Classifier_AdaboostClassifier_Telecom_Data.ipynb

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    "colab": {
      "name": "AdaboostClassifier_Telecom_Data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/ML_Algos/blob/main/AdaboostClassifier/AdaboostClassifier_Telecom_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2B54uxLDndzf"
      },
      "source": [
        "# Business Problem\n",
        "\n",
        "***Video walkthrough of the notebook*** : https://www.youtube.com/watch?v=ehS6tmQveA4&t=1602s\n",
        "\n",
        "A telecommunication company is facing dip in revenue due to customer attrition and was looking for ways to tackle the issue. \n",
        "\n",
        "- Analysis of the current churn data and look for patterns\n",
        "- Possible churn prediction system \n",
        "\n",
        "# Why solve using data science?\n",
        "\n",
        "Not all problems needs to be solved using ML/DL techniques!\n",
        "\n",
        "Justification in this use case:\n",
        "\n",
        "- Customer churn does not happen with specific set of factors.  Factors may overlap or there many too many resons for the churn.\n",
        "\n",
        "- Scalability: As the organization gets more customers having ML solutions to handle them will be lot better than doing manual analysis.\n",
        "\n",
        "With these justifications, lets get our customers data and understand.\n",
        "\n",
        "# Advantages of having a churn prediction system\n",
        "\n",
        "Client can react in time and retain the customers by making a special offer according to the preference\n",
        "\n",
        "# Dataset Recieved\n",
        "\n",
        "Telecom users dataset\n",
        "https://www.kaggle.com/radmirzosimov/telecom-users-dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h80AkerHz09Y",
        "outputId": "3fb26545-69d4-4196-e72f-0160a171d393"
      },
      "source": [
        "!pip install plotly==4.14.3"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting plotly==4.14.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1f/f6/bd3c17c8003b6641df1228e80e1acac97ed8402635e46c2571f8e1ef63af/plotly-4.14.3-py2.py3-none-any.whl (13.2MB)\n",
            "\u001b[K     |████████████████████████████████| 13.2MB 262kB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly==4.14.3) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly==4.14.3) (1.3.3)\n",
            "Installing collected packages: plotly\n",
            "  Found existing installation: plotly 4.4.1\n",
            "    Uninstalling plotly-4.4.1:\n",
            "      Successfully uninstalled plotly-4.4.1\n",
            "Successfully installed plotly-4.14.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySfkknDslgfk"
      },
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn import metrics"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-iuCMjW7gRt"
      },
      "source": [
        "path_to_file = 'https://raw.githubusercontent.com/gulabpatel/Biasness/master/telecom_users.csv'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqvmzLM58Qcp"
      },
      "source": [
        "# Read the input data\n",
        "df_data = pd.read_csv(path_to_file)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "M_4O4_uE8pkU",
        "outputId": "9b637d89-1e87-42a9-b296-173fea4ebbd6"
      },
      "source": [
        "# Take first few rows\n",
        "df_data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</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>1869</td>\n",
              "      <td>7010-BRBUU</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>72</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>24.10</td>\n",
              "      <td>1734.65</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4528</td>\n",
              "      <td>9688-YGXVR</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>44</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>88.15</td>\n",
              "      <td>3973.2</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6344</td>\n",
              "      <td>9286-DOJGF</td>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>38</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</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>Bank transfer (automatic)</td>\n",
              "      <td>74.95</td>\n",
              "      <td>2869.85</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6739</td>\n",
              "      <td>6994-KERXL</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</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>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>55.90</td>\n",
              "      <td>238.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>432</td>\n",
              "      <td>2181-UAESM</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>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>No</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>53.45</td>\n",
              "      <td>119.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  customerID  gender  ...  MonthlyCharges TotalCharges Churn\n",
              "0        1869  7010-BRBUU    Male  ...           24.10      1734.65    No\n",
              "1        4528  9688-YGXVR  Female  ...           88.15       3973.2    No\n",
              "2        6344  9286-DOJGF  Female  ...           74.95      2869.85   Yes\n",
              "3        6739  6994-KERXL    Male  ...           55.90        238.5    No\n",
              "4         432  2181-UAESM    Male  ...           53.45        119.5    No\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVu3EDjk_4jt"
      },
      "source": [
        "# Data Glossary\n",
        "\n",
        "- customerID - customer id\n",
        "- gender - client gender (male / female)\n",
        "- SeniorCitizen - is the client retired (1, 0)\n",
        "- Partner - is the client married (Yes, No)\n",
        "- tenure - how many months a person has been a client of the company\n",
        "- PhoneService - is the telephone service connected (Yes, No)\n",
        "- MultipleLines - are multiple phone lines connected (Yes, No, No phone service)\n",
        "- InternetService - client's Internet service provider (DSL, Fiber optic, No)\n",
        "- OnlineSecurity - is the online security service connected (Yes, No, No internet service)\n",
        "- OnlineBackup - is the online backup service activated (Yes, No, No internet service)\n",
        "- DeviceProtection - does the client have equipment insurance (Yes, No, No internet service)\n",
        "- TechSupport - is the technical support service connected (Yes, No, No internet service)\n",
        "- StreamingTV - is the streaming TV service connected (Yes, No, No internet service)\n",
        "- StreamingMovies - is the streaming cinema service activated (Yes, No, No internet service)\n",
        "- Contract - type of customer contract (Month-to-month, One year, Two year)\n",
        "- PaperlessBilling - whether the client uses paperless billing (Yes, No)\n",
        "- PaymentMethod - payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
        "- MonthlyCharges - current monthly payment\n",
        "- TotalCharges - the total amount that the client paid for the services for the entire time\n",
        "- Churn - whether there was a churn (Yes or No)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2UB6Fmy_q1K"
      },
      "source": [
        "# Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7oeIcCk8zcQ"
      },
      "source": [
        "# Remove ids from the analysis\n",
        "df_data = df_data.drop(columns=['Unnamed: 0', 'customerID'])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "ZE0aRxboBADK",
        "outputId": "361006a7-0b7b-4c82-9d4f-b5f3f005d5f2"
      },
      "source": [
        "df_data.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>72</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>24.10</td>\n",
              "      <td>1734.65</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>44</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>88.15</td>\n",
              "      <td>3973.2</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>38</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</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>Bank transfer (automatic)</td>\n",
              "      <td>74.95</td>\n",
              "      <td>2869.85</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>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</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>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>55.90</td>\n",
              "      <td>238.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</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>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>No</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>53.45</td>\n",
              "      <td>119.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0    Male              0     Yes  ...          24.10       1734.65    No\n",
              "1  Female              0      No  ...          88.15        3973.2    No\n",
              "2  Female              1     Yes  ...          74.95       2869.85   Yes\n",
              "3    Male              0      No  ...          55.90         238.5    No\n",
              "4    Male              0      No  ...          53.45         119.5    No\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L7vxm0_PBFRV",
        "outputId": "80bd103a-834a-4587-c513-6ed14e9e8987"
      },
      "source": [
        "# Let's see of we have any missing values\n",
        "df_data.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5986 entries, 0 to 5985\n",
            "Data columns (total 20 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   gender            5986 non-null   object \n",
            " 1   SeniorCitizen     5986 non-null   int64  \n",
            " 2   Partner           5986 non-null   object \n",
            " 3   Dependents        5986 non-null   object \n",
            " 4   tenure            5986 non-null   int64  \n",
            " 5   PhoneService      5986 non-null   object \n",
            " 6   MultipleLines     5986 non-null   object \n",
            " 7   InternetService   5986 non-null   object \n",
            " 8   OnlineSecurity    5986 non-null   object \n",
            " 9   OnlineBackup      5986 non-null   object \n",
            " 10  DeviceProtection  5986 non-null   object \n",
            " 11  TechSupport       5986 non-null   object \n",
            " 12  StreamingTV       5986 non-null   object \n",
            " 13  StreamingMovies   5986 non-null   object \n",
            " 14  Contract          5986 non-null   object \n",
            " 15  PaperlessBilling  5986 non-null   object \n",
            " 16  PaymentMethod     5986 non-null   object \n",
            " 17  MonthlyCharges    5986 non-null   float64\n",
            " 18  TotalCharges      5986 non-null   object \n",
            " 19  Churn             5986 non-null   object \n",
            "dtypes: float64(1), int64(2), object(17)\n",
            "memory usage: 935.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQZ601g0EM52"
      },
      "source": [
        "#### From the data gloassary, we can observe that the `TotalCharges` is a number but it is in `object` type. Let's analyze that."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1mdcGc7CYKY"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kv31CtGbBXBQ",
        "outputId": "5bdc6336-12ae-4df0-ddb8-d438e9d9d884"
      },
      "source": [
        "df_data['TotalCharges'].value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.2       10\n",
              "           10\n",
              "19.75       8\n",
              "19.55       7\n",
              "19.9        6\n",
              "           ..\n",
              "1025.15     1\n",
              "3582.4      1\n",
              "449.3       1\n",
              "2320.8      1\n",
              "5730.15     1\n",
              "Name: TotalCharges, Length: 5611, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4YepltCIItU"
      },
      "source": [
        "Looks like there are about 10 blank values in the `TotalCharges` field. Let's update the values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VFxNwQTFMdR",
        "outputId": "5e22c3cf-1ec2-4bf0-cf4b-2024697fd8ee"
      },
      "source": [
        "# Observe that TotalCharges have blank values\n",
        "print('Before removing blank values')\n",
        "print(df_data[df_data['TotalCharges'] == ' '].index)\n",
        "df_data['TotalCharges'] = df_data['TotalCharges'].replace(r'^\\s*$', 0, regex=True)\n",
        "print('After removing blank values')\n",
        "print(df_data[df_data['TotalCharges'] == ' '].index)\n",
        "df_data['TotalCharges'] = df_data['TotalCharges'].astype(float)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before removing blank values\n",
            "Int64Index([356, 634, 2771, 3086, 3255, 4326, 5375, 5382, 5695, 5951], dtype='int64')\n",
            "After removing blank values\n",
            "Int64Index([], dtype='int64')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y4tYMR6WMAvJ",
        "outputId": "5791f80f-d598-4533-e00e-8e06e6ebf58f"
      },
      "source": [
        "# Let's review the data information\n",
        "df_data.info()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5986 entries, 0 to 5985\n",
            "Data columns (total 20 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   gender            5986 non-null   object \n",
            " 1   SeniorCitizen     5986 non-null   int64  \n",
            " 2   Partner           5986 non-null   object \n",
            " 3   Dependents        5986 non-null   object \n",
            " 4   tenure            5986 non-null   int64  \n",
            " 5   PhoneService      5986 non-null   object \n",
            " 6   MultipleLines     5986 non-null   object \n",
            " 7   InternetService   5986 non-null   object \n",
            " 8   OnlineSecurity    5986 non-null   object \n",
            " 9   OnlineBackup      5986 non-null   object \n",
            " 10  DeviceProtection  5986 non-null   object \n",
            " 11  TechSupport       5986 non-null   object \n",
            " 12  StreamingTV       5986 non-null   object \n",
            " 13  StreamingMovies   5986 non-null   object \n",
            " 14  Contract          5986 non-null   object \n",
            " 15  PaperlessBilling  5986 non-null   object \n",
            " 16  PaymentMethod     5986 non-null   object \n",
            " 17  MonthlyCharges    5986 non-null   float64\n",
            " 18  TotalCharges      5986 non-null   float64\n",
            " 19  Churn             5986 non-null   object \n",
            "dtypes: float64(2), int64(2), object(16)\n",
            "memory usage: 935.4+ KB\n"
          ],
          "name": "stdout"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kztOfLKMMqqj"
      },
      "source": [
        "## Business Problem to Data Science Questions\n",
        "\n",
        "Dolores, the client, was expecting to analysze the current data to understand what went wrong and correct. Here are the questions the team came up with. \n",
        "\n",
        "_NOTE_: As we present to the stakeholders, questions willget updated.\n",
        "\n",
        "- How gender, partner, and dependents are related to chrun?\n",
        "- Are we facing churn with customers with longer tenure?\n",
        "- Are we having issues with phone and internet services?\n",
        "- Does customers opted for tech support stayed for longer tenure with less churn?\n",
        "- Did customers monthly charge and total charge relate with churn?\n",
        "- Do customers opted for streaming face issue with the service?\n",
        "- Which contract do customers prefer in order to stay with the business?\n",
        "\n",
        "## Let's visualize\n",
        "\n",
        "We will be using `Plotly` and `Seaborn` for the visualization pupose. `Pandas` used for analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nCFfRDgV4oH"
      },
      "source": [
        "### How gender, partner, and dependents are related to chrun?"
      ]
    },
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        "outputId": "59ebe78c-3a44-4121-c5bd-6eb9788ed0a6"
      },
      "source": [
        "fig = px.treemap(df_data.groupby(['gender', 'Partner', 'Dependents','Churn']).size().reset_index(name='count'), \n",
        "                 path=['gender', 'Partner', 'Dependents','Churn'], values='count',\n",
        "                 color='Churn', title='How gender, partner, and dependents are related to chrun?')\n",
        "fig.show()"
      ],
      "execution_count": 12,
      "outputs": [
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      "source": [
        "### Are we facing churn with customers with longer tenure?"
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        "                   title='Are we facing churn with customers with longer tenure?')\n",
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      "execution_count": 13,
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      "source": [
        "### Are we having issues with phone and internet services?"
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        "fig = px.sunburst(df_data.groupby(['Churn', 'PhoneService', 'InternetService']).size().reset_index(name='count'), \n",
        "                  path=['Churn', 'PhoneService', 'InternetService'], values='count', title='Are we having issues with phone and internet services?')\n",
        "fig.show()"
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      "execution_count": 14,
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internet services?\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XU4L6-8oavt1"
      },
      "source": [
        "### Does customers opted for tech support stayed for longer tenure with less churn?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uxSdzoy6vQq"
      },
      "source": [
        "df_tech_yes = df_data[df_data['TechSupport'] == 'Yes']\n",
        "df_tech_no = df_data[df_data['TechSupport'] == 'No']"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZQjPjRI8mAT"
      },
      "source": [
        "### Customers getting tech support"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CYNBWMv5airj",
        "outputId": "cf9da9a6-59ef-4f0c-f845-3a1f92f04ff5"
      },
      "source": [
        "fig = px.histogram(df_tech_yes.groupby(['tenure', 'Churn']).size().reset_index(name='count'), \n",
        "                   x=\"tenure\", y='count', color=\"Churn\", marginal=\"rug\", color_discrete_map={\"Yes\": \"#E45756\", \"No\": \"#1CBE4F\"},\n",
        "                   title='Statistics of customers opted for tech support')\n",
        "fig.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzlGFBOj8sGt"
      },
      "source": [
        "### Customers not getting tech support"
      ]
    },
    {
      "cell_type": "code",
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          "height": 542
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        "id": "36NeuejE5e1V",
        "outputId": "fcea9191-7447-497f-afd1-45787e25f6eb"
      },
      "source": [
        "fig = px.histogram(df_tech_no.groupby(['tenure', 'Churn']).size().reset_index(name='count'), \n",
        "                   x=\"tenure\", y='count', color=\"Churn\", marginal=\"rug\", color_discrete_map={\"Yes\": \"#E45756\", \"No\": \"#1CBE4F\"},\n",
        "                   title='Statistics of customers opted out of the tech support')\n",
        "fig.show()"
      ],
      "execution_count": 17,
      "outputs": [
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Statistics of customers opted out of the tech support\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"tenure\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"matches\": \"x\", \"showgrid\": true, \"showticklabels\": false}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 0.7326], \"title\": {\"text\": \"sum of count\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.7426, 1.0], \"matches\": \"y2\", \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\"}},                        {\"responsive\": true}                    ).then(function(){\n",
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtEz9N7J9gJc"
      },
      "source": [
        "## Did customers monthly charge and total charge relate with churn?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "DdeL6k0a_GiV",
        "outputId": "f44f3931-c4df-4429-aade-8249a271dcea"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(26,8.27)})\n",
        "sns.kdeplot(data=df_data, x=\"MonthlyCharges\", hue=\"Churn\", multiple=\"stack\").set(title='Did customers monthly charge and total charge relate with churn?')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Did customers monthly charge and total charge relate with churn?')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1872x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "T3UFEoTX9OI0",
        "outputId": "5edbf22e-3498-46d0-9653-4aff6ec3e981"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(26,8.27)})\n",
        "sns.kdeplot(data=df_data, x=\"TotalCharges\", hue=\"Churn\", multiple=\"stack\").set(title='Did customers total charge and total charge relate with churn?')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Did customers total charge and total charge relate with churn?')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1872x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcjJLvM7ASky"
      },
      "source": [
        "## Do customers opted for streaming, face issue with the service?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "L0VLDI0xA-8w",
        "outputId": "ad5c0504-966e-4028-daa9-58cd2038b3d5"
      },
      "source": [
        "ax = sns.barplot(x=\"StreamingTV\", y=\"count\", hue='Churn', \n",
        "                 data=df_data.groupby(['Churn', 'StreamingTV']).size().reset_index(name='count'), palette=\"Set2\").set(title='Streaming TV vs Churn')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1872x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "YACRLoNRE40H",
        "outputId": "ed2e22f4-f1a6-48d2-af58-f9d43a1b0abd"
      },
      "source": [
        "ax = sns.barplot(x=\"StreamingMovies\", y=\"count\", hue='Churn', \n",
        "                 data=df_data.groupby(['Churn', 'StreamingMovies']).size().reset_index(name='count'), \n",
        "                 palette=\"Set2\").set(title='Streaming Movies vs Churn')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1872x595.44 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrJWXKcWGF_U"
      },
      "source": [
        "## Which contract do customers prefer in order to stay with the business?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PaYtBRaiGFk9",
        "outputId": "0831abb5-ff7c-4902-fbcc-30086e68376a"
      },
      "source": [
        "fig = px.sunburst(df_data.groupby(['Contract', 'Churn']).size().reset_index(name='count'), \n",
        "                  path=['Contract', 'Churn'], values='count', title='Which contract do customers prefer in order to stay with the business?')\n",
        "fig.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWlpkSgWado8"
      },
      "source": [
        "# Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ld9KXF93RsYY"
      },
      "source": [
        "# List of categorical columns\n",
        "cat_columns = ['gender', 'SeniorCitizen', 'Partner', 'PhoneService', \n",
        "               'MultipleLines', 'InternetService', 'OnlineSecurity', \n",
        "               'OnlineBackup', 'DeviceProtection',  'TechSupport',  \n",
        "               'StreamingTV',  'StreamingMovies', 'Contract',\n",
        "               'PaperlessBilling', 'PaymentMethod', 'Dependents']"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7MFfZ17bUjW"
      },
      "source": [
        "![img1](https://github.com/SSaishruthi/Guvi_AI_For_India/raw/main/encoding.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAWWElGoFZt1"
      },
      "source": [
        "# We can really quickly build dummy features with pandas by calling the get_dummies function. \n",
        "df_processed = pd.get_dummies(df_data, prefix_sep=\"__\",\n",
        "                              columns=cat_columns)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "ZKpUtz9vTvgh",
        "outputId": "53c11894-ab84-4a0e-f2c6-72ce0f9cbe43"
      },
      "source": [
        "df_processed.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>gender__Female</th>\n",
              "      <th>gender__Male</th>\n",
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              "      <th>InternetService__DSL</th>\n",
              "      <th>InternetService__Fiber optic</th>\n",
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              "      <th>OnlineSecurity__No</th>\n",
              "      <th>OnlineSecurity__No internet service</th>\n",
              "      <th>OnlineSecurity__Yes</th>\n",
              "      <th>OnlineBackup__No</th>\n",
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              "      <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>PaperlessBilling__Yes</th>\n",
              "      <th>PaymentMethod__Bank transfer (automatic)</th>\n",
              "      <th>PaymentMethod__Credit card (automatic)</th>\n",
              "      <th>PaymentMethod__Electronic check</th>\n",
              "      <th>PaymentMethod__Mailed check</th>\n",
              "      <th>Dependents__No</th>\n",
              "      <th>Dependents__Yes</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>72</td>\n",
              "      <td>24.10</td>\n",
              "      <td>1734.65</td>\n",
              "      <td>No</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>88.15</td>\n",
              "      <td>3973.20</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>74.95</td>\n",
              "      <td>2869.85</td>\n",
              "      <td>Yes</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "      <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>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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>53.45</td>\n",
              "      <td>119.50</td>\n",
              "      <td>No</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>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>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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tenure  MonthlyCharges  ...  Dependents__No Dependents__Yes\n",
              "0      72           24.10  ...               0               1\n",
              "1      44           88.15  ...               1               0\n",
              "2      38           74.95  ...               1               0\n",
              "3       4           55.90  ...               1               0\n",
              "4       2           53.45  ...               1               0\n",
              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNbCoOgJa22w"
      },
      "source": [
        "Now we got the data with one hot encoded feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ef9MNmATxbY",
        "outputId": "08a4ca1f-69aa-410d-ac19-b46dfc248d7b"
      },
      "source": [
        "# Encode target column\n",
        "# First let's see unique values in the target column\n",
        "print('Before encoding:', df_processed['Churn'].unique())\n",
        "# Encode target columns: Assign `Yes` to 1 and `No` to 0\n",
        "df_processed[\"Churn\"] = np.where(df_processed[\"Churn\"].str.contains(\"Yes\"), 1, 0)\n",
        "print('After encoding:', df_processed['Churn'].unique())"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before encoding: ['No' 'Yes']\n",
            "After encoding: [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TIoEM7LVbs91"
      },
      "source": [
        "Let's save the data transformation we did before so that we perform the same operation in the test dataset. If there is any drift in the data, we might have to re-train the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2fHUWfxVMkJ"
      },
      "source": [
        "cat_dummies = [col for col in df_processed \n",
        "               if \"__\" in col \n",
        "               and col.split(\"__\")[0] in cat_columns]\n",
        "with open('cat_dummies.txt', 'w') as filehandle:\n",
        "    for listitem in cat_dummies:\n",
        "        filehandle.write('%s\\n' % listitem)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7OzZY0r_VZMH"
      },
      "source": [
        "processed_columns = list(df_processed.columns[:])\n",
        "with open('processed_columns.txt', 'w') as filehandle:\n",
        "    for listitem in processed_columns:\n",
        "        filehandle.write('%s\\n' % listitem)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nIiETE6vcTtR",
        "outputId": "96dbd8a2-b0f9-4d27-f2fc-549dc74a8b53"
      },
      "source": [
        "# Looks like the dataset is imbalanced\n",
        "df_processed['Churn'].value_counts()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4399\n",
              "1    1587\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rglq68KhcyhO"
      },
      "source": [
        "### Choosing algorithms some tips!\n",
        "\n",
        "- Explainability\n",
        "- Memory: can you load your data fully? need incremental learning algorithms?\n",
        "- Number of features\n",
        "- Nonlinearity of the data\n",
        "- Training speed\n",
        "- Prediction speed\n",
        "\n",
        "#### How to deal with data imbalance?\n",
        "\n",
        "There are many ways to handle the dta imbalance.\n",
        "\n",
        "- Choose a learning algorithm that provide weights for every class.\n",
        "- Data-level approach: Under-sampling, Over-sampling, Cluster-based over sampling, Synthetic minority over-sampling technique (SMOTE)\n",
        "- Algorithmic ensemble techniques\n",
        "- Bagging techniques\n",
        "- Boosting: Ada boost, Gradient Tree boosting, XG Boost/\n",
        "\n",
        "- https://www.analyticsvidhya.com/blog/2017/03/imbalanced-data-classification/\n",
        "- https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/\n",
        "\n",
        "Here, we are using adaptive boosting technique in this example to deal with data imbalance.\n",
        "\n",
        "#### An AdaBoost classifier.\n",
        "\n",
        "Ada Boost is the first original boosting technique which creates a highly accurate prediction rule by combining many weak and inaccurate rules.  Each classifier is serially trained with the goal of correctly classifying examples in every round that were incorrectly classified in the previous round."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPopv0ZKVdS6",
        "outputId": "de72a0ca-8e7c-47e4-c7f0-1e7f4999106c"
      },
      "source": [
        "# Get only features\n",
        "feature_df = df_processed.drop(['Churn'], axis=1)\n",
        "# Extract target column \n",
        "target_df = df_processed[['Churn']]\n",
        "# Split dataset into train and test (Best Practise is to split into train, validation, and test)\n",
        "x_train,x_test,y_train,y_test = train_test_split(feature_df, target_df, test_size=0.2, random_state = 0)\n",
        "# Initialize adaboost classifier\n",
        "cls = AdaBoostClassifier(n_estimators=100)\n",
        "# Fit the model\n",
        "cls.fit(x_train, y_train)\n",
        "# Predict and calculate metrics\n",
        "print(\"Accuracy:\", metrics.accuracy_score(y_test, cls.predict(x_test)))\n",
        "print('Recall Score:', metrics.recall_score(y_test, cls.predict(x_test), average='weighted'))\n",
        "print('Precision Score:', metrics.precision_score(y_test, cls.predict(x_test), average='weighted'))\n",
        "print('F1 Score:', metrics.f1_score(y_test, cls.predict(x_test), average='weighted'))\n",
        "print('Confusion matrix:', metrics.confusion_matrix(y_test, cls.predict(x_test)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning:\n",
            "\n",
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8063439065108514\n",
            "Recall Score: 0.8063439065108514\n",
            "Precision Score: 0.7978525894830667\n",
            "F1 Score: 0.8001391971967932\n",
            "Confusion matrix: [[794  89]\n",
            " [143 172]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6D1pKnvgVxpN"
      },
      "source": [
        "import pickle\n",
        "# save the classifier\n",
        "with open('classifier.pkl', 'wb') as fid:\n",
        "    pickle.dump(cls, fid)    "
      ],
      "execution_count": 31,
      "outputs": []
    }
  ]
}