Machine_Learning_Tutorial_1.ipynb

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    "colab": {
      "name": "Machine Learning Tutorial 1.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNMTB0nsHS3ww1rezmp+x5t",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/philsaurabh/Machine-Learning-Tutorials/blob/main/Machine_Learning_Tutorial_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries"
      ],
      "metadata": {
        "id": "de85fWTURf7g"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "5IoSep5PLIm1"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(style=\"white\")\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import dataset"
      ],
      "metadata": {
        "id": "AXEiN-_tRiyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/philsaurabh/Machine-Learning-Tutorials/main/Telco_Customer_Churn.csv')"
      ],
      "metadata": {
        "id": "rqvFKocFLwot"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 357
        },
        "id": "h2ebyRl7L3uw",
        "outputId": "bb168c4f-371b-4183-e104-d16bf950cbda"
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      "execution_count": 3,
      "outputs": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
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              "      <th>Churn</th>\n",
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              "      <td>One year</td>\n",
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              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
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              "      <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",
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              "    <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",
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            "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": 3
        }
      ]
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      "source": [
        "df.shape"
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        },
        "id": "CMWe9TXXL5if",
        "outputId": "5a6c0f32-8e14-4412-ea44-b03041f60086"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7043, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
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      "cell_type": "code",
      "source": [
        "df.columns"
      ],
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        },
        "id": "Z9MqrddyMhDv",
        "outputId": "a2dea73a-1a70-4cb7-bf5b-bbf30f7f9ecf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
              "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
              "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
              "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
              "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYiACAKSMjZu",
        "outputId": "70b21f52-c258-45a8-d8a1-297c875beb2e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           object\n",
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "o5tY-eImMmhv",
        "outputId": "043511a4-f912-49b8-905f-b791564b1acc"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of       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",
              "7038  6840-RESVB    Male              0  ...          84.80       1990.5     No\n",
              "7039  2234-XADUH  Female              0  ...         103.20       7362.9     No\n",
              "7040  4801-JZAZL  Female              0  ...          29.60       346.45     No\n",
              "7041  8361-LTMKD    Male              1  ...          74.40        306.6    Yes\n",
              "7042  3186-AJIEK    Male              0  ...         105.65       6844.5     No\n",
              "\n",
              "[7043 rows x 21 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['TotalCharges']"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "1zyMnpfsMrAA",
        "outputId": "5eaea0f3-8177-42ee-8e6c-3c6d631493f8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         29.85\n",
              "1        1889.5\n",
              "2        108.15\n",
              "3       1840.75\n",
              "4        151.65\n",
              "         ...   \n",
              "7038     1990.5\n",
              "7039     7362.9\n",
              "7040     346.45\n",
              "7041      306.6\n",
              "7042     6844.5\n",
              "Name: TotalCharges, Length: 7043, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning and Preprocessing"
      ],
      "metadata": {
        "id": "TygAZSxsRobW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiNVywfgPbAZ",
        "outputId": "9583d92a-d2c3-4eba-8ac0-9426ee11d7f7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df.Churn);\n",
        "print ('Churn Percentages are\\n',df.Churn.value_counts() / df.Churn.count())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "cwuTMceyPdfN",
        "outputId": "19bdf8e4-b453-4989-bf13-82311535a326"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Churn Percentages are\n",
            " No     0.73463\n",
            "Yes    0.26537\n",
            "Name: Churn, dtype: float64\n"
          ]
        },
        {
          "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": "code",
      "source": [
        "df.drop('customerID', axis=1, inplace=True)\n",
        "df['Churn'].replace(to_replace ='Yes', value=1, inplace=True)\n",
        "df['Churn'].replace(to_replace = 'No', value=0, inplace=True)\n",
        "df = pd.get_dummies(df)"
      ],
      "metadata": {
        "id": "5obnbEvlPiBv"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "BUTUwj_iPrrq",
        "outputId": "f77a5d6b-efbf-4d42-bccd-e844026d0d55"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-67d87e55-e7f5-4d7f-90c7-073cf2dc5136\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Churn</th>\n",
              "      <th>gender_Female</th>\n",
              "      <th>gender_Male</th>\n",
              "      <th>Partner_No</th>\n",
              "      <th>Partner_Yes</th>\n",
              "      <th>Dependents_No</th>\n",
              "      <th>Dependents_Yes</th>\n",
              "      <th>PhoneService_No</th>\n",
              "      <th>PhoneService_Yes</th>\n",
              "      <th>MultipleLines_No</th>\n",
              "      <th>MultipleLines_No phone service</th>\n",
              "      <th>MultipleLines_Yes</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>OnlineSecurity_No</th>\n",
              "      <th>OnlineSecurity_No internet service</th>\n",
              "      <th>OnlineSecurity_Yes</th>\n",
              "      <th>OnlineBackup_No</th>\n",
              "      <th>OnlineBackup_No internet service</th>\n",
              "      <th>OnlineBackup_Yes</th>\n",
              "      <th>DeviceProtection_No</th>\n",
              "      <th>DeviceProtection_No internet service</th>\n",
              "      <th>DeviceProtection_Yes</th>\n",
              "      <th>TechSupport_No</th>\n",
              "      <th>TechSupport_No internet service</th>\n",
              "      <th>TechSupport_Yes</th>\n",
              "      <th>StreamingTV_No</th>\n",
              "      <th>StreamingTV_No internet service</th>\n",
              "      <th>StreamingTV_Yes</th>\n",
              "      <th>StreamingMovies_No</th>\n",
              "      <th>StreamingMovies_No internet service</th>\n",
              "      <th>StreamingMovies_Yes</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>PaperlessBilling_No</th>\n",
              "      <th>...</th>\n",
              "      <th>TotalCharges_973.35</th>\n",
              "      <th>TotalCharges_973.45</th>\n",
              "      <th>TotalCharges_973.55</th>\n",
              "      <th>TotalCharges_973.65</th>\n",
              "      <th>TotalCharges_973.95</th>\n",
              "      <th>TotalCharges_978</th>\n",
              "      <th>TotalCharges_978.6</th>\n",
              "      <th>TotalCharges_979.05</th>\n",
              "      <th>TotalCharges_979.5</th>\n",
              "      <th>TotalCharges_98.35</th>\n",
              "      <th>TotalCharges_98.5</th>\n",
              "      <th>TotalCharges_980.35</th>\n",
              "      <th>TotalCharges_981.45</th>\n",
              "      <th>TotalCharges_982.95</th>\n",
              "      <th>TotalCharges_985.05</th>\n",
              "      <th>TotalCharges_987.95</th>\n",
              "      <th>TotalCharges_989.05</th>\n",
              "      <th>TotalCharges_989.45</th>\n",
              "      <th>TotalCharges_99.45</th>\n",
              "      <th>TotalCharges_99.6</th>\n",
              "      <th>TotalCharges_99.75</th>\n",
              "      <th>TotalCharges_99.95</th>\n",
              "      <th>TotalCharges_990.3</th>\n",
              "      <th>TotalCharges_990.45</th>\n",
              "      <th>TotalCharges_990.85</th>\n",
              "      <th>TotalCharges_990.9</th>\n",
              "      <th>TotalCharges_992.7</th>\n",
              "      <th>TotalCharges_993.15</th>\n",
              "      <th>TotalCharges_994.55</th>\n",
              "      <th>TotalCharges_994.8</th>\n",
              "      <th>TotalCharges_995.35</th>\n",
              "      <th>TotalCharges_996.45</th>\n",
              "      <th>TotalCharges_996.85</th>\n",
              "      <th>TotalCharges_996.95</th>\n",
              "      <th>TotalCharges_997.65</th>\n",
              "      <th>TotalCharges_997.75</th>\n",
              "      <th>TotalCharges_998.1</th>\n",
              "      <th>TotalCharges_999.45</th>\n",
              "      <th>TotalCharges_999.8</th>\n",
              "      <th>TotalCharges_999.9</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <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",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <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>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>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <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>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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>...</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>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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>42.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>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>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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</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>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>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>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>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>0</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>0</td>\n",
              "      <td>2</td>\n",
              "      <td>70.70</td>\n",
              "      <td>1</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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>...</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>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>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>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>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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 6576 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67d87e55-e7f5-4d7f-90c7-073cf2dc5136')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-67d87e55-e7f5-4d7f-90c7-073cf2dc5136 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-67d87e55-e7f5-4d7f-90c7-073cf2dc5136');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   SeniorCitizen  tenure  ...  TotalCharges_999.8  TotalCharges_999.9\n",
              "0              0       1  ...                   0                   0\n",
              "1              0      34  ...                   0                   0\n",
              "2              0       2  ...                   0                   0\n",
              "3              0      45  ...                   0                   0\n",
              "4              0       2  ...                   0                   0\n",
              "\n",
              "[5 rows x 6576 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONx9XgOZPs9e",
        "outputId": "ab4f7067-6ada-4023-d0d9-7f432470f25f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "IWHPk3hcPyFV"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EuAbdPPSP1CS",
        "outputId": "30be068f-97ce-4dee-8d05-f329dc3e9b9b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, ConfusionMatrixDisplay, precision_score, recall_score, f1_score, classification_report, roc_curve, plot_roc_curve, auc, precision_recall_curve, plot_precision_recall_curve, average_precision_score\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "lkreEtCGP30H"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns = ['Churn'])\n",
        "y = df['Churn'].values"
      ],
      "metadata": {
        "id": "NAuANBsEP60p"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCcm_yOfP9gO",
        "outputId": "6683a0a6-bdb4-4466-dc41-55fc56444b44"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Scaler = StandardScaler()\n",
        "X = Scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "553KTpPoP_s7"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzAEshfUQBvp",
        "outputId": "cd4ff1f4-db4c-4508-872c-3d36620652ac"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.43991649, -1.27744458, -1.16032292, ..., -0.01191659,\n",
              "        -0.01191659, -0.01191659],\n",
              "       [-0.43991649,  0.06632742, -0.25962894, ..., -0.01191659,\n",
              "        -0.01191659, -0.01191659],\n",
              "       [-0.43991649, -1.23672422, -0.36266036, ..., -0.01191659,\n",
              "        -0.01191659, -0.01191659],\n",
              "       ...,\n",
              "       [-0.43991649, -0.87024095, -1.1686319 , ..., -0.01191659,\n",
              "        -0.01191659, -0.01191659],\n",
              "       [ 2.27315869, -1.15528349,  0.32033821, ..., -0.01191659,\n",
              "        -0.01191659, -0.01191659],\n",
              "       [-0.43991649,  1.36937906,  1.35896134, ..., -0.01191659,\n",
              "        -0.01191659, -0.01191659]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "pxcTp-fEQF8m"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpDK35icQJz4",
        "outputId": "d1f62e3f-c780-4b94-f30c-1113b12efc86"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5634, 6575), (1409, 6575), (5634,), (1409,))"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()#KNeighborsClassifier()#SVC#RandomForestClassifier()#AdaBoostClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "score = cross_val_score(model, X_train, y_train, cv = 6)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "roc = roc_auc_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print ('Train score of '+str(model)[:-2]+' is', score.mean(),'%')\n",
        "print ('--')\n",
        "print ('Precision score is ', precision)\n",
        "print ('--')\n",
        "print ('ROC Score is', roc)\n",
        "print ('--')\n",
        "print ('Recall Score is ', recall)\n",
        "print ('--')\n",
        "print ('Confusion Matrix\\n\\n', cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkTY9D6zQN08",
        "outputId": "6814248d-1fb2-4802-af1b-0c5aea9f541f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train score of LogisticRegression is 0.7669506567270146 %\n",
            "--\n",
            "Precision score is  0.5842696629213483\n",
            "--\n",
            "ROC Score is 0.7073918039065492\n",
            "--\n",
            "Recall Score is  0.5576407506702413\n",
            "--\n",
            "Confusion Matrix\n",
            "\n",
            " [[888 148]\n",
            " [165 208]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#HoldOut Validation Approach- Train And Test Split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(X_train, y_train)\n",
        "result = model.score(X_test, y_test)\n",
        "print(result)\n",
        "#K Fold Cross Validation\n",
        "from sklearn.model_selection import KFold\n",
        "model=DecisionTreeClassifier()\n",
        "kfold_validation=KFold(10)\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "results=cross_val_score(model,X,y,cv=kfold_validation)\n",
        "print(results)\n",
        "print(np.mean(results))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sfmMieEmQY_V",
        "outputId": "c54ef7fb-2060-4762-bec7-36dcfecd702e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7700496806245565\n",
            "[0.77730496 0.76028369 0.75319149 0.78409091 0.765625   0.74715909\n",
            " 0.76420455 0.74289773 0.78693182 0.78125   ]\n",
            "0.7662939232753063\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "skfold=StratifiedKFold(n_splits=5)\n",
        "model=DecisionTreeClassifier()\n",
        "scores=cross_val_score(model,X,y,cv=skfold)\n",
        "print(np.mean(scores))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovqbtpaCRBuC",
        "outputId": "84006ca8-06d1-4cda-e6b6-428c732d3be5"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7644485128072779\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gAQOaeL8RPaO",
        "outputId": "7bbb8fac-ccf2-4dd8-962f-844e301baf8a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.76933996, 0.76295245, 0.75017743, 0.765625  , 0.77414773])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import ShuffleSplit\n",
        "model=DecisionTreeClassifier()\n",
        "ssplit=ShuffleSplit(n_splits=10,test_size=0.30)\n",
        "results=cross_val_score(model,X,y,cv=ssplit)"
      ],
      "metadata": {
        "id": "LbUU6w9YRW-H"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLDuS23VRXcE",
        "outputId": "6b236365-3aed-4ffd-a253-e074e9a9ec1a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.7624231 , 0.7780407 , 0.78513961, 0.78561287, 0.77330809,\n",
              "       0.76762896, 0.77614766, 0.75863701, 0.77330809, 0.77236157])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aE8XaBHORa18",
        "outputId": "5a2af2c3-fe2b-413e-ee6f-8644d33567aa"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.773260766682442"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    }
  ]
}