phone service', 'No', inplace=True)"
      ],
      "id": "bf18be4a",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.278681,
          "end_time": "2021-07-31T18:46:23.989559",
          "exception": false,
          "start_time": "2021-07-31T18:46:23.710878",
          "status": "completed"
        },
        "tags": [],
        "id": "b2e1d1cc"
      },
      "source": [
        "* Replace long negative text by \"No\" for ease of access"
      ],
      "id": "b2e1d1cc"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:24.551902Z",
          "iopub.status.busy": "2021-07-31T18:46:24.550916Z",
          "iopub.status.idle": "2021-07-31T18:46:24.576846Z",
          "shell.execute_reply": "2021-07-31T18:46:24.576196Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.484238Z"
        },
        "papermill": {
          "duration": 0.308587,
          "end_time": "2021-07-31T18:46:24.576980",
          "exception": false,
          "start_time": "2021-07-31T18:46:24.268393",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67d188e1",
        "outputId": "4154f1ff-8b50-4445-b087-6323371352df"
      },
      "source": [
        "for i in df.columns:\n",
        "    if df[i].dtypes==\"object\":\n",
        "        print(f'{i} : {df[i].unique()}')\n",
        "        print(\"****************************************************\")"
      ],
      "id": "67d188e1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender : ['Female' 'Male']\n",
            "****************************************************\n",
            "Partner : ['Yes' 'No']\n",
            "****************************************************\n",
            "Dependents : ['No' 'Yes']\n",
            "****************************************************\n",
            "PhoneService : ['No' 'Yes']\n",
            "****************************************************\n",
            "MultipleLines : ['No' 'Yes']\n",
            "****************************************************\n",
            "InternetService : ['DSL' 'Fiber optic' 'No']\n",
            "****************************************************\n",
            "OnlineSecurity : ['No' 'Yes']\n",
            "****************************************************\n",
            "OnlineBackup : ['Yes' 'No']\n",
            "****************************************************\n",
            "DeviceProtection : ['No' 'Yes']\n",
            "****************************************************\n",
            "TechSupport : ['No' 'Yes']\n",
            "****************************************************\n",
            "StreamingTV : ['No' 'Yes']\n",
            "****************************************************\n",
            "StreamingMovies : ['No' 'Yes']\n",
            "****************************************************\n",
            "Contract : ['Month-to-month' 'One year' 'Two year']\n",
            "****************************************************\n",
            "PaperlessBilling : ['Yes' 'No']\n",
            "****************************************************\n",
            "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "****************************************************\n",
            "Churn : ['No' 'Yes']\n",
            "****************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.27889,
          "end_time": "2021-07-31T18:46:25.135475",
          "exception": false,
          "start_time": "2021-07-31T18:46:24.856585",
          "status": "completed"
        },
        "tags": [],
        "id": "1f9dd58e"
      },
      "source": [
        "* After Removing all long values we have above dataframe"
      ],
      "id": "1f9dd58e"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:25.701359Z",
          "iopub.status.busy": "2021-07-31T18:46:25.700688Z",
          "iopub.status.idle": "2021-07-31T18:46:25.704174Z",
          "shell.execute_reply": "2021-07-31T18:46:25.703512Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.508018Z"
        },
        "papermill": {
          "duration": 0.290378,
          "end_time": "2021-07-31T18:46:25.704359",
          "exception": false,
          "start_time": "2021-07-31T18:46:25.413981",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4319d4ec",
        "outputId": "b423a6b9-12a0-45f1-ddd4-f7e139067c8d"
      },
      "source": [
        "print(df['gender'].value_counts(ascending=True))"
      ],
      "id": "4319d4ec",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Female    3483\n",
            "Male      3549\n",
            "Name: gender, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:26.287496Z",
          "iopub.status.busy": "2021-07-31T18:46:26.286794Z",
          "iopub.status.idle": "2021-07-31T18:46:26.303561Z",
          "shell.execute_reply": "2021-07-31T18:46:26.303017Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.515609Z"
        },
        "papermill": {
          "duration": 0.316946,
          "end_time": "2021-07-31T18:46:26.303669",
          "exception": false,
          "start_time": "2021-07-31T18:46:25.986723",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "9eb98331",
        "outputId": "22742801-85be-4b99-cb87-440b83c28689"
      },
      "source": [
        "# Replaceing Male be 0 and Female by 1\n",
        "\n",
        "df['gender'].replace({'Female':1,'Male':0},inplace=True)\n",
        "df.head()"
      ],
      "id": "9eb98331",
      "execution_count": null,
      "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>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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>0</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>1889.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>1</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": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0       1              0     Yes  ...          29.85         29.85    No\n",
              "1       0              0      No  ...          56.95       1889.50    No\n",
              "2       0              0      No  ...          53.85        108.15   Yes\n",
              "3       0              0      No  ...          42.30       1840.75    No\n",
              "4       1              0      No  ...          70.70        151.65   Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 365
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.177167,
          "end_time": "2021-07-31T18:46:27.077875",
          "exception": false,
          "start_time": "2021-07-31T18:46:26.900708",
          "status": "completed"
        },
        "tags": [],
        "id": "0d6c8828"
      },
      "source": [
        "* There are few features in which categorical variables are more than two and they are not \"Yes\" or \"No\" types"
      ],
      "id": "0d6c8828"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:27.441620Z",
          "iopub.status.busy": "2021-07-31T18:46:27.441047Z",
          "iopub.status.idle": "2021-07-31T18:46:27.460544Z",
          "shell.execute_reply": "2021-07-31T18:46:27.459901Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.562898Z"
        },
        "papermill": {
          "duration": 0.203783,
          "end_time": "2021-07-31T18:46:27.460671",
          "exception": false,
          "start_time": "2021-07-31T18:46:27.256888",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9974b95",
        "outputId": "ac72d8b1-cb08-4e12-ed3f-1d2d6ee8c57c"
      },
      "source": [
        "for i in df.columns:\n",
        "    if (len(df[i].unique()) >2) & (df[i].dtypes != \"int64\") &(df[i].dtypes!= \"float64\"):\n",
        "        print(i)"
      ],
      "id": "e9974b95",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "InternetService\n",
            "Contract\n",
            "PaymentMethod\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.177237,
          "end_time": "2021-07-31T18:46:27.812472",
          "exception": false,
          "start_time": "2021-07-31T18:46:27.635235",
          "status": "completed"
        },
        "tags": [],
        "id": "eb271c2c"
      },
      "source": [
        "* Above 3 Features column in dataset have more than 2 categorical values"
      ],
      "id": "eb271c2c"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:28.170585Z",
          "iopub.status.busy": "2021-07-31T18:46:28.169891Z",
          "iopub.status.idle": "2021-07-31T18:46:28.174308Z",
          "shell.execute_reply": "2021-07-31T18:46:28.173733Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.583503Z"
        },
        "papermill": {
          "duration": 0.187611,
          "end_time": "2021-07-31T18:46:28.174515",
          "exception": false,
          "start_time": "2021-07-31T18:46:27.986904",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57797aad",
        "outputId": "b1a37a75-8308-4718-de36-f2b91dcff7f6"
      },
      "source": [
        "print(df['InternetService'].value_counts(ascending=True))"
      ],
      "id": "57797aad",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No             1520\n",
            "DSL            2416\n",
            "Fiber optic    3096\n",
            "Name: InternetService, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:28.753990Z",
          "iopub.status.busy": "2021-07-31T18:46:28.753372Z",
          "iopub.status.idle": "2021-07-31T18:46:28.757825Z",
          "shell.execute_reply": "2021-07-31T18:46:28.758231Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.591366Z"
        },
        "papermill": {
          "duration": 0.29564,
          "end_time": "2021-07-31T18:46:28.758409",
          "exception": false,
          "start_time": "2021-07-31T18:46:28.462769",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50766a08",
        "outputId": "08493081-7d33-46d6-f2ee-8cd61103e372"
      },
      "source": [
        "print(df['Contract'].value_counts(ascending=True))"
      ],
      "id": "50766a08",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "One year          1472\n",
            "Two year          1685\n",
            "Month-to-month    3875\n",
            "Name: Contract, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:29.122423Z",
          "iopub.status.busy": "2021-07-31T18:46:29.121712Z",
          "iopub.status.idle": "2021-07-31T18:46:29.124654Z",
          "shell.execute_reply": "2021-07-31T18:46:29.125160Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.604028Z"
        },
        "papermill": {
          "duration": 0.188789,
          "end_time": "2021-07-31T18:46:29.125364",
          "exception": false,
          "start_time": "2021-07-31T18:46:28.936575",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ea31cfdb",
        "outputId": "6d8c0fad-99e5-4a6f-c8f7-de61bf66edc9"
      },
      "source": [
        "print(df['PaymentMethod'].value_counts(ascending=True))"
      ],
      "id": "ea31cfdb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Credit card (automatic)      1521\n",
            "Bank transfer (automatic)    1542\n",
            "Mailed check                 1604\n",
            "Electronic check             2365\n",
            "Name: PaymentMethod, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.288626,
          "end_time": "2021-07-31T18:46:30.282946",
          "exception": false,
          "start_time": "2021-07-31T18:46:29.994320",
          "status": "completed"
        },
        "tags": [],
        "id": "bc95f335"
      },
      "source": [
        "* To deal with such kind of feature column we are required to use one-hot encoding."
      ],
      "id": "bc95f335"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:30.865697Z",
          "iopub.status.busy": "2021-07-31T18:46:30.864851Z",
          "iopub.status.idle": "2021-07-31T18:46:30.888090Z",
          "shell.execute_reply": "2021-07-31T18:46:30.888663Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.633377Z"
        },
        "papermill": {
          "duration": 0.317029,
          "end_time": "2021-07-31T18:46:30.888872",
          "exception": false,
          "start_time": "2021-07-31T18:46:30.571843",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4d4e4b4",
        "outputId": "90356a38-4881-46f6-b694-67eadb204991"
      },
      "source": [
        "more_than_2 = ['InternetService' ,'Contract' ,'PaymentMethod']\n",
        "df = pd.get_dummies(data=df, columns= more_than_2, drop_first=True)\n",
        "df.dtypes"
      ],
      "id": "b4d4e4b4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                     int64\n",
              "SeniorCitizen                              int64\n",
              "Partner                                   object\n",
              "Dependents                                object\n",
              "tenure                                     int64\n",
              "PhoneService                              object\n",
              "MultipleLines                             object\n",
              "OnlineSecurity                            object\n",
              "OnlineBackup                              object\n",
              "DeviceProtection                          object\n",
              "TechSupport                               object\n",
              "StreamingTV                               object\n",
              "StreamingMovies                           object\n",
              "PaperlessBilling                          object\n",
              "MonthlyCharges                           float64\n",
              "TotalCharges                             float64\n",
              "Churn                                     object\n",
              "InternetService_Fiber optic                uint8\n",
              "InternetService_No                         uint8\n",
              "Contract_One year                          uint8\n",
              "Contract_Two year                          uint8\n",
              "PaymentMethod_Credit card (automatic)      uint8\n",
              "PaymentMethod_Electronic check             uint8\n",
              "PaymentMethod_Mailed check                 uint8\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 370
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:31.454643Z",
          "iopub.status.busy": "2021-07-31T18:46:31.453979Z",
          "iopub.status.idle": "2021-07-31T18:46:31.460644Z",
          "shell.execute_reply": "2021-07-31T18:46:31.460038Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.657428Z"
        },
        "papermill": {
          "duration": 0.290051,
          "end_time": "2021-07-31T18:46:31.460793",
          "exception": false,
          "start_time": "2021-07-31T18:46:31.170742",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bf65575",
        "outputId": "88f20706-44ed-4c8a-8f43-5b547f2d3f39"
      },
      "source": [
        "df.shape"
      ],
      "id": "7bf65575",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7032, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 371
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:32.036827Z",
          "iopub.status.busy": "2021-07-31T18:46:32.036049Z",
          "iopub.status.idle": "2021-07-31T18:46:32.041578Z",
          "shell.execute_reply": "2021-07-31T18:46:32.040964Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.662448Z"
        },
        "papermill": {
          "duration": 0.296159,
          "end_time": "2021-07-31T18:46:32.041733",
          "exception": false,
          "start_time": "2021-07-31T18:46:31.745574",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94259f92",
        "outputId": "7e9141d1-cf2c-4193-90fc-fbb0fcf135b9"
      },
      "source": [
        "df.columns"
      ],
      "id": "94259f92",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
              "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
              "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
              "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
              "       'InternetService_Fiber optic', 'InternetService_No',\n",
              "       'Contract_One year', 'Contract_Two year',\n",
              "       'PaymentMethod_Credit card (automatic)',\n",
              "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 372
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:32.618919Z",
          "iopub.status.busy": "2021-07-31T18:46:32.618026Z",
          "iopub.status.idle": "2021-07-31T18:46:32.621073Z",
          "shell.execute_reply": "2021-07-31T18:46:32.621618Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.678519Z"
        },
        "papermill": {
          "duration": 0.296784,
          "end_time": "2021-07-31T18:46:32.621799",
          "exception": false,
          "start_time": "2021-07-31T18:46:32.325015",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "412b1391",
        "outputId": "441be2f0-e261-46f2-dd7a-84de0e09bd12"
      },
      "source": [
        "for i in df.columns:\n",
        "    if (df[i].dtypes == \"int64\")  | (df[i].dtypes== \"float64\"):\n",
        "        print(i)"
      ],
      "id": "412b1391",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender\n",
            "SeniorCitizen\n",
            "tenure\n",
            "MonthlyCharges\n",
            "TotalCharges\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.284162,
          "end_time": "2021-07-31T18:46:33.188355",
          "exception": false,
          "start_time": "2021-07-31T18:46:32.904193",
          "status": "completed"
        },
        "tags": [],
        "id": "c84c4c1e"
      },
      "source": [
        "* Above feature column have numerical data so we will need to bring it into a particular range if they varies a lot"
      ],
      "id": "c84c4c1e"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:33.828357Z",
          "iopub.status.busy": "2021-07-31T18:46:33.827609Z",
          "iopub.status.idle": "2021-07-31T18:46:33.960985Z",
          "shell.execute_reply": "2021-07-31T18:46:33.961830Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.694186Z"
        },
        "papermill": {
          "duration": 0.490116,
          "end_time": "2021-07-31T18:46:33.962004",
          "exception": false,
          "start_time": "2021-07-31T18:46:33.471888",
          "status": "completed"
        },
        "tags": [],
        "id": "17aa75cd"
      },
      "source": [
        "# Using MinMaxScaler of Feature Scaling\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()"
      ],
      "id": "17aa75cd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.282444,
          "end_time": "2021-07-31T18:46:34.527154",
          "exception": false,
          "start_time": "2021-07-31T18:46:34.244710",
          "status": "completed"
        },
        "tags": [],
        "id": "a005f58b"
      },
      "source": [
        "* Here we will consider gender and seniorcitizen because because their values already present in a small scale of 0 or 1\n",
        "* So we will consider only \"tenure\", \"MonthlyCharges\", \"TotalCharges\""
      ],
      "id": "a005f58b"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:35.096998Z",
          "iopub.status.busy": "2021-07-31T18:46:35.096353Z",
          "iopub.status.idle": "2021-07-31T18:46:35.113710Z",
          "shell.execute_reply": "2021-07-31T18:46:35.113179Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.921697Z"
        },
        "papermill": {
          "duration": 0.304467,
          "end_time": "2021-07-31T18:46:35.113850",
          "exception": false,
          "start_time": "2021-07-31T18:46:34.809383",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "90d40764",
        "outputId": "32604537-d51d-41a5-bb32-2faa1873afb8"
      },
      "source": [
        "large_cols = [\"tenure\", \"MonthlyCharges\", \"TotalCharges\"]\n",
        "df[large_cols] = scaler.fit_transform(df[large_cols])\n",
        "df[large_cols].head()"
      ],
      "id": "90d40764",
      "execution_count": null,
      "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>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.115423</td>\n",
              "      <td>0.001275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.464789</td>\n",
              "      <td>0.385075</td>\n",
              "      <td>0.215867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.014085</td>\n",
              "      <td>0.354229</td>\n",
              "      <td>0.010310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.619718</td>\n",
              "      <td>0.239303</td>\n",
              "      <td>0.210241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.014085</td>\n",
              "      <td>0.521891</td>\n",
              "      <td>0.015330</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     tenure  MonthlyCharges  TotalCharges\n",
              "0  0.000000        0.115423      0.001275\n",
              "1  0.464789        0.385075      0.215867\n",
              "2  0.014085        0.354229      0.010310\n",
              "3  0.619718        0.239303      0.210241\n",
              "4  0.014085        0.521891      0.015330"
            ]
          },
          "metadata": {},
          "execution_count": 375
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:35.688745Z",
          "iopub.status.busy": "2021-07-31T18:46:35.688090Z",
          "iopub.status.idle": "2021-07-31T18:46:35.713660Z",
          "shell.execute_reply": "2021-07-31T18:46:35.713001Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.945182Z"
        },
        "papermill": {
          "duration": 0.311517,
          "end_time": "2021-07-31T18:46:35.713803",
          "exception": false,
          "start_time": "2021-07-31T18:46:35.402286",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "884b9998",
        "outputId": "ee64e992-3bc5-40ec-d97e-2413d7dbb521"
      },
      "source": [
        "# After feature scaling we have following dataset\n",
        "\n",
        "df.head()"
      ],
      "id": "884b9998",
      "execution_count": null,
      "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>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>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>Yes</td>\n",
              "      <td>0.115423</td>\n",
              "      <td>0.001275</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0.464789</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</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>No</td>\n",
              "      <td>0.385075</td>\n",
              "      <td>0.215867</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0.014085</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>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.354229</td>\n",
              "      <td>0.010310</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0.619718</td>\n",
              "      <td>No</td>\n",
              "      <td>No</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>No</td>\n",
              "      <td>0.239303</td>\n",
              "      <td>0.210241</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>0.014085</td>\n",
              "      <td>Yes</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>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0.521891</td>\n",
              "      <td>0.015330</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  ...  PaymentMethod_Mailed check\n",
              "0       1  ...                           0\n",
              "1       0  ...                           1\n",
              "2       0  ...                           1\n",
              "3       0  ...                           0\n",
              "4       1  ...                           0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 376
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:36.291098Z",
          "iopub.status.busy": "2021-07-31T18:46:36.289580Z",
          "iopub.status.idle": "2021-07-31T18:46:36.297496Z",
          "shell.execute_reply": "2021-07-31T18:46:36.297975Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.965226Z"
        },
        "papermill": {
          "duration": 0.298617,
          "end_time": "2021-07-31T18:46:36.298183",
          "exception": false,
          "start_time": "2021-07-31T18:46:35.999566",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8a17b17",
        "outputId": "653beda4-a3b3-4bcb-8edb-78a1977dd8c3"
      },
      "source": [
        "for i in df.columns:\n",
        "    if (df[i].dtypes == \"object\"):\n",
        "        print(i)"
      ],
      "id": "f8a17b17",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Partner\n",
            "Dependents\n",
            "PhoneService\n",
            "MultipleLines\n",
            "OnlineSecurity\n",
            "OnlineBackup\n",
            "DeviceProtection\n",
            "TechSupport\n",
            "StreamingTV\n",
            "StreamingMovies\n",
            "PaperlessBilling\n",
            "Churn\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.285229,
          "end_time": "2021-07-31T18:46:36.871183",
          "exception": false,
          "start_time": "2021-07-31T18:46:36.585954",
          "status": "completed"
        },
        "tags": [],
        "id": "ed7ee093"
      },
      "source": [
        "* All the column present above have object type of dataset in them i.e here only \"Yes\" or \"No\""
      ],
      "id": "ed7ee093"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:37.455870Z",
          "iopub.status.busy": "2021-07-31T18:46:37.453712Z",
          "iopub.status.idle": "2021-07-31T18:46:37.542559Z",
          "shell.execute_reply": "2021-07-31T18:46:37.541906Z",
          "shell.execute_reply.started": "2021-07-31T18:45:24.982383Z"
        },
        "papermill": {
          "duration": 0.388501,
          "end_time": "2021-07-31T18:46:37.542693",
          "exception": false,
          "start_time": "2021-07-31T18:46:37.154192",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "6904aab0",
        "outputId": "77f86cef-1c8c-4093-da9d-29f9283a273e"
      },
      "source": [
        "two_cate = ['Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'PaperlessBilling', 'Churn']\n",
        "for i in two_cate:\n",
        "    df[i].replace({\"No\":0, \"Yes\":1}, inplace=True)\n",
        "df.head()"
      ],
      "id": "6904aab0",
      "execution_count": null,
      "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>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>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.115423</td>\n",
              "      <td>0.001275</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.464789</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.385075</td>\n",
              "      <td>0.215867</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.014085</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.354229</td>\n",
              "      <td>0.010310</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.619718</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.239303</td>\n",
              "      <td>0.210241</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.014085</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.521891</td>\n",
              "      <td>0.015330</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  ...  PaymentMethod_Mailed check\n",
              "0       1  ...                           0\n",
              "1       0  ...                           1\n",
              "2       0  ...                           1\n",
              "3       0  ...                           0\n",
              "4       1  ...                           0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IeiumF2kKSLZ",
        "outputId": "6a1d289d-6084-4c4e-9e97-7307fbd81c09"
      },
      "source": [
        "print(list(df.columns.values))"
      ],
      "id": "IeiumF2kKSLZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn', 'InternetService_Fiber optic', 'InternetService_No', 'Contract_One year', 'Contract_Two year', 'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KiMchAJLDNZ"
      },
      "source": [
        "two_cat = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'PaperlessBilling', 'Churn', 'InternetService_Fiber optic', 'InternetService_No', 'Contract_One year', 'Contract_Two year', 'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check']"
      ],
      "id": "3KiMchAJLDNZ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R0LV4FnxKMLV",
        "outputId": "a0d494ba-07d5-4656-bf23-df408eba12ca"
      },
      "source": [
        "for col in two_cat:\n",
        "  plt.figure(figsize=(5, 5))\n",
        "  sns.barplot(data = df, y=col, x=\"Churn\")"
      ],
      "id": "R0LV4FnxKMLV",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAAE9CAYAAABdgjpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAS/ElEQVR4nO3df6yfd1338eeL7u4WQXCwE6fb+mNQxaILyw5FbwRuZYyS6OaPETqzOGTaaJx3uFEXEN205s49C7kFkxlXoQFJdMxN8RhLxsIGGhVoC8joyKQrsLWx7kfnmGxunO3tH9/ryJfjWc/3fNqr3/M9fT6Sk17X58e375OcvPK5rut7XVeqCknS0j1r3AVI0qQyQCWpkQEqSY0MUElqZIBKUiMDVJIanTLuAo6XM844o9atWzfuMiStMHv37n2wqqYW6lsxAbpu3Tr27Nkz7jIkrTBJvvpMfR7CS1IjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIarZhbOSUdm6uvvprDhw9z5plnsn379nGXMxEMUEkAHD58mEOHDo27jIniIbwkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY16fSdSks3Ae4BVwHur6rp5/W8Ffh6YBR4A3lxVX+36ngLu7IbeW1UX91mrNIp7t/3AuEvozeyR5wOnMHvkqyv291xzzZ2LD1qC3gI0ySrgeuC1wEFgd5KZqrpraNhngemqeizJLwHbgTd2fY9X1Uv7qk+SjlWfh/CbgP1VdaCqngRuBC4ZHlBVd1TVY93uJ4Gze6xHko6rPgP0LOC+of2DXdszuRL4yND+aUn2JPlkkp/oo0BJOhbL4r3wSS4HpoFXDzWvrapDSc4Fbk9yZ1XdM2/eVmArwJo1a05YvZIE/a5ADwHnDO2f3bV9iyQXAu8ALq6qJ+baq+pQ9+8B4OPA+fPnVtWOqpququmpqanjW70kLaLPAN0NbEiyPslqYAswMzwgyfnADQzC8/6h9tOTnNptnwG8Ahi++CRJY9fbIXxVzSa5CriVwdeYdlbVviTbgD1VNQO8E3gO8OdJ4JtfV/o+4IYkTzMI+evmXb2XpLHr9RxoVe0Cds1ru2Zo+8JnmPcPwMr8IpqkFcM7kSSpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJamRASpJjZbF4+wkjd8Zpz0NzHb/ahQGqCQAfu28fxt3CRPHQ3hJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlKjXgM0yeYkdyfZn+RtC/S/NcldST6f5GNJ1g71XZHkS93PFX3WKUktegvQJKuA64HXAxuBy5JsnDfss8B0VZ0H3Axs7+Y+H7gWeDmwCbg2yel91SpJLfpcgW4C9lfVgap6ErgRuGR4QFXdUVWPdbufBM7utl8H3FZVR6rqYeA2YHOPtUrSkvUZoGcB9w3tH+zansmVwEca50rSCXfKuAsASHI5MA28eonztgJbAdasWdNDZZL0zPpcgR4CzhnaP7tr+xZJLgTeAVxcVU8sZW5V7aiq6aqanpqaOm6FS9Io+gzQ3cCGJOuTrAa2ADPDA5KcD9zAIDzvH+q6FbgoyendxaOLujZJWjZ6O4SvqtkkVzEIvlXAzqral2QbsKeqZoB3As8B/jwJwL1VdXFVHUnyuwxCGGBbVR3pq1ZJatHrOdCq2gXsmtd2zdD2hUeZuxPY2V91knRsvBNJkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIajRSgSX4qyZeSPJLka0keTfK1vouTpOVs1Fd6bAd+vKq+2GcxkjRJRj2E/1fDU5K+1agr0D1JPgR8GJh7dztV9Re9VCVJE2DUAH0u8BiD97PPKcAAlXTSGilAq+rn+i5EkibNqFfhvyfJx5J8ods/L8lv9luaJC1vo15E+mPg7cA3AKrq88CWvoqSpEkwaoB+W1V9el7b7PEuRpImyagB+mCSFzK4cESSS4F/6a0qSZoAo16F/2VgB/DiJIeALwOX91aVJE2AUa/CHwAuTPJs4FlV9Wi/ZUnS8jfqVfinklwHPDYXnkk+02tlkrTMjXoOdF839qNJnt+1pZ+SJGkyjBqgs1V1NfBe4O+SXEB3QUmSTlajXkQKQFV9KMk+4E+BNb1VJUkTYNQA/fm5jar6QpJXApf0U5IkTYajBmiSH62q24G1SdbO6/73/sqSpOVvsRXoq4HbgR9foM+nMUk6qR01QKvq2m5zW1V9ebgvyfreqpKkCTDqVfhbFmi7+XgWIkmTZrFzoC8GXgI8L8lPDXU9FzhtsQ9Pshl4D7AKeG9VXTev/1XAu4HzgC1VdfNQ31PAnd3uvVV18eK/jiSdOIudA/1e4MeA7+Bbz4M+CvzC0SYmWQVcD7wWOAjsTjJTVXcNDbsXeBPwawt8xONV9dJF6pOksVnsHOhfAX+V5Ieq6h+X+NmbgP3dffQkuZHBV5/+K0Cr6itd39NL/GxJGrvFDuGvrqrtwM8kuWx+f1X976NMPwu4b2j/IPDyJdR2WpI9DJ47el1VfXgJcyWpd4sdws+9ynhP34UsYG1VHUpyLnB7kjur6p7hAUm2AlsB1qzxxihJJ9ZiAXpbkqmq+sBwY5IpBudBj+YQcM7Q/tld20iq6lD374EkHwfOB+6ZN2YHg+eUMj097b35kk6oxb7G9AfAKxdo/2Hg9xeZuxvYkGR9ktUM3qE0M0pRSU5Pcmq3fQbwCobOnUrScrBYgF5QVf/tbqOq+kvgVUebWFWzwFXArQxOBdxUVfuSbEtyMUCSlyU5CLwBuKF7UAnA9wF7kvwTcAeDc6AGqKRlZbFD+G87St+iX8Kvql3Arnlt1wxt72ZwaD9/3j8AP7DY50vSOC0Wgvcn2TS/McnLgAf6KUmSJsNiK9BfB25K8n5gb9c2Dfwsvhde0knuqCvQ7l3wmxg8UPlN3U+Al1fVp/ouTpKWs1EeqPwQ8KKq+um+i5GkSTLKhaCnGDxQefUJqEeSJsaor/Q4APx9khng63ONVfX/e6lKkibAqAF6T/fzLODb+ytHx9PVV1/N4cOHOfPMM9m+ffu4y5FWnJECtKp+ByDJc7p934c0AQ4fPsyhQyPfPStpiUZ6In2S70/yWWAfsC/J3iQv6bc0SVreRn2lxw7grVW1tqrWAr8K/HF/ZUnS8jdqgD67qu6Y26mqjwPP7qUiSZoQI1+FT/JbwAe7/csZXJmXpJPWqCvQNwNTDN4D/xfd9pv7KkqSJsGoV+EfBo72+g5JOuks9k6kd1fVW5L8NfDfnvjuq4YlncwWW4HOnfN8V9+FjMsFv/4n4y6hN9/+4KOsAu598NEV+3vufefPjrsEncQWe63x3u7fT5yYciRpcox0DjTJK4DfBtZ2cwJUVZ3bX2mStLyN+jWm9wH/h8FDlZ/qrxxJmhyjBugjVfWRXiuRpAkzaoDekeSdDL4D+sRcY1V9ppeqJGkCjBqgL+/+nR5qK+BHj285kjQ5Rv0i/Y/0XYgkTZpRH2f3nUnel+Qj3f7GJFf2W5okLW+j3gv/fuBW4Lu7/X8G3tJHQTp+nl79bJ469bk8vdoHZ0l9GPUc6BlVdVOStwNU1WwSv860zH19w0XjLkFa0UZdgX49yQvo7odP8oPAI71VJUkTYNQV6FuBGeCFSf6ewePsLu2tKkmaAEddgSZ5WZIzu+97vhr4DQbfA/0ocPAE1CdJy9Zih/A3AE922/8TeAdwPfAwg/ckSdJJa7FD+FVVdaTbfiOwo6puAW5J8rl+S5Ok5W2xFeiqJHMh+xrg9qG+Uc+fStKKtFgI/hnwiSQPAo8DfweQ5EV4FV7SSW6xByr/3yQfA74L+GhVzb3W41nAr/RdnCQtZ4sehlfVJxdo++d+ypGkyTHqF+klSfMYoJLUyACVpEa9BmiSzUnuTrI/ydsW6H9Vks8kmU1y6by+K5J8qfu5os86JalFbwGaZBWDu5ZeD2wELkuycd6we4E3AX86b+7zgWsZPAl/E3BtktP7qlWSWvS5At0E7K+qA1X1JHAjcMnwgKr6SlV9Hnh63tzXAbdV1ZGqehi4DdjcY62StGR9BuhZwH1D+we7tr7nStIJMdEXkZJsTbInyZ4HHnhg3OVIOsn0GaCHgHOG9s/u2o7b3KraUVXTVTU9NTXVXKgktegzQHcDG5KsT7Ia2MLgocyjuBW4KMnp3cWji7o2SVo2egvQqpoFrmIQfF8EbqqqfUm2JbkY/uuBzQeBNwA3JNnXzT0C/C6DEN4NbBt6rJ4kLQu9PpKuqnYBu+a1XTO0vZvB4flCc3cCO/usT5KOxURfRJKkcTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGvUaoEk2J7k7yf4kb1ug/9QkH+r6P5VkXde+LsnjST7X/fxRn3VKUotT+vrgJKuA64HXAgeB3UlmququoWFXAg9X1YuSbAF+D3hj13dPVb20r/ok6Vj1uQLdBOyvqgNV9SRwI3DJvDGXAB/otm8GXpMkPdYkScdNnwF6FnDf0P7Brm3BMVU1CzwCvKDrW5/ks0k+keSVPdYpSU16O4Q/Rv8CrKmqh5JcAHw4yUuq6mvDg5JsBbYCrFmzZgxlSjqZ9bkCPQScM7R/dte24JgkpwDPAx6qqieq6iGAqtoL3AN8z/z/oKp2VNV0VU1PTU318CtI0jPrM0B3AxuSrE+yGtgCzMwbMwNc0W1fCtxeVZVkqrsIRZJzgQ3AgR5rlaQl6+0Qvqpmk1wF3AqsAnZW1b4k24A9VTUDvA/4YJL9wBEGIQvwKmBbkm8ATwO/WFVH+qpVklr0eg60qnYBu+a1XTO0/R/AGxaYdwtwS5+1SdKx8k4kSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDXqNUCTbE5yd5L9Sd62QP+pST7U9X8qybqhvrd37XcneV2fdUpSi94CNMkq4Hrg9cBG4LIkG+cNuxJ4uKpeBPw+8Hvd3I3AFuAlwGbgD7vPk6Rlo88V6CZgf1UdqKongRuBS+aNuQT4QLd9M/CaJOnab6yqJ6rqy8D+7vMkadnoM0DPAu4b2j/YtS04pqpmgUeAF4w4V5LG6pRxF3AskmwFtna7/57k7nHWs0ydATw47iL6knddMe4SVpoV/ffCtWmZtfaZOvoM0EPAOUP7Z3dtC405mOQU4HnAQyPOpap2ADuOY80rTpI9VTU97jo0Gfx7WZo+D+F3AxuSrE+ymsFFoZl5Y2aAuSXEpcDtVVVd+5buKv16YAPw6R5rlaQl620FWlWzSa4CbgVWATural+SbcCeqpoB3gd8MMl+4AiDkKUbdxNwFzAL/HJVPdVXrZLUIoMFn1aqJFu7Ux3Sovx7WRoDVJIaeSunJDUyQFewxW6lleYk2Znk/iRfGHctk8QAXaFGvJVWmvN+BrdNawkM0JVrlFtpJQCq6m8ZfBNGS2CArlzeDiv1zACVpEYG6Mo10u2wktoZoCvXKLfSSjoGBugK1T0ecO5W2i8CN1XVvvFWpeUqyZ8B/wh8b5KDSa4cd02TwDuRJKmRK1BJamSASlIjA1SSGhmgktTIAJWkRhP9UjkpyZnAu4GXAf8G/CvwYeDiqvqxcdamlc8VqCZWkgB/CXy8ql5YVRcAbwe+8xg/14WFRuIfiibZjwDfqKo/mmuoqn9KcjrwmiQ3A98P7AUur6pK8hVguqoeTDINvKuq/leS3wZeCJwL3Nu9IntNt78GeHdV/cGJ/OW0/LkC1SSbC8eFnA+8hcGzUM8FXjHC520ELqyqy7r9FwOvY/BowGuT/I9jK1crjQGqlerTVXWwqp4GPgesG2HOTFU9PrT/N1X1RFU9CNzPMZ4a0MpjgGqS7QMueIa+J4a2n+Kbp6tm+ebf/Wnz5nx9xM+QAANUk+124NQkW+cakpwHvPIoc77CN0P3p/srTScDA1QTqwZPwvlJ4MIk9yTZB/w/4PBRpv0O8J4kexisKqVmPo1Jkhq5ApWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1Og/AVm+A4XD3MeiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAAE9CAYAAABdgjpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAV5UlEQVR4nO3df5BdZ33f8fcHOTYtBWpjNZ7KliUbBSJCaofFtKFAAgaLklpk4gyCMjgDRYXaBcaTGlMYOygNAZsSklYMVkGFMGOEY0pmOxX1UAxkgAJagRoiUcWyAFsaBDIy2MWubNnf/nGP4HpZ7959pKP9ofdrZmfP85znOfe7MzufOeeeX6kqJEmz97i5LkCSFioDVJIaGaCS1MgAlaRGBqgkNTJAJanRKXNdwPFy5pln1ooVK+a6DEmLzPbt2++uqqVTrVs0AbpixQomJibmugxJi0yS7z7WOg/hJamRASpJjQxQSWpkgEpSo14DNMmaJLuT7ElyzRTr35Dkm0l2JPliktVd/4okD3T9O5J8sM86JalFb2fhkywBNgIvBvYB25KMV9WuoWE3VdUHu/GXAu8D1nTr7qiqC/qqT5KOVZ97oBcBe6pqb1U9CGwB1g4PqKp7h5pPAHy2nqQFo88AXQbcNdTe1/U9SpIrktwBXA+8aWjVyiTfSPKFJM+b6gOSrE8ykWTi4MGDx7N2SZrRnJ9EqqqNVXU+8FbgHV3394DlVXUhcBVwU5InTTF3U1WNVdXY0qVT3iggSb3pM0D3A+cMtc/u+h7LFuDlAFV1uKp+2C1vB+4AfqmnOiWpSZ+3cm4DViVZySA41wGvGh6QZFVV3d41Xwbc3vUvBQ5V1cNJzgNWAXt7rHVRuvrqqzlw4ABnnXUW119//VyXIy06vQVoVR1JciVwK7AE2FxVO5NsACaqahy4MsnFwEPAPcDl3fTnAxuSPAQ8Aryhqg71VetideDAAfbvn26nX9Kx6PVhIlW1Fdg6qe/aoeU3P8a8TwKf7LM2STpWc34SSZIWKgNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNFs1L5Vo969/++VyX0Jsn3n0fS4A7775v0f6d2294zVyXoJOYe6CS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYn/a2ci9kjpz7hUb8lHV8G6CL2k1UvmesSpEXNQ3hJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY16DdAka5LsTrInyTVTrH9Dkm8m2ZHki0lWD617Wzdvd5JL+qxTklr0FqBJlgAbgZcCq4FXDgdk56aqemZVXQBcD7yvm7saWAc8A1gDfKDbniTNG33ugV4E7KmqvVX1ILAFWDs8oKruHWo+AahueS2wpaoOV9W3gT3d9iRp3ujzXvhlwF1D7X3AcyYPSnIFcBVwKvDCoblfmTR3WT9lSlKbOT+JVFUbq+p84K3AO2YzN8n6JBNJJg4ePNhPgZL0GPoM0P3AOUPts7u+x7IFePls5lbVpqoaq6qxpUuXHmO5kjQ7fQboNmBVkpVJTmVwUmh8eECSVUPNlwG3d8vjwLokpyVZCawCvtZjrZI0a719B1pVR5JcCdwKLAE2V9XOJBuAiaoaB65McjHwEHAPcHk3d2eSm4FdwBHgiqp6uK9aJalFrw9UrqqtwNZJfdcOLb95mrl/BPxRf9VJ0rGZ85NIkrRQGaCS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJalRrwGaZE2S3Un2JLlmivVXJdmV5K+TfDbJuUPrHk6yo/sZ77NOSWpxSl8bTrIE2Ai8GNgHbEsyXlW7hoZ9AxirqvuTvBG4HnhFt+6Bqrqgr/ok6Vj1uQd6EbCnqvZW1YPAFmDt8ICq+lxV3d81vwKc3WM9knRc9Rmgy4C7htr7ur7H8jrg00PtxyeZSPKVJC+fakKS9d2YiYMHDx57xZI0C70dws9GklcDY8ALhrrPrar9Sc4Dbkvyzaq6Y3heVW0CNgGMjY3VCStYkuh3D3Q/cM5Q++yu71GSXAy8Hbi0qg4f7a+q/d3vvcDngQt7rFWSZq3PAN0GrEqyMsmpwDrgUWfTk1wI3MggPH8w1H96ktO65TOB5wLDJ58kac71dghfVUeSXAncCiwBNlfVziQbgImqGgduAP4e8BdJAO6sqkuBXwZuTPIIg5B/96Sz95I053r9DrSqtgJbJ/VdO7R88WPM+zLwzD5rk6Rj5Z1IktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNRopQLvXc0iShoy6B3p7khuSrO61GklaQEYN0H8E/C3woe4VG+uTPKnHuiRp3hspQKvqvqr6z1X168BbgeuA7yX5aJKn9lqhJM1TI38HmuTSJJ8C3g/8B+A84L8x6XmfknSyGPWByrcDnwNu6B52fNQtSZ5//MuSpPlv1AB9TVV9cbgjyXOr6ktV9aYe6pKkeW/Uk0h/NkXffzyehUjSQjPtHmiSfwL8OrA0yVVDq57E4EVxknTSmukQ/lQGb808BXjiUP+9wGV9FSVJC8G0AVpVXwC+kOQjVfXdE1STJC0IMx3Cv7+q3gL8pyQ1eX33DndJOinNdAj/se73e/suRJIWmpkO4bd3DxJZX1X/4gTVJEkLwoyXMVXVw8C5SU49AfVI0oIx6oX0e4EvJRkHfnK0s6re10tVkrQAjBqgd3Q/j+PRlzNJ0klrpACtqne2bDzJGuBPGVx0/6Gqevek9VcB/xI4AhwEXnv0cqkklwPv6Ib++6r6aEsNktSXkQI0yeeAqS5jeuE0c5YAG4EXA/uAbUnGq2rX0LBvAGNVdX+SNwLXA69IcgaDR+aNdZ+7vZt7z4h/lyT1btRD+N8fWn488DsM9hqncxGwp6r2AiTZAqwFfhqgVfW5ofFfAV7dLV8CfKaqDnVzPwOsAT4+Yr2S1LtRD+G3T+r6UpKvzTBtGXDXUHsf8Jxpxr8O+PQ0c5eNUKoknTCjHsKfMdR8HPAs4MnHq4gkr2ZwuP6CWc5bD6wHWL58+fEqR5JGMuoh/HYG30WGwaH7txnsMU5nP3DOUPvsru9RklwMvB14QVUdHpr7G5Pmfn7y3KraBGwCGBsb+7nvaCWpT6Mewq9s2PY2YFWSlQwCcR3wquEBSS4EbgTWVNUPhlbdCrwryeld+yXA2xpqkKTejPpOpCuS/P2h9ulJ/vV0c6rqCHAlgzD8FnBzVe1MsiHJ0YeQ3MDgcXl/kWRHd6E+3cmjP2QQwtuADUdPKEnSfDHqIfzrq2rj0UZV3ZPk9cAHpptUVVuZ9NK5qrp2aPniaeZuBjaPWJ8knXCjvtJjSZIcbXTXeHpvvKST2qh7oP8D+ESSG7v2v+r6JOmkNWqAvpVBaL6xa38G+FAvFUnSAjHqWfhHknwEuK2qdvdbkiQtDKOehb8U2EF32J7kgqNnzCXpZDXqSaTrGNzb/iOAqtoBtFwbKkmLxqgB+lBV/XhSn3f+SDqpjXoSaWeSVzG4nGkV8Cbgy/2VJUnz36h7oP8GeAZwmMEj5e4F3tJXUZK0EIx6Fv5+Bg/8eHt3f/qPqspDeEkntWn3QJNcm+Tp3fJpSW4D9gDf756iJEknrZkO4V8BHL3u8/Ju/D9g8NzOd/VYlyTNezMF6INDh+qXAB+vqoer6luMfgJKkhalmULwcJJfAb4P/CaPfjfS3+2tKkkn3NVXX82BAwc466yzuP766+e6nAVhpgB9M3ALsBT4k6r6NkCSf8bgjZqSFokDBw6wf//PvTRC05g2QKvqq8DTp+j/ued8StLJZtR74X8xyYeTfLprr04y0zuRJGlRG/VC+o8weDXHP+zaf4sX0ks6yY0aoGdW1c3AI/DT9x093FtVkrQAjBqgP0nyFLoHiCT5x8Dkh4tI0kll1Gs5rwLGgfOTfInBWfnLeqtKkhaAUe+F/3qSFwBPAwLsrqqHeq1Mkua52dxNdBGwopvza0moqj/vpSpJWgBGCtAkHwPOZ/Baj6MnjwowQCWdtEbdAx0DVvsIO0n6mVHPwv8NcFafhUjSQjPqHuiZwK4kX2PwVHoAqurSXqqS5qk7NzxzrkvozZFDZwCncOTQdxft37n82m8e1+2NGqB/cFw/VZIWgVEvY/pC34VI0kIz0ys9vtj9vi/JvUM/9yW5d6aNJ1mTZHeSPUmumWL985N8PcmRJJdNWvdwkh3dz/hs/zBJ6ttMj7P7p93vJ852w0mWABuBFwP7gG1Jxqtq19CwO4Hf49EPaj7qgaq6YLafK0knyrQBmuSM6dZX1aFpVl8E7Kmqvd22tgBrgZ8GaFV9p1v3yIj1StK8MdN3oNsZXDCfKdYVcN40c5cBdw219wHPmUVtj08yARwB3l1Vfzl5QJL1wHqA5cuXz2LTknTsZjqEX3miCpnCuVW1P8l5wG1JvllVdwwPqKpNwCaAsbExL/KXdEKNfC98kmXAucNzquqvppmyHzhnqH121zeSqtrf/d6b5PPAhcAd006SpBNo1Hvh38PgHfG7ePS98NMF6DZgVZKVDIJzHfCqET/vdOD+qjqc5EzguYCvCZQ0r4y6B/py4GlVdXjGkZ2qOpLkSgavAlkCbK6qnUk2ABNVNZ7k2cCngNOBf57knVX1DOCXgRu7k0uPY/Ad6K7H+ChJmhOjBuhe4BcYuo1zFFO9vbOqrh1a3sbg0H7yvC8Di/NeMkmLxqgBej+wI8lnefS98G/qpSpJWgBGDdD/CXyewfeeR4AH+ipI0tw48/GPAEe63xrFTBfSnwK8C3gt8F0G14MuB/4L8O96r07SCfP7v/qjuS5hwZnpeaA3AGcAK6vqWVX1awwunn9yt06STlozBehvAa+vqvuOdlTVvcAbgZf1WZgkzXczBWhN9RqPqnqY7h3xknSymilAdyV5zeTOJK8G/k8/JUnSwjDTWfgrgP+a5LUMHiwCgxfM/R3gt/ssTJLmu5keJrIfeE6SFwLP6Lq3VtVne69Mkua5UV/pcRtwW8+1SNKCMuprjSVJkxigktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUqNcATbImye4ke5JcM8X65yf5epIjSS6btO7yJLd3P5f3WacktegtQJMsATYCLwVWA69MsnrSsDuB3wNumjT3DOA64DnARcB1SU7vq1ZJatHnHuhFwJ6q2ltVDwJbgLXDA6rqO1X118Ajk+ZeAnymqg5V1T3AZ4A1PdYqSbPWZ4AuA+4aau/r+vqeK0knxII+iZRkfZKJJBMHDx6c63IknWT6DND9wDlD7bO7vuM2t6o2VdVYVY0tXbq0uVBJatFngG4DViVZmeRUYB0wPuLcW4GXJDm9O3n0kq5PkuaN3gK0qo4AVzIIvm8BN1fVziQbklwKkOTZSfYBvwvcmGRnN/cQ8IcMQngbsKHrk6R545Q+N15VW4Gtk/quHVrexuDwfKq5m4HNfdYnScdiQZ9EkqS5ZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGvQZokjVJdifZk+SaKdafluQT3fqvJlnR9a9I8kCSHd3PB/usU5JanNLXhpMsATYCLwb2AduSjFfVrqFhrwPuqaqnJlkHvAd4Rbfujqq6oK/6JOlY9bkHehGwp6r2VtWDwBZg7aQxa4GPdsu3AC9Kkh5rkqTjps8AXQbcNdTe1/VNOaaqjgA/Bp7SrVuZ5BtJvpDkeVN9QJL1SSaSTBw8ePD4Vi9JM5ivJ5G+ByyvqguBq4Cbkjxp8qCq2lRVY1U1tnTp0hNepKSTW58Buh84Z6h9dtc35ZgkpwBPBn5YVYer6ocAVbUduAP4pR5rlaRZ6zNAtwGrkqxMciqwDhifNGYcuLxbvgy4raoqydLuJBRJzgNWAXt7rFWSZq23s/BVdSTJlcCtwBJgc1XtTLIBmKiqceDDwMeS7AEOMQhZgOcDG5I8BDwCvKGqDvVVqyS16C1AAapqK7B1Ut+1Q8v/D/jdKeZ9Evhkn7VJ0rGaryeRJGneM0AlqZEBKkmNDFBJamSASlIjA1SSGhmgktTIAJWkRgaoJDUyQCWpkQEqSY0MUElqZIBKUiMDVJIaGaCS1MgAlaRGBqgkNTJAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1MkAlqZEBKkmNDFBJatRrgCZZk2R3kj1Jrpli/WlJPtGt/2qSFUPr3tb1705ySZ91SlKL3gI0yRJgI/BSYDXwyiSrJw17HXBPVT0V+BPgPd3c1cA64BnAGuAD3fYkad7ocw/0ImBPVe2tqgeBLcDaSWPWAh/tlm8BXpQkXf+WqjpcVd8G9nTbk6R5o88AXQbcNdTe1/VNOaaqjgA/Bp4y4lxJmlOnzHUBxyLJemB91/y/SXbPZT3z1JnA3XNdRF/y3svnuoTFZlH/v3BdWmad+1gr+gzQ/cA5Q+2zu76pxuxLcgrwZOCHI86lqjYBm45jzYtOkomqGpvrOrQw+P8yO30ewm8DViVZmeRUBieFxieNGQeO7kJcBtxWVdX1r+vO0q8EVgFf67FWSZq13vZAq+pIkiuBW4ElwOaq2plkAzBRVePAh4GPJdkDHGIQsnTjbgZ2AUeAK6rq4b5qlaQWGezwabFKsr77qkOakf8vs2OASlIjb+WUpEYG6CI206200lFJNif5QZK/metaFhIDdJEa8VZa6aiPMLhtWrNggC5eo9xKKwFQVX/F4EoYzYIBunh5O6zUMwNUkhoZoIvXSLfDSmpngC5eo9xKK+kYGKCLVPd4wKO30n4LuLmqds5tVZqvknwc+F/A05LsS/K6ua5pIfBOJElq5B6oJDUyQCWpkQEqSY0MUElqZIBKUqMF/VI5KclZwPuBZwM/Ar4P/CVwaVX91lzWpsXPPVAtWEkCfAr4fFWdX1XPAt4G/OIxbtcdC43EfxQtZL8JPFRVHzzaUVX/O8npwIuS3AL8CrAdeHVVVZLvAGNVdXeSMeC9VfUbSf4AOB84D7ize0X28q69HHh/Vf3ZifzjNP+5B6qF7Gg4TuVC4C0MnoV6HvDcEba3Gri4ql7ZtZ8OXMLg0YDXJfmFYytXi40BqsXqa1W1r6oeAXYAK0aYM15VDwy1/3tVHa6qu4EfcIxfDWjxMUC1kO0EnvUY6w4PLT/Mz76uOsLP/u8fP2nOT0bchgQYoFrYbgNOS7L+aEeSXwWeN82c7/Cz0P2d/krTycAA1YJVgyfh/DZwcZI7kuwE/hg4MM20dwJ/mmSCwV6l1MynMUlSI/dAJamRASpJjQxQSWpkgEpSIwNUkhoZoJLUyACVpEYGqCQ1+v/xPTDyT6esBgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:38.025620Z",
          "iopub.status.busy": "2021-07-31T18:46:38.025073Z",
          "iopub.status.idle": "2021-07-31T18:46:38.030946Z",
          "shell.execute_reply": "2021-07-31T18:46:38.030527Z",
          "shell.execute_reply.started": "2021-07-31T18:45:25.047896Z"
        },
        "papermill": {
          "duration": 0.184391,
          "end_time": "2021-07-31T18:46:38.031063",
          "exception": false,
          "start_time": "2021-07-31T18:46:37.846672",
          "status": "completed"
        },
        "tags": [],
        "id": "b2d9936d"
      },
      "source": [
        "# Splitting Dataset into train and test set\n",
        "\n",
        "X = df.drop('Churn', axis=1)\n",
        "y = df['Churn']"
      ],
      "id": "b2d9936d",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:38.386053Z",
          "iopub.status.busy": "2021-07-31T18:46:38.385407Z",
          "iopub.status.idle": "2021-07-31T18:46:38.392232Z",
          "shell.execute_reply": "2021-07-31T18:46:38.391653Z",
          "shell.execute_reply.started": "2021-07-31T18:45:25.057177Z"
        },
        "papermill": {
          "duration": 0.184567,
          "end_time": "2021-07-31T18:46:38.392434",
          "exception": false,
          "start_time": "2021-07-31T18:46:38.207867",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beb1b545",
        "outputId": "76a30e08-c5c9-49df-cc10-dadeeae8f8da"
      },
      "source": [
        "X.shape, y.shape"
      ],
      "id": "beb1b545",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7032, 23), (7032,))"
            ]
          },
          "metadata": {},
          "execution_count": 380
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:38.967401Z",
          "iopub.status.busy": "2021-07-31T18:46:38.966771Z",
          "iopub.status.idle": "2021-07-31T18:46:39.038865Z",
          "shell.execute_reply": "2021-07-31T18:46:39.038235Z",
          "shell.execute_reply.started": "2021-07-31T18:45:25.070856Z"
        },
        "papermill": {
          "duration": 0.361176,
          "end_time": "2021-07-31T18:46:39.039005",
          "exception": false,
          "start_time": "2021-07-31T18:46:38.677829",
          "status": "completed"
        },
        "tags": [],
        "id": "7da1f3ca"
      },
      "source": [
        "# Performing Train Test Split\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42)"
      ],
      "id": "7da1f3ca",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.284918,
          "end_time": "2021-07-31T18:46:39.610584",
          "exception": false,
          "start_time": "2021-07-31T18:46:39.325666",
          "status": "completed"
        },
        "tags": [],
        "id": "f44bee7f"
      },
      "source": [
        "## Model Building"
      ],
      "id": "f44bee7f"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.287224,
          "end_time": "2021-07-31T18:46:40.184647",
          "exception": false,
          "start_time": "2021-07-31T18:46:39.897423",
          "status": "completed"
        },
        "tags": [],
        "id": "a47a6fe9"
      },
      "source": [
        "### Using Logistics Regression"
      ],
      "id": "a47a6fe9"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:40.759871Z",
          "iopub.status.busy": "2021-07-31T18:46:40.759202Z",
          "iopub.status.idle": "2021-07-31T18:46:41.055405Z",
          "shell.execute_reply": "2021-07-31T18:46:41.054823Z",
          "shell.execute_reply.started": "2021-07-31T18:45:25.132296Z"
        },
        "papermill": {
          "duration": 0.586268,
          "end_time": "2021-07-31T18:46:41.055552",
          "exception": false,
          "start_time": "2021-07-31T18:46:40.469284",
          "status": "completed"
        },
        "tags": [],
        "id": "a2115837"
      },
      "source": [
        "# Importing Logistic Regression\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report"
      ],
      "id": "a2115837",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:41.630371Z",
          "iopub.status.busy": "2021-07-31T18:46:41.629716Z",
          "iopub.status.idle": "2021-07-31T18:46:41.632507Z",
          "shell.execute_reply": "2021-07-31T18:46:41.632085Z",
          "shell.execute_reply.started": "2021-07-31T18:45:25.202488Z"
        },
        "papermill": {
          "duration": 0.291825,
          "end_time": "2021-07-31T18:46:41.632623",
          "exception": false,
          "start_time": "2021-07-31T18:46:41.340798",
          "status": "completed"
        },
        "tags": [],
        "id": "e1fc1d63"
      },
      "source": [
        "# creating object for model\n",
        "model_lg = LogisticRegression(max_iter=120,random_state=0, n_jobs=20)"
      ],
      "id": "e1fc1d63",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:41.987708Z",
          "iopub.status.busy": "2021-07-31T18:46:41.987020Z",
          "iopub.status.idle": "2021-07-31T18:46:44.762143Z",
          "shell.execute_reply": "2021-07-31T18:46:44.762982Z",
          "shell.execute_reply.started": "2021-07-31T18:45:25.207965Z"
        },
        "papermill": {
          "duration": 2.956492,
          "end_time": "2021-07-31T18:46:44.763201",
          "exception": false,
          "start_time": "2021-07-31T18:46:41.806709",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc5b718e",
        "outputId": "64f417f6-9931-449a-e9dd-7a2bc4af316f"
      },
      "source": [
        "# Model Training\n",
        "\n",
        "model_lg.fit(X_train, y_train)"
      ],
      "id": "cc5b718e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=120,\n",
              "                   multi_class='auto', n_jobs=20, penalty='l2', random_state=0,\n",
              "                   solver='lbfgs', tol=0.0001, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 384
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:45.431737Z",
          "iopub.status.busy": "2021-07-31T18:46:45.431040Z",
          "iopub.status.idle": "2021-07-31T18:46:45.437006Z",
          "shell.execute_reply": "2021-07-31T18:46:45.437713Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.511334Z"
        },
        "papermill": {
          "duration": 0.310978,
          "end_time": "2021-07-31T18:46:45.437918",
          "exception": false,
          "start_time": "2021-07-31T18:46:45.126940",
          "status": "completed"
        },
        "tags": [],
        "id": "2e11b5c9"
      },
      "source": [
        "# Making Predictions\n",
        "pred_lg = model_lg.predict(X_test)"
      ],
      "id": "2e11b5c9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:46.043294Z",
          "iopub.status.busy": "2021-07-31T18:46:46.042546Z",
          "iopub.status.idle": "2021-07-31T18:46:46.046527Z",
          "shell.execute_reply": "2021-07-31T18:46:46.047178Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.531051Z"
        },
        "papermill": {
          "duration": 0.304638,
          "end_time": "2021-07-31T18:46:46.047667",
          "exception": false,
          "start_time": "2021-07-31T18:46:45.743029",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65a5ed96",
        "outputId": "4fbfad58-ab3d-4c62-c713-a480c3dc67ab"
      },
      "source": [
        "# Calculating Accuracy of the model\n",
        "\n",
        "lg = round(accuracy_score(y_test, pred_lg)*100,2)\n",
        "print(lg)"
      ],
      "id": "65a5ed96",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:46.643859Z",
          "iopub.status.busy": "2021-07-31T18:46:46.643187Z",
          "iopub.status.idle": "2021-07-31T18:46:46.651744Z",
          "shell.execute_reply": "2021-07-31T18:46:46.652395Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.542654Z"
        },
        "papermill": {
          "duration": 0.307136,
          "end_time": "2021-07-31T18:46:46.652606",
          "exception": false,
          "start_time": "2021-07-31T18:46:46.345470",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5753c1b0",
        "outputId": "b53b2086-9a0d-4407-d0dc-a8804e3bdc99"
      },
      "source": [
        "# Classification Report\n",
        "\n",
        "print(classification_report(y_test, pred_lg))"
      ],
      "id": "5753c1b0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.89      0.86      1033\n",
            "           1       0.62      0.51      0.56       374\n",
            "\n",
            "    accuracy                           0.79      1407\n",
            "   macro avg       0.73      0.70      0.71      1407\n",
            "weighted avg       0.78      0.79      0.78      1407\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:47.244495Z",
          "iopub.status.busy": "2021-07-31T18:46:47.243711Z",
          "iopub.status.idle": "2021-07-31T18:46:47.456009Z",
          "shell.execute_reply": "2021-07-31T18:46:47.455404Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.564196Z"
        },
        "papermill": {
          "duration": 0.506364,
          "end_time": "2021-07-31T18:46:47.456141",
          "exception": false,
          "start_time": "2021-07-31T18:46:46.949777",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "6b3c027f",
        "outputId": "fd657420-5547-434e-8656-50caff23cdb0"
      },
      "source": [
        "# confusion Matrix\n",
        "\n",
        "cm1 = confusion_matrix(y_test, pred_lg)\n",
        "sns.heatmap(cm1/np.sum(cm1), annot=True, fmt='0.2%', cmap=\"Reds\")\n",
        "plt.title(\"Logistic Regression Confusion Matrix\",fontsize=12)\n",
        "plt.show()"
      ],
      "id": "6b3c027f",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.297493,
          "end_time": "2021-07-31T18:46:48.049966",
          "exception": false,
          "start_time": "2021-07-31T18:46:47.752473",
          "status": "completed"
        },
        "tags": [],
        "id": "c1a7ada8"
      },
      "source": [
        "### Using decision Tree Classifer"
      ],
      "id": "c1a7ada8"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:48.647907Z",
          "iopub.status.busy": "2021-07-31T18:46:48.647072Z",
          "iopub.status.idle": "2021-07-31T18:46:48.770666Z",
          "shell.execute_reply": "2021-07-31T18:46:48.770002Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.752480Z"
        },
        "papermill": {
          "duration": 0.421192,
          "end_time": "2021-07-31T18:46:48.770822",
          "exception": false,
          "start_time": "2021-07-31T18:46:48.349630",
          "status": "completed"
        },
        "tags": [],
        "id": "1a817d63"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "id": "1a817d63",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:49.366968Z",
          "iopub.status.busy": "2021-07-31T18:46:49.365507Z",
          "iopub.status.idle": "2021-07-31T18:46:49.371497Z",
          "shell.execute_reply": "2021-07-31T18:46:49.371902Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.839947Z"
        },
        "papermill": {
          "duration": 0.305165,
          "end_time": "2021-07-31T18:46:49.372049",
          "exception": false,
          "start_time": "2021-07-31T18:46:49.066884",
          "status": "completed"
        },
        "tags": [],
        "id": "6340714a"
      },
      "source": [
        "# Creating object of the model\n",
        "model_dt = DecisionTreeClassifier(max_depth=4, random_state=42)"
      ],
      "id": "6340714a",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:49.781297Z",
          "iopub.status.busy": "2021-07-31T18:46:49.780598Z",
          "iopub.status.idle": "2021-07-31T18:46:49.803357Z",
          "shell.execute_reply": "2021-07-31T18:46:49.802930Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.845834Z"
        },
        "papermill": {
          "duration": 0.236285,
          "end_time": "2021-07-31T18:46:49.803470",
          "exception": false,
          "start_time": "2021-07-31T18:46:49.567185",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efc019f3",
        "outputId": "61587311-eefc-402d-898b-64216baf920e"
      },
      "source": [
        "model_dt.fit(X_train, y_train)"
      ],
      "id": "efc019f3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=4, 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, presort='deprecated',\n",
              "                       random_state=42, splitter='best')"
            ]
          },
          "metadata": {},
          "execution_count": 391
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:50.167890Z",
          "iopub.status.busy": "2021-07-31T18:46:50.167211Z",
          "iopub.status.idle": "2021-07-31T18:46:50.171892Z",
          "shell.execute_reply": "2021-07-31T18:46:50.172297Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.877289Z"
        },
        "papermill": {
          "duration": 0.191676,
          "end_time": "2021-07-31T18:46:50.172452",
          "exception": false,
          "start_time": "2021-07-31T18:46:49.980776",
          "status": "completed"
        },
        "tags": [],
        "id": "eddd75ec"
      },
      "source": [
        "pred_dt = model_dt.predict(X_test)"
      ],
      "id": "eddd75ec",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:50.534827Z",
          "iopub.status.busy": "2021-07-31T18:46:50.534144Z",
          "iopub.status.idle": "2021-07-31T18:46:50.540568Z",
          "shell.execute_reply": "2021-07-31T18:46:50.539970Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.884947Z"
        },
        "papermill": {
          "duration": 0.189441,
          "end_time": "2021-07-31T18:46:50.540712",
          "exception": false,
          "start_time": "2021-07-31T18:46:50.351271",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83c3300a",
        "outputId": "c4e60522-d7b4-4989-ed2f-ae0e98fcb02c"
      },
      "source": [
        "dt  = round(accuracy_score(y_test, pred_dt)*100, 2)\n",
        "print(dt)    "
      ],
      "id": "83c3300a",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.46\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:51.122404Z",
          "iopub.status.busy": "2021-07-31T18:46:51.121730Z",
          "iopub.status.idle": "2021-07-31T18:46:51.131980Z",
          "shell.execute_reply": "2021-07-31T18:46:51.131371Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.899882Z"
        },
        "papermill": {
          "duration": 0.303999,
          "end_time": "2021-07-31T18:46:51.132116",
          "exception": false,
          "start_time": "2021-07-31T18:46:50.828117",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9c6fc77",
        "outputId": "27a05f72-4578-49af-a884-6f688911b4dc"
      },
      "source": [
        "print(classification_report(y_test, pred_dt))"
      ],
      "id": "b9c6fc77",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.88      0.86      1033\n",
            "           1       0.61      0.53      0.57       374\n",
            "\n",
            "    accuracy                           0.78      1407\n",
            "   macro avg       0.72      0.70      0.71      1407\n",
            "weighted avg       0.78      0.78      0.78      1407\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:51.713770Z",
          "iopub.status.busy": "2021-07-31T18:46:51.713126Z",
          "iopub.status.idle": "2021-07-31T18:46:51.899388Z",
          "shell.execute_reply": "2021-07-31T18:46:51.898795Z",
          "shell.execute_reply.started": "2021-07-31T18:45:27.923089Z"
        },
        "papermill": {
          "duration": 0.478017,
          "end_time": "2021-07-31T18:46:51.899525",
          "exception": false,
          "start_time": "2021-07-31T18:46:51.421508",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "f7290e9a",
        "outputId": "b668011d-81b7-497c-988a-d7f85151c13e"
      },
      "source": [
        "# confusion Maxtrix\n",
        "cm2 = confusion_matrix(y_test, pred_dt)\n",
        "sns.heatmap(cm2/np.sum(cm2), annot = True, fmt=  '0.2%', cmap = 'Reds')\n",
        "plt.title(\"Decision Tree Classifier Confusion Matrix\",fontsize=12)\n",
        "plt.show()"
      ],
      "id": "f7290e9a",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.29431,
          "end_time": "2021-07-31T18:46:57.369534",
          "exception": false,
          "start_time": "2021-07-31T18:46:57.075224",
          "status": "completed"
        },
        "tags": [],
        "id": "3c0c17be"
      },
      "source": [
        "### Using XGBoost Classifier"
      ],
      "id": "3c0c17be"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:57.960375Z",
          "iopub.status.busy": "2021-07-31T18:46:57.959695Z",
          "iopub.status.idle": "2021-07-31T18:46:58.058949Z",
          "shell.execute_reply": "2021-07-31T18:46:58.058232Z",
          "shell.execute_reply.started": "2021-07-31T18:45:29.074535Z"
        },
        "papermill": {
          "duration": 0.39509,
          "end_time": "2021-07-31T18:46:58.059084",
          "exception": false,
          "start_time": "2021-07-31T18:46:57.663994",
          "status": "completed"
        },
        "tags": [],
        "id": "2e24a3fe"
      },
      "source": [
        "from xgboost import XGBClassifier"
      ],
      "id": "2e24a3fe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:58.651933Z",
          "iopub.status.busy": "2021-07-31T18:46:58.651196Z",
          "iopub.status.idle": "2021-07-31T18:46:58.654983Z",
          "shell.execute_reply": "2021-07-31T18:46:58.654422Z",
          "shell.execute_reply.started": "2021-07-31T18:45:29.157042Z"
        },
        "papermill": {
          "duration": 0.301618,
          "end_time": "2021-07-31T18:46:58.655119",
          "exception": false,
          "start_time": "2021-07-31T18:46:58.353501",
          "status": "completed"
        },
        "tags": [],
        "id": "e2b28d59"
      },
      "source": [
        "# Creating model object\n",
        "model_xgb = XGBClassifier(max_depth= 8, n_estimators= 125, random_state= 0,  learning_rate= 0.03, n_jobs=5)"
      ],
      "id": "e2b28d59",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:46:59.250312Z",
          "iopub.status.busy": "2021-07-31T18:46:59.249614Z",
          "iopub.status.idle": "2021-07-31T18:47:00.751461Z",
          "shell.execute_reply": "2021-07-31T18:47:00.750751Z",
          "shell.execute_reply.started": "2021-07-31T18:45:29.161900Z"
        },
        "papermill": {
          "duration": 1.800758,
          "end_time": "2021-07-31T18:47:00.751603",
          "exception": false,
          "start_time": "2021-07-31T18:46:58.950845",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9de176d5",
        "outputId": "e7285617-11f2-4490-c557-25d44e1b7a8f"
      },
      "source": [
        "# Training Model\n",
        "model_xgb.fit(X_train, y_train)"
      ],
      "id": "9de176d5",
      "execution_count": null,
      "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=0.03, max_delta_step=0, max_depth=8,\n",
              "              min_child_weight=1, missing=None, n_estimators=125, n_jobs=5,\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": {},
          "execution_count": 398
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:01.345982Z",
          "iopub.status.busy": "2021-07-31T18:47:01.342348Z",
          "iopub.status.idle": "2021-07-31T18:47:01.355872Z",
          "shell.execute_reply": "2021-07-31T18:47:01.356479Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.510400Z"
        },
        "papermill": {
          "duration": 0.31109,
          "end_time": "2021-07-31T18:47:01.356661",
          "exception": false,
          "start_time": "2021-07-31T18:47:01.045571",
          "status": "completed"
        },
        "tags": [],
        "id": "fa822928"
      },
      "source": [
        "# Making Prediction\n",
        "pred_xgb = model_xgb.predict(X_test)"
      ],
      "id": "fa822928",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:01.952976Z",
          "iopub.status.busy": "2021-07-31T18:47:01.951330Z",
          "iopub.status.idle": "2021-07-31T18:47:01.958286Z",
          "shell.execute_reply": "2021-07-31T18:47:01.957844Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.524457Z"
        },
        "papermill": {
          "duration": 0.308852,
          "end_time": "2021-07-31T18:47:01.958417",
          "exception": false,
          "start_time": "2021-07-31T18:47:01.649565",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b2440c9",
        "outputId": "3478f1e6-8c0e-4fd7-fec9-9f4f3ad6565b"
      },
      "source": [
        "# Calculating Accuracy Score\n",
        "xgb = round(accuracy_score(y_test, pred_xgb)*100, 2)\n",
        "print(xgb)"
      ],
      "id": "3b2440c9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.68\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:02.331337Z",
          "iopub.status.busy": "2021-07-31T18:47:02.330412Z",
          "iopub.status.idle": "2021-07-31T18:47:02.341054Z",
          "shell.execute_reply": "2021-07-31T18:47:02.340487Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.533108Z"
        },
        "papermill": {
          "duration": 0.196881,
          "end_time": "2021-07-31T18:47:02.341190",
          "exception": false,
          "start_time": "2021-07-31T18:47:02.144309",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "095ff65d",
        "outputId": "9749d366-2ea8-4a19-9b2c-31014318a0a0"
      },
      "source": [
        "print(classification_report(y_test,pred_xgb))"
      ],
      "id": "095ff65d",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.89      0.86      1033\n",
            "           1       0.62      0.51      0.56       374\n",
            "\n",
            "    accuracy                           0.79      1407\n",
            "   macro avg       0.73      0.70      0.71      1407\n",
            "weighted avg       0.78      0.79      0.78      1407\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:02.943703Z",
          "iopub.status.busy": "2021-07-31T18:47:02.943032Z",
          "iopub.status.idle": "2021-07-31T18:47:03.150133Z",
          "shell.execute_reply": "2021-07-31T18:47:03.149538Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.550404Z"
        },
        "papermill": {
          "duration": 0.509922,
          "end_time": "2021-07-31T18:47:03.150309",
          "exception": false,
          "start_time": "2021-07-31T18:47:02.640387",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "fc04961a",
        "outputId": "cb5f5223-1018-44b3-80a3-69f99e45bc2f"
      },
      "source": [
        "# confusion Maxtrix\n",
        "cm4 = confusion_matrix(y_test, pred_xgb)\n",
        "sns.heatmap(cm4/np.sum(cm4), annot = True, fmt=  '0.2%', cmap = 'Reds')\n",
        "plt.title(\"XGBoost Classifier Confusion Matrix\",fontsize=12)\n",
        "plt.show()"
      ],
      "id": "fc04961a",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.294448,
          "end_time": "2021-07-31T18:47:03.743662",
          "exception": false,
          "start_time": "2021-07-31T18:47:03.449214",
          "status": "completed"
        },
        "tags": [],
        "id": "f99ee036"
      },
      "source": [
        "### Using KNeighbours"
      ],
      "id": "f99ee036"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:04.403550Z",
          "iopub.status.busy": "2021-07-31T18:47:04.402901Z",
          "iopub.status.idle": "2021-07-31T18:47:04.406776Z",
          "shell.execute_reply": "2021-07-31T18:47:04.406210Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.718256Z"
        },
        "papermill": {
          "duration": 0.301758,
          "end_time": "2021-07-31T18:47:04.406893",
          "exception": false,
          "start_time": "2021-07-31T18:47:04.105135",
          "status": "completed"
        },
        "tags": [],
        "id": "3e7406af"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "id": "3e7406af",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:04.995082Z",
          "iopub.status.busy": "2021-07-31T18:47:04.994485Z",
          "iopub.status.idle": "2021-07-31T18:47:04.997956Z",
          "shell.execute_reply": "2021-07-31T18:47:04.997353Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.725111Z"
        },
        "papermill": {
          "duration": 0.301213,
          "end_time": "2021-07-31T18:47:04.998084",
          "exception": false,
          "start_time": "2021-07-31T18:47:04.696871",
          "status": "completed"
        },
        "tags": [],
        "id": "74aab18a"
      },
      "source": [
        "# Creating model object\n",
        "model_kn = KNeighborsClassifier(n_neighbors=9, leaf_size=20)"
      ],
      "id": "74aab18a",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:05.598731Z",
          "iopub.status.busy": "2021-07-31T18:47:05.597968Z",
          "iopub.status.idle": "2021-07-31T18:47:05.653181Z",
          "shell.execute_reply": "2021-07-31T18:47:05.652517Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.736849Z"
        },
        "papermill": {
          "duration": 0.358507,
          "end_time": "2021-07-31T18:47:05.653340",
          "exception": false,
          "start_time": "2021-07-31T18:47:05.294833",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bfe599b",
        "outputId": "be179c06-d212-4aa6-9f26-ed3d05a3cb8b"
      },
      "source": [
        "# Training Model\n",
        "model_kn.fit(X_train, y_train)"
      ],
      "id": "6bfe599b",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=20, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=9, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {},
          "execution_count": 405
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:06.251334Z",
          "iopub.status.busy": "2021-07-31T18:47:06.250526Z",
          "iopub.status.idle": "2021-07-31T18:47:06.660676Z",
          "shell.execute_reply": "2021-07-31T18:47:06.660051Z",
          "shell.execute_reply.started": "2021-07-31T18:45:30.775784Z"
        },
        "papermill": {
          "duration": 0.712824,
          "end_time": "2021-07-31T18:47:06.660809",
          "exception": false,
          "start_time": "2021-07-31T18:47:05.947985",
          "status": "completed"
        },
        "tags": [],
        "id": "46fb7d58"
      },
      "source": [
        "# Making Prediction\n",
        "pred_kn = model_kn.predict(X_test)"
      ],
      "id": "46fb7d58",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:07.262094Z",
          "iopub.status.busy": "2021-07-31T18:47:07.261413Z",
          "iopub.status.idle": "2021-07-31T18:47:07.266024Z",
          "shell.execute_reply": "2021-07-31T18:47:07.265511Z",
          "shell.execute_reply.started": "2021-07-31T18:45:31.192402Z"
        },
        "papermill": {
          "duration": 0.308645,
          "end_time": "2021-07-31T18:47:07.266143",
          "exception": false,
          "start_time": "2021-07-31T18:47:06.957498",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2a942595",
        "outputId": "7af5624e-3c5c-4e12-ee37-044a53d07499"
      },
      "source": [
        "# Calculating Accuracy Score\n",
        "kn = round(accuracy_score(y_test, pred_kn)*100, 2)\n",
        "print(kn)"
      ],
      "id": "2a942595",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "74.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:07.633338Z",
          "iopub.status.busy": "2021-07-31T18:47:07.632823Z",
          "iopub.status.idle": "2021-07-31T18:47:07.645735Z",
          "shell.execute_reply": "2021-07-31T18:47:07.645299Z",
          "shell.execute_reply.started": "2021-07-31T18:45:31.199075Z"
        },
        "papermill": {
          "duration": 0.198401,
          "end_time": "2021-07-31T18:47:07.645846",
          "exception": false,
          "start_time": "2021-07-31T18:47:07.447445",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30033dc6",
        "outputId": "f0d3f235-7087-49ea-9c4b-bcbda8157fc2"
      },
      "source": [
        "print(classification_report(y_test,pred_kn))"
      ],
      "id": "30033dc6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.83      0.83      1033\n",
            "           1       0.53      0.52      0.52       374\n",
            "\n",
            "    accuracy                           0.75      1407\n",
            "   macro avg       0.68      0.67      0.68      1407\n",
            "weighted avg       0.75      0.75      0.75      1407\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:08.016787Z",
          "iopub.status.busy": "2021-07-31T18:47:08.016061Z",
          "iopub.status.idle": "2021-07-31T18:47:08.218027Z",
          "shell.execute_reply": "2021-07-31T18:47:08.218686Z",
          "shell.execute_reply.started": "2021-07-31T18:45:31.218215Z"
        },
        "papermill": {
          "duration": 0.390682,
          "end_time": "2021-07-31T18:47:08.218852",
          "exception": false,
          "start_time": "2021-07-31T18:47:07.828170",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "e480f425",
        "outputId": "d6428635-3e35-4093-95f0-c553ece4f020"
      },
      "source": [
        "# confusion Maxtrix\n",
        "cm5 = confusion_matrix(y_test, pred_kn)\n",
        "sns.heatmap(cm5/np.sum(cm5), annot = True, fmt=  '0.2%', cmap = 'Reds')\n",
        "plt.title(\"KN Classifier Confusion Matrix\",fontsize=12)\n",
        "plt.show()"
      ],
      "id": "e480f425",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.298177,
          "end_time": "2021-07-31T18:47:08.811789",
          "exception": false,
          "start_time": "2021-07-31T18:47:08.513612",
          "status": "completed"
        },
        "tags": [],
        "id": "1bc270ca"
      },
      "source": [
        "###  Using SVM"
      ],
      "id": "1bc270ca"
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:09.410956Z",
          "iopub.status.busy": "2021-07-31T18:47:09.410252Z",
          "iopub.status.idle": "2021-07-31T18:47:09.414059Z",
          "shell.execute_reply": "2021-07-31T18:47:09.413471Z",
          "shell.execute_reply.started": "2021-07-31T18:45:31.394407Z"
        },
        "papermill": {
          "duration": 0.304252,
          "end_time": "2021-07-31T18:47:09.414201",
          "exception": false,
          "start_time": "2021-07-31T18:47:09.109949",
          "status": "completed"
        },
        "tags": [],
        "id": "8ec9677a"
      },
      "source": [
        "from sklearn.svm import SVC, LinearSVC"
      ],
      "id": "8ec9677a",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:10.013570Z",
          "iopub.status.busy": "2021-07-31T18:47:10.012812Z",
          "iopub.status.idle": "2021-07-31T18:47:10.015216Z",
          "shell.execute_reply": "2021-07-31T18:47:10.014744Z",
          "shell.execute_reply.started": "2021-07-31T18:45:31.399409Z"
        },
        "papermill": {
          "duration": 0.302823,
          "end_time": "2021-07-31T18:47:10.015377",
          "exception": false,
          "start_time": "2021-07-31T18:47:09.712554",
          "status": "completed"
        },
        "tags": [],
        "id": "0516b395"
      },
      "source": [
        "model_svm = SVC(kernel='rbf', random_state = 42)"
      ],
      "id": "0516b395",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:10.389416Z",
          "iopub.status.busy": "2021-07-31T18:47:10.388742Z",
          "iopub.status.idle": "2021-07-31T18:47:11.626750Z",
          "shell.execute_reply": "2021-07-31T18:47:11.627116Z",
          "shell.execute_reply.started": "2021-07-31T18:45:31.413657Z"
        },
        "papermill": {
          "duration": 1.428268,
          "end_time": "2021-07-31T18:47:11.627280",
          "exception": false,
          "start_time": "2021-07-31T18:47:10.199012",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8a4f5196",
        "outputId": "e373ca96-1c77-4c68-cca4-c995393caa10"
      },
      "source": [
        "model_svm.fit(X_train, y_train)"
      ],
      "id": "8a4f5196",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=42, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {},
          "execution_count": 412
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:11.998354Z",
          "iopub.status.busy": "2021-07-31T18:47:11.997753Z",
          "iopub.status.idle": "2021-07-31T18:47:12.489391Z",
          "shell.execute_reply": "2021-07-31T18:47:12.488843Z",
          "shell.execute_reply.started": "2021-07-31T18:45:32.470802Z"
        },
        "papermill": {
          "duration": 0.679496,
          "end_time": "2021-07-31T18:47:12.489503",
          "exception": false,
          "start_time": "2021-07-31T18:47:11.810007",
          "status": "completed"
        },
        "tags": [],
        "id": "24ae6dd4"
      },
      "source": [
        "# Making Prediction\n",
        "pred_svm = model_svm.predict(X_test)"
      ],
      "id": "24ae6dd4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:12.864595Z",
          "iopub.status.busy": "2021-07-31T18:47:12.863930Z",
          "iopub.status.idle": "2021-07-31T18:47:12.868899Z",
          "shell.execute_reply": "2021-07-31T18:47:12.869433Z",
          "shell.execute_reply.started": "2021-07-31T18:45:32.696983Z"
        },
        "papermill": {
          "duration": 0.192479,
          "end_time": "2021-07-31T18:47:12.869632",
          "exception": false,
          "start_time": "2021-07-31T18:47:12.677153",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "352a79c5",
        "outputId": "2f783cdd-ee62-41f8-f624-2d31e33701ef"
      },
      "source": [
        "# Calculating Accuracy Score\n",
        "sv = round(accuracy_score(y_test, pred_svm)*100, 2)\n",
        "print(sv)"
      ],
      "id": "352a79c5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "78.39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:13.470471Z",
          "iopub.status.busy": "2021-07-31T18:47:13.469732Z",
          "iopub.status.idle": "2021-07-31T18:47:13.479223Z",
          "shell.execute_reply": "2021-07-31T18:47:13.479774Z",
          "shell.execute_reply.started": "2021-07-31T18:45:32.710401Z"
        },
        "papermill": {
          "duration": 0.311937,
          "end_time": "2021-07-31T18:47:13.479952",
          "exception": false,
          "start_time": "2021-07-31T18:47:13.168015",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78f25d30",
        "outputId": "a40c5866-343c-47fb-f119-20972737fd09"
      },
      "source": [
        "print(classification_report(y_test,pred_kn))"
      ],
      "id": "78f25d30",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.83      0.83      1033\n",
            "           1       0.53      0.52      0.52       374\n",
            "\n",
            "    accuracy                           0.75      1407\n",
            "   macro avg       0.68      0.67      0.68      1407\n",
            "weighted avg       0.75      0.75      0.75      1407\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-07-31T18:47:14.080073Z",
          "iopub.status.busy": "2021-07-31T18:47:14.079410Z",
          "iopub.status.idle": "2021-07-31T18:47:14.238034Z",
          "shell.execute_reply": "2021-07-31T18:47:14.238629Z",
          "shell.execute_reply.started": "2021-07-31T18:45:32.727386Z"
        },
        "papermill": {
          "duration": 0.463369,
          "end_time": "2021-07-31T18:47:14.238815",
          "exception": false,
          "start_time": "2021-07-31T18:47:13.775446",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "c1806b42",
        "outputId": "62c237c1-b25d-49c6-efe5-a2e53b219559"
      },
      "source": [
        "# confusion Maxtrix\n",
        "cm6 = confusion_matrix(y_test, pred_svm)\n",
        "sns.heatmap(cm6/np.sum(cm6), annot = True, fmt=  '0.2%', cmap = 'Reds')\n",
        "plt.title(\"SVM Classifier Confusion Matrix\",fontsize=12)\n",
        "plt.show()"
      ],
      "id": "c1806b42",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}