Predicting_customer_churn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "fyrrJYMG30Ln",
        "JmRvDH2839xZ",
        "zrHrKYbBUxOS",
        "3xFPdnTnf_1f",
        "e2cMF8YDt_Lz"
      ],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNdpmvxRIrVDuq9atze8vsl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nawaz-kmr/Predicting_customer_churn/blob/main/Predicting_customer_churn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What is customer churn?**\n",
        "\n",
        "So, customer churn is simply the rate at which customers leave doing business with an entity. Simply put, churn prediction involves determining the possibility of customers stopping doing business with an entity. \n",
        "In other words, if a consumer has purchased a subscription to a particular service, we must determine the likelihood that the customer would leave or cancel the membership"
      ],
      "metadata": {
        "id": "BQSsWpjLrmkR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AKyJ-YFmrlwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uaz-4fJZAIB",
        "outputId": "faa0aeaa-7cc6-4c02-c0d4-ae98f9594d3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd '/gdrive/MyDrive/ChurnPrediction/'\n",
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZzFjyh7ZZyO2",
        "outputId": "5a37d9c6-86ff-4e29-b7a5-3bef4ba2b3d1"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/gdrive/MyDrive/ChurnPrediction\n",
            "dataset.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !nvidia-smi"
      ],
      "metadata": {
        "id": "88UQ66X9ai9V"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import files"
      ],
      "metadata": {
        "id": "taCaDAHtbHhS"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# uploaded = files.upload()"
      ],
      "metadata": {
        "id": "0FtH_RlKbpzW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting customer churn** is critical for telecommunication companies to be able to effectively retain customers. It is more costly to acquire new customers than to retain existing ones. For this reason, large telecommunications corporations are seeking to develop models to predict which customers are more likely to change and take actions accordingly.\n",
        "\n",
        "In this article, we build a model to predict how likely a customer will churn by analyzing its characteristics: \n",
        "**(1) demographic information**, \n",
        "**(2) account information, and \n",
        "(3) services information.**\n",
        "The objective is to obtain a data-driven solution that will allow us to reduce churn rates and, as a consequence, to increase customer satisfaction and corporation revenue.\n",
        "\n"
      ],
      "metadata": {
        "id": "Fa7L11cXiFs0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas  as pd"
      ],
      "metadata": {
        "id": "2k8jFIekcbt8"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1.Data Reading**"
      ],
      "metadata": {
        "id": "fyrrJYMG30Ln"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **data set** used in this article is available in the Kaggle (CC BY-NC-ND) and contains **nineteen columns** (independent variables) that indicate the characteristics of the clients of a fictional telecommunications corporation. The **Churn column** (response variable) indicates whether the customer departed within the last month or not. The class No includes the clients that did not leave the company last month, while the class Yes contains the clients that decided to terminate their relations with the company. The objective of the analysis is to obtain the relation between the customer’s characteristics and the churn."
      ],
      "metadata": {
        "id": "QejMGIK7jBad"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('dataset.csv')\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "yA1Gq6XLhbkU",
        "outputId": "f87226b9-2d08-4f85-86fc-b1380b9505a3"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83ebbb3e-28ac-438e-afd0-c7f0ced36db5\">\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>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>...</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83ebbb3e-28ac-438e-afd0-c7f0ced36db5')\"\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-83ebbb3e-28ac-438e-afd0-c7f0ced36db5 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-83ebbb3e-28ac-438e-afd0-c7f0ced36db5');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WdcBu4CutnnL",
        "outputId": "0c4e2b83-513f-460b-9709-a0a37868684d"
      },
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data set contains 19 independent variables, which can be classified into 3 groups:\n",
        "\n",
        "**(1) Demographic Information**\n",
        "\n",
        "**gender**: Whether the client is a female or a male (Female, Male).\n",
        "**SeniorCitizen**: Whether the client is a senior citizen or not ( 0, 1).\n",
        "**Partner**: Whether the client has a partner or not (Yes, No).\n",
        "**Dependents**: Whether the client has dependents or not (Yes, No).\n",
        "\n",
        "**(2) Customer Account Information**\n",
        "\n",
        "**tenure**: Number of months the customer has stayed with the company (Multiple different numeric values).\n",
        "**Contract**: Indicates the customer’s current contract type (Month-to-Month, One year, Two year).\n",
        "**PaperlessBilling**: Whether the client has paperless billing or not (Yes, No).\n",
        "**PaymentMethod**: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit Card (automatic)).\n",
        "**MontlyCharges**: The amount charged to the customer monthly (Multiple different numeric values).\n",
        "**TotalCharges**: The total amount charged to the customer (Multiple different numeric values).\n",
        "\n",
        "**(3) Services Information**\n",
        "\n",
        "**PhoneService**: Whether the client has a phone service or not (Yes, No).\n",
        "**MultipleLines**: Whether the client has multiple lines or not (No phone service, No, Yes).\n",
        "**InternetServices**: Whether the client is subscribed to Internet service with the company (DSL, Fiber optic, No)\n",
        "**OnlineSecurity**: Whether the client has online security or not (No internet service, No, Yes).\n",
        "**OnlineBackup**: Whether the client has online backup or not (No internet service, No, Yes).\n",
        "**DeviceProtection**: Whether the client has device protection or not (No internet service, No, Yes).\n",
        "**TechSupport**: Whether the client has tech support or not (No internet service, No, Yes).\n",
        "**StreamingTV**: Whether the client has streaming TV or not (No internet service, No, Yes).\n",
        "StreamingMovies: Whether the client has streaming movies or not (No internet service, No, Yes)."
      ],
      "metadata": {
        "id": "INotXMxF1iq8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Exploratory Data Analysis and Data Cleaning**"
      ],
      "metadata": {
        "id": "JmRvDH2839xZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory data analysis** consists of analyzing the main characteristics of a data set usually by means of visualization methods and **summary statistics**. The objective is to understand the data, discover patterns and anomalies, and check assumptions before performing further evaluations."
      ],
      "metadata": {
        "id": "oLIsLaaQ4Nsk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Missing values and data types**\n",
        "\n",
        "At the beginning of EDA, we want to know as much information as possible about the data, this is when the pandas.DataFrame.info method comes in handy. This method prints a concise summary of the data frame, including the column names and their data types, the number of non-null values, and the amount of memory used by the data frame."
      ],
      "metadata": {
        "id": "50Q3jwjh4Nov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FvbtDPSt45p8",
        "outputId": "458ee008-b81c-4fec-8c56-4ac91b850c4d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   object \n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(1), int64(2), object(18)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As shown above, the data set contains **7043 observations** and **21 columns**. Apparently, there are no null values on the data set; however, we observe that the column **TotalCharges** was wrongly detected as an object. This column represents the total amount charged to the customer and it is, therefore, **a numeric variable.** For further analysis, we need to transform this column into a numeric data type. To do so, we can use the **pd.to_numeric** function. By default, this function raises an exception when it sees non-numeric data; however, we can use the argument **errors='coerce'** to skip those cases and replace them with a NaN."
      ],
      "metadata": {
        "id": "zG2_viSw4Nmm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['TotalCharges'] = pd.to_numeric(data['TotalCharges'],errors='coerce')"
      ],
      "metadata": {
        "id": "LK7sAMvPtnjR"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['TotalCharges'].isnull()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "t9LVrzmDtngl",
        "outputId": "1f66fa75-b2cd-412f-ccbe-beafde52133f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
              "488   4472-LVYGI  Female              0     Yes        Yes       0   \n",
              "753   3115-CZMZD    Male              0      No        Yes       0   \n",
              "936   5709-LVOEQ  Female              0     Yes        Yes       0   \n",
              "1082  4367-NUYAO    Male              0     Yes        Yes       0   \n",
              "1340  1371-DWPAZ  Female              0     Yes        Yes       0   \n",
              "3331  7644-OMVMY    Male              0     Yes        Yes       0   \n",
              "3826  3213-VVOLG    Male              0     Yes        Yes       0   \n",
              "4380  2520-SGTTA  Female              0     Yes        Yes       0   \n",
              "5218  2923-ARZLG    Male              0     Yes        Yes       0   \n",
              "6670  4075-WKNIU  Female              0     Yes        Yes       0   \n",
              "6754  2775-SEFEE    Male              0      No        Yes       0   \n",
              "\n",
              "     PhoneService     MultipleLines InternetService       OnlineSecurity  ...  \\\n",
              "488            No  No phone service             DSL                  Yes  ...   \n",
              "753           Yes                No              No  No internet service  ...   \n",
              "936           Yes                No             DSL                  Yes  ...   \n",
              "1082          Yes               Yes              No  No internet service  ...   \n",
              "1340           No  No phone service             DSL                  Yes  ...   \n",
              "3331          Yes                No              No  No internet service  ...   \n",
              "3826          Yes               Yes              No  No internet service  ...   \n",
              "4380          Yes                No              No  No internet service  ...   \n",
              "5218          Yes                No              No  No internet service  ...   \n",
              "6670          Yes               Yes             DSL                   No  ...   \n",
              "6754          Yes               Yes             DSL                  Yes  ...   \n",
              "\n",
              "         DeviceProtection          TechSupport          StreamingTV  \\\n",
              "488                   Yes                  Yes                  Yes   \n",
              "753   No internet service  No internet service  No internet service   \n",
              "936                   Yes                   No                  Yes   \n",
              "1082  No internet service  No internet service  No internet service   \n",
              "1340                  Yes                  Yes                  Yes   \n",
              "3331  No internet service  No internet service  No internet service   \n",
              "3826  No internet service  No internet service  No internet service   \n",
              "4380  No internet service  No internet service  No internet service   \n",
              "5218  No internet service  No internet service  No internet service   \n",
              "6670                  Yes                  Yes                  Yes   \n",
              "6754                   No                  Yes                   No   \n",
              "\n",
              "          StreamingMovies  Contract PaperlessBilling  \\\n",
              "488                    No  Two year              Yes   \n",
              "753   No internet service  Two year               No   \n",
              "936                   Yes  Two year               No   \n",
              "1082  No internet service  Two year               No   \n",
              "1340                   No  Two year               No   \n",
              "3331  No internet service  Two year               No   \n",
              "3826  No internet service  Two year               No   \n",
              "4380  No internet service  Two year               No   \n",
              "5218  No internet service  One year              Yes   \n",
              "6670                   No  Two year               No   \n",
              "6754                   No  Two year              Yes   \n",
              "\n",
              "                  PaymentMethod MonthlyCharges  TotalCharges  Churn  \n",
              "488   Bank transfer (automatic)          52.55           NaN     No  \n",
              "753                Mailed check          20.25           NaN     No  \n",
              "936                Mailed check          80.85           NaN     No  \n",
              "1082               Mailed check          25.75           NaN     No  \n",
              "1340    Credit card (automatic)          56.05           NaN     No  \n",
              "3331               Mailed check          19.85           NaN     No  \n",
              "3826               Mailed check          25.35           NaN     No  \n",
              "4380               Mailed check          20.00           NaN     No  \n",
              "5218               Mailed check          19.70           NaN     No  \n",
              "6670               Mailed check          73.35           NaN     No  \n",
              "6754  Bank transfer (automatic)          61.90           NaN     No  \n",
              "\n",
              "[11 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30e7a6b0-90d5-4e81-b966-6758583be181\">\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>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>...</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>488</th>\n",
              "      <td>4472-LVYGI</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>52.55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>753</th>\n",
              "      <td>3115-CZMZD</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</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>Mailed check</td>\n",
              "      <td>20.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>936</th>\n",
              "      <td>5709-LVOEQ</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>80.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1082</th>\n",
              "      <td>4367-NUYAO</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</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>Mailed check</td>\n",
              "      <td>25.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1340</th>\n",
              "      <td>1371-DWPAZ</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>56.05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3331</th>\n",
              "      <td>7644-OMVMY</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</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>Mailed check</td>\n",
              "      <td>19.85</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3826</th>\n",
              "      <td>3213-VVOLG</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</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>Mailed check</td>\n",
              "      <td>25.35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4380</th>\n",
              "      <td>2520-SGTTA</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</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>Mailed check</td>\n",
              "      <td>20.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5218</th>\n",
              "      <td>2923-ARZLG</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>...</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>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>19.70</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6670</th>\n",
              "      <td>4075-WKNIU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>73.35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6754</th>\n",
              "      <td>2775-SEFEE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>61.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-30e7a6b0-90d5-4e81-b966-6758583be181')\"\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-30e7a6b0-90d5-4e81-b966-6758583be181 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-30e7a6b0-90d5-4e81-b966-6758583be181');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can now observe that the column TotalCharges has **11** missing values.\n",
        "\n",
        "These observations have also a **tenure of 0**, even though MontlyCharges is **not null** for these entries. This information appeared to be contradictory, and therefore, we decide to remove those observations from the data set.\n"
      ],
      "metadata": {
        "id": "2LIer6rAPffd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "HxpEcauptndR"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUb56Fwptnab",
        "outputId": "76e51c45-3828-4c50-cc03-4a95b7aed16a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 7032 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7032 non-null   object \n",
            " 1   gender            7032 non-null   object \n",
            " 2   SeniorCitizen     7032 non-null   int64  \n",
            " 3   Partner           7032 non-null   object \n",
            " 4   Dependents        7032 non-null   object \n",
            " 5   tenure            7032 non-null   int64  \n",
            " 6   PhoneService      7032 non-null   object \n",
            " 7   MultipleLines     7032 non-null   object \n",
            " 8   InternetService   7032 non-null   object \n",
            " 9   OnlineSecurity    7032 non-null   object \n",
            " 10  OnlineBackup      7032 non-null   object \n",
            " 11  DeviceProtection  7032 non-null   object \n",
            " 12  TechSupport       7032 non-null   object \n",
            " 13  StreamingTV       7032 non-null   object \n",
            " 14  StreamingMovies   7032 non-null   object \n",
            " 15  Contract          7032 non-null   object \n",
            " 16  PaperlessBilling  7032 non-null   object \n",
            " 17  PaymentMethod     7032 non-null   object \n",
            " 18  MonthlyCharges    7032 non-null   float64\n",
            " 19  TotalCharges      7032 non-null   float64\n",
            " 20  Churn             7032 non-null   object \n",
            "dtypes: float64(2), int64(2), object(17)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have 7032 entries in the dataset"
      ],
      "metadata": {
        "id": "kOaimpBURlqm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Remove customerID column**\n",
        "\n",
        "The customerID column is useless to explain whether not the customer will churn. Therefore, we drop this column from the data set"
      ],
      "metadata": {
        "id": "EDUrste2R18l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(columns='customerID',inplace=True)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "FxATiuSGtnXZ",
        "outputId": "89d81336-6c6e-4b91-ed58-8ee94d8e8894"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  Female              0     Yes         No       1           No   \n",
              "1    Male              0      No         No      34          Yes   \n",
              "2    Male              0      No         No       2          Yes   \n",
              "3    Male              0      No         No      45           No   \n",
              "4  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
              "0  No phone service             DSL             No          Yes   \n",
              "1                No             DSL            Yes           No   \n",
              "2                No             DSL            Yes          Yes   \n",
              "3  No phone service             DSL            Yes           No   \n",
              "4                No     Fiber optic             No           No   \n",
              "\n",
              "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
              "0               No          No          No              No  Month-to-month   \n",
              "1              Yes          No          No              No        One year   \n",
              "2               No          No          No              No  Month-to-month   \n",
              "3              Yes         Yes          No              No        One year   \n",
              "4               No          No          No              No  Month-to-month   \n",
              "\n",
              "  PaperlessBilling              PaymentMethod  MonthlyCharges  TotalCharges  \\\n",
              "0              Yes           Electronic check           29.85         29.85   \n",
              "1               No               Mailed check           56.95       1889.50   \n",
              "2              Yes               Mailed check           53.85        108.15   \n",
              "3               No  Bank transfer (automatic)           42.30       1840.75   \n",
              "4              Yes           Electronic check           70.70        151.65   \n",
              "\n",
              "  Churn  \n",
              "0    No  \n",
              "1    No  \n",
              "2   Yes  \n",
              "3    No  \n",
              "4   Yes  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27697ad5-0c24-48f4-a801-a0d4a9afa258\">\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>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.50</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27697ad5-0c24-48f4-a801-a0d4a9afa258')\"\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-27697ad5-0c24-48f4-a801-a0d4a9afa258 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-27697ad5-0c24-48f4-a801-a0d4a9afa258');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Payment method denominations**\n",
        "As shown below, some payment method denominations contain in parenthesis the word **automatic**. These denominations are too long to be used as tick labels in further visualizations. Therefore, we remove this clarification in parenthesis from the entries of the PaymentMethod column.\n",
        "\n"
      ],
      "metadata": {
        "id": "qWOwWy__TC7c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.PaymentMethod.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imd_mlSntnUa",
        "outputId": "c8950672-b8ce-4c1b-f294-eb477e5c75c0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Electronic check', 'Mailed check', 'Bank transfer (automatic)',\n",
              "       'Credit card (automatic)'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['PaymentMethod'] = data.PaymentMethod.str.replace(' (automatic)','',regex=False)"
      ],
      "metadata": {
        "id": "35a4WjhdtnRW"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.PaymentMethod.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9p1Djp6tnN-",
        "outputId": "2d3b5e5a-7e1f-4913-dfd0-53ce51ee18ac"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Electronic check', 'Mailed check', 'Bank transfer', 'Credit card'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3. Data Visualization**\n"
      ],
      "metadata": {
        "id": "zrHrKYbBUxOS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.Churn.value_counts().plot(kind='barh')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "lOLOnEOZtnLX",
        "outputId": "9793766b-4f00-4d1f-9891-b36a60f4c339"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0e5339190>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKz0lEQVR4nO3bf6jd913H8dfb3P6w7eyPpY7Qbt4WClK34krRFYuMgXNr1T90SkfRsg26KcypDG0pSIcgUZhslcEWUHHaaTectHZutWv7h6K0JlubptbYbHRomAtTlvkD1G0f/zjf1OMlaXKTm3xv3vfxgMP9nu/33Pv9vm9Onvnme86pMUYAOPt929wHAMDGEHSAJgQdoAlBB2hC0AGaWJlrx9u3bx+rq6tz7R7grLRnz56vjjEuP9q22YK+urqa3bt3z7V7gLNSVX3pWNtccgFoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmliZa8fPHDyc1Ts/Ndfu2WAv7Lxl7kOALc8ZOkATgg7QhKADNCHoAE0IOkATgg7QhKADNCHoAE0IOkATgg7QhKADNCHoAE0IOkATgg7QhKADNCHoAE0IOkATgg7QhKADNCHoAE0IOkATgg7QhKADNPGSQa+Fv6qqNy+t+8mq+szpPzQA1mPlpTaOMUZVvSvJJ6rq8enxv57kTWfi4AA4cS8Z9CQZY+yrqj9L8itJLkzyh0nurqpXJzknyT1jjAeq6nuS/F6Sc7M48/+JMcbzp+/QAVh23KBP3pfkc0n+O8lDSR4bY7y9qi5J8mRVfTbJu5J8cIxxX1Wdm2Tb2h9SVXckuSNJtn3H5Rtx/ABMTijoY4z/qKr7k/x7kp9K8qNV9d5p8/lJXpXkb7I4c78yySePdnY+xtiVZFeSnLfjmrEBxw/A5ETP0JPkW9Otsricsn/N9ueq6okktyT586p65xjjsQ06TgCO42TetvhwkndXVSVJVb12+np1ki+OMe5N8kCS6zbsKAE4rpMJ+q9l8WLo3qp6drqfLC7F7Kuqp5K8OslHN+YQATgRJ3zJZYxxz9Lddx5l+84kOzfgmAA4CT4pCtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtDEylw7fs0VF2f3zlvm2j1AO87QAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZoQdIAmBB2gCUEHaELQAZpYmWvHzxw8nNU7PzXX7gFm8cLOW07bz3aGDtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtCEoAM0IegATQg6QBOCDtCEoAM0IegATawr6FU1qur9S/ffW1X3bPhRAbBu6z1D/68kP15V20/HwQBw8tYb9G8k2ZXkF9duqKrVqnqsqvZW1aNV9aoNOUIATsjJXEP/UJLbquriNet/O8nvjzGuS3JfknvXfmNV3VFVu6tq9zf/8/BJ7BqAY1l30McYX0/y0SQ/v2bTjUk+Ni3/QZKbjvK9u8YYN4wxbth2wdp/DwA4FSf7LpcPJHlHkgs38FgAOAUnFfQxxr8m+XgWUT/ir5PcOi3fluQvT+3QAFiPU3kf+vuTLL/b5d1J3lZVe5P8dJL3nMqBAbA+K+t58BjjoqXlryS5YOn+l5K8YeMODYD18ElRgCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaAJQQdoQtABmhB0gCYEHaCJlbl2/JorLs7unbfMtXuAdpyhAzQh6ABNCDpAE4IO0ISgAzQh6ABNCDpAE4IO0ISgAzQh6ABNCDpAE4IO0ISgAzQh6ABNCDpAE4IO0ISgAzQh6ABNCDpAE4IO0ISgAzQh6ABNCDpAE4IO0ISgAzQh6ABN1Bhjnh1X/VuS/bPsfF7bk3x17oOYwVaceyvOnJj7dPuuMcblR9uwcgZ2fiz7xxg3zLj/WVTVbnNvDVtx5sTccx6DSy4ATQg6QBNzBn3XjPuek7m3jq04c2Lu2cz2oigAG8slF4AmBB2giVmCXlVvqqr9VXWgqu6c4xg2UlX9blUdqqp9S+suq6pHqur56eul0/qqqnun2fdW1fVL33P79Pjnq+r2OWY5UVX1yqp6vKr+rqqerar3TOu7z31+VT1ZVU9Pc79vWn9VVT0xzXd/VZ07rT9vun9g2r669LPumtbvr6ofnmeiE1dV26rq81X10HR/K8z8QlU9U1VPVdXuad3mfY6PMc7oLcm2JF9IcnWSc5M8neTaM30cGzzTDya5Psm+pXW/meTOafnOJL8xLd+c5NNJKsnrkjwxrb8syRenr5dOy5fOPdtLzLwjyfXT8suS/EOSa7fA3JXkomn5nCRPTPN8PMmt0/oPJ/nZafnnknx4Wr41yf3T8rXTc/+8JFdNfye2zT3fcWb/pSQfS/LQdH8rzPxCku1r1m3a5/gcv6Abkzy8dP+uJHfN/Qe3AXOtrgn6/iQ7puUdWXyQKkk+kuStax+X5K1JPrK0/v89brPfkjyQ5Ie20txJLkjyuSTfn8UnBFem9S8+x5M8nOTGaXllelytfd4vP24z3pJcmeTRJG9I8tA0Q+uZp2M8WtA37XN8jksuVyT5x6X7/zSt6+YVY4wvT8v/nOQV0/Kx5j9rfy/Tf6lfm8XZavu5p0sPTyU5lOSRLM40vzbG+Mb0kOUZXpxv2n44yctz9s39gSS/nORb0/2Xp//MSTKS/EVV7amqO6Z1m/Y5PudH/7eMMcaoqpbvD62qi5L8SZJfGGN8vape3NZ17jHGN5N8b1VdkuRPk3z3zId0WlXVjyQ5NMbYU1Wvn/t4zrCbxhgHq+o7kzxSVX+/vHGzPcfnOEM/mOSVS/evnNZ185Wq2pEk09dD0/pjzX/W/V6q6pwsYn7fGOOT0+r2cx8xxvhaksezuNxwSVUdOUFanuHF+abtFyf5l5xdc/9Akh+rqheS/HEWl10+mN4zJ0nGGAenr4ey+Mf7+7KJn+NzBP1vk1wzvUJ+bhYvmjw4w3Gcbg8mOfJq9u1ZXGM+sv5nplfEX5fk8PTft4eTvLGqLp1eNX/jtG5TqsWp+O8keW6M8VtLm7rPffl0Zp6q+vYsXjd4Louwv2V62Nq5j/w+3pLksbG4kPpgklund4RcleSaJE+emSnWZ4xx1xjjyjHGahZ/Xx8bY9yWxjMnSVVdWFUvO7KcxXNzXzbzc3ymFxpuzuJdEV9IcvfcL3xswDx/lOTLSf4ni+tj78jimuGjSZ5P8tkkl02PrSQfmmZ/JskNSz/n7UkOTLe3zT3XcWa+KYvri3uTPDXdbt4Cc1+X5PPT3PuS/Oq0/uos4nQgySeSnDetP3+6f2DafvXSz7p7+n3sT/LmuWc7wflfn/97l0vrmaf5np5uzx5p1WZ+jvvoP0ATPikK0ISgAzQh6ABNCDpAE4IO0ISgAzQh6ABN/C+qkVWKJ//eFAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the data set is imbalanced"
      ],
      "metadata": {
        "id": "ZWrCICY-V7Ce"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Demographic Information**"
      ],
      "metadata": {
        "id": "o17iH2w-WPMe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "stacked bar chart for each demographic attribute **(gender, SeniorCitizen, Partner, Dependents)**, showing the percentage of Churn for each category of the attribute."
      ],
      "metadata": {
        "id": "zc6tBZsBWZ0w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "GiGkjUZCtmsi"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.gender.value_counts()\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvFTOibAtmVn",
        "outputId": "9de567cd-c6b2-43ec-b859-9bdc491c8da3"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      3549\n",
              "Female    3483\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data.Churn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "V0FpSxcVYw7x",
        "outputId": "7c8d345d-54b6-46a3-f61f-84a56bada9ab"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0d8afe590>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in ['gender', 'SeniorCitizen', 'Partner', 'Dependents']:\n",
        "  pd.crosstab(data[x],data.Churn).plot(kind='bar',stacked=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kaSlJfTyallr",
        "outputId": "f0a7e6a5-c870-43d8-e007-c53ec0cc25d8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEgCAYAAABYaaN4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaGElEQVR4nO3de5RV5Z3m8e/DRbDVxFuJNkWnkGFUcBS1ghrbHsTEC2rQjNcYpIkztGthxBVn1qBZGcpkmMQVjYkmUekBL1m2hASNqEQGL92JtlELJQRQR4KoxXgpQUnQBXL5zR/nhT5iFVVQp84uzvt81qrF3r99qd92lU/tes979lFEYGZmeehVdANmZlY9Dn0zs4w49M3MMuLQNzPLiEPfzCwjfYpuYEcOPPDAaGhoKLoNM7PdysKFC9+LiLq2tvXo0G9oaKC5ubnoNszMdiuSXm9vm4d3zMwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjHQY+pL6S3pO0h8kLZV0farfJek1SYvS14hUl6RbJC2XtFjSsWXnGi/p1fQ1vvsuy8zM2tKZd+RuAEZHxDpJfYGnJP0mbftvEfGr7fY/Exiavo4HbgOOl7Q/MBVoBAJYKGluRLxfiQsxs3Y0fbboDmpH09qiO+iyDu/0o2RdWu2bvnb0cVtjgXvScb8H9pV0CHA6sCAi1qSgXwCc0bX2zcxsZ3RqTF9Sb0mLgHcpBfezadO0NIRzs6R+qTYQeLPs8JZUa6++/feaKKlZUnNra+tOXo6Zme1Ipx64FhGbgRGS9gUekHQkcC3wNrAHMB3478B3utpQRExP56OxsXH3+ABf//lcWTXwJ7RZT7VTs3ci4gPgSeCMiHgrDeFsAO4ERqbdVgGDyg6rT7X26mZmViWdmb1Tl+7wkbQn8CXg5TROjyQB5wJL0iFzgcvSLJ4TgLUR8RYwHzhN0n6S9gNOSzUzM6uSzgzvHALcLak3pV8SsyPiYUlPSKoDBCwCrkj7zwPGAMuBj4AJABGxRtJ3gefTft+JiDWVuxQzM+tIh6EfEYuBY9qoj25n/wAmtbNtJjBzJ3s0M7MK8Ttyzcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy4tA3M8uIQ9/MLCMOfTOzjDj0zcwy0mHoS+ov6TlJf5C0VNL1qT5Y0rOSlkv6haQ9Ur1fWl+etjeUnevaVH9F0unddVFmZta2ztzpbwBGR8TRwAjgDEknADcAN0fEvwPeBy5P+18OvJ/qN6f9kDQMuBgYDpwB/ExS70pejJmZ7ViHoR8l69Jq3/QVwGjgV6l+N3BuWh6b1knbT5WkVJ8VERsi4jVgOTCyIldhZmad0qkxfUm9JS0C3gUWAH8CPoiITWmXFmBgWh4IvAmQtq8FDiivt3FM+feaKKlZUnNra+vOX5GZmbWrU6EfEZsjYgRQT+nu/PDuaigipkdEY0Q01tXVdde3MTPL0k7N3omID4AngROBfSX1SZvqgVVpeRUwCCBt/yywurzexjFmZlYFnZm9Uydp37S8J/Al4CVK4X9+2m088GBanpvWSdufiIhI9YvT7J7BwFDguUpdiJmZdaxPx7twCHB3mmnTC5gdEQ9LWgbMkvQ/gReBGWn/GcDPJS0H1lCasUNELJU0G1gGbAImRcTmyl6OmZntSIehHxGLgWPaqK+gjdk3EbEeuKCdc00Dpu18m2ZmVgl+R66ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpYRh76ZWUYc+mZmGXHom5llxKFvZpaRznxylpntxhrW/1PRLdSMlUU3UAG+0zczy4hD38wsIw59M7OMdDimL2kQcA8wAAhgekT8WFIT8F+A1rTrdRExLx1zLXA5sBm4KiLmp/oZwI+B3sD/jojvV/ZyiuEx08paWXQDZjWsMy/kbgKuiYgXJO0DLJS0IG27OSJuLN9Z0jDgYmA48NfAY5L+fdr8U+BLQAvwvKS5EbGsEhdiZmYd6zD0I+It4K20/BdJLwEDd3DIWGBWRGwAXpO0HBiZti2PiBUAkmalfR36ZmZVslNj+pIagGOAZ1PpSkmLJc2UtF+qDQTeLDusJdXaq2//PSZKapbU3Nrauv1mMzPrgk6HvqS9gTnA1RHxZ+A2YAgwgtJfAjdVoqGImB4RjRHRWFdXV4lTmplZ0qk3Z0nqSynw742I+wEi4p2y7f8IPJxWVwGDyg6vTzV2UDczsyro8E5fkoAZwEsR8cOy+iFlu50HLEnLc4GLJfWTNBgYCjwHPA8MlTRY0h6UXuydW5nLMDOzzujMnf5JwDjgj5IWpdp1wCWSRlCaxrkS+AeAiFgqaTalF2g3AZMiYjOApCuB+ZSmbM6MiKUVvBYzM+tAZ2bvPAWojU3zdnDMNGBaG/V5OzrOzMy6l9+Ra2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZaTD0Jc0SNKTkpZJWippcqrvL2mBpFfTv/uluiTdImm5pMWSji071/i0/6uSxnffZZmZWVs6c6e/CbgmIoYBJwCTJA0DpgCPR8RQ4PG0DnAmMDR9TQRug9IvCWAqcDwwEpi69ReFmZlVR4ehHxFvRcQLafkvwEvAQGAscHfa7W7g3LQ8FrgnSn4P7CvpEOB0YEFErImI94EFwBkVvRozM9uhnRrTl9QAHAM8CwyIiLfSpreBAWl5IPBm2WEtqdZeffvvMVFSs6Tm1tbWnWnPzMw60OnQl7Q3MAe4OiL+XL4tIgKISjQUEdMjojEiGuvq6ipxSjMzSzoV+pL6Ugr8eyPi/lR+Jw3bkP59N9VXAYPKDq9PtfbqZmZWJZ2ZvSNgBvBSRPywbNNcYOsMnPHAg2X1y9IsnhOAtWkYaD5wmqT90gu4p6WamZlVSZ9O7HMSMA74o6RFqXYd8H1gtqTLgdeBC9O2ecAYYDnwETABICLWSPou8Hza7zsRsaYiV2FmZp3SYehHxFOA2tl8ahv7BzCpnXPNBGbuTINmZlY5fkeumVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWkQ5DX9JMSe9KWlJWa5K0StKi9DWmbNu1kpZLekXS6WX1M1JtuaQplb8UMzPrSGfu9O8CzmijfnNEjEhf8wAkDQMuBoanY34mqbek3sBPgTOBYcAlaV8zM6uiPh3tEBG/ldTQyfONBWZFxAbgNUnLgZFp2/KIWAEgaVbad9lOd2xmZrusK2P6V0panIZ/9ku1gcCbZfu0pFp79U+RNFFSs6Tm1tbWLrRnZmbb29XQvw0YAowA3gJuqlRDETE9IhojorGurq5SpzUzMzoxvNOWiHhn67KkfwQeTqurgEFlu9anGjuom5lZlezSnb6kQ8pWzwO2zuyZC1wsqZ+kwcBQ4DngeWCopMGS9qD0Yu/cXW/bzMx2RYd3+pLuA0YBB0pqAaYCoySNAAJYCfwDQEQslTSb0gu0m4BJEbE5nedKYD7QG5gZEUsrfjVmZrZDnZm9c0kb5Rk72H8aMK2N+jxg3k51Z2ZmFeV35JqZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhlx6JuZZcShb2aWEYe+mVlGHPpmZhnpMPQlzZT0rqQlZbX9JS2Q9Gr6d79Ul6RbJC2XtFjSsWXHjE/7vyppfPdcjpmZ7Uhn7vTvAs7YrjYFeDwihgKPp3WAM4Gh6WsicBuUfkkAU4HjgZHA1K2/KMzMrHo6DP2I+C2wZrvyWODutHw3cG5Z/Z4o+T2wr6RDgNOBBRGxJiLeBxbw6V8kZmbWzXZ1TH9ARLyVlt8GBqTlgcCbZfu1pFp79U+RNFFSs6Tm1tbWXWzPzMza0uUXciMigKhAL1vPNz0iGiOisa6urlKnNTMzdj3030nDNqR/3031VcCgsv3qU629upmZVdGuhv5cYOsMnPHAg2X1y9IsnhOAtWkYaD5wmqT90gu4p6WamZlVUZ+OdpB0HzAKOFBSC6VZON8HZku6HHgduDDtPg8YAywHPgImAETEGknfBZ5P+30nIrZ/cdjMzLpZh6EfEZe0s+nUNvYNYFI755kJzNyp7szMrKI6DH2z3c3GjRtpaWlh/fr1RbfSZf3796e+vp6+ffsW3YrVCIe+1ZyWlhb22WcfGhoakFR0O7ssIli9ejUtLS0MHjy46HasRvjZO1Zz1q9fzwEHHLBbBz6AJA444ICa+IvFeg6HvtWk3T3wt6qV67Cew6FvZpYRh75l7+233+biiy9myJAhHHfccYwZM4bp06dz9tlnF92aWcU59C1rEcF5553HqFGj+NOf/sTChQv53ve+xzvvvNOl827atKlCHZpVlkPfsvbkk0/St29frrjiim21o48+mpNPPpl169Zx/vnnc/jhh3PppZdSehsKNDQ08N577wHQ3NzMqFGjAGhqamLcuHGcdNJJjBs3jqamJr7+9a8zatQoDj30UG655ZaqX5/Z9hz6lrUlS5Zw3HHHtbntxRdf5Ec/+hHLli1jxYoVPP300x2eb9myZTz22GPcd999ALz88svMnz+f5557juuvv56NGzdWtH+zneXQN2vHyJEjqa+vp1evXowYMYKVK1d2eMyXv/xl9txzz23rZ511Fv369ePAAw/koIMO6vKwkVlXOfQta8OHD2fhwoVtbuvXr9+25d69e28bp+/Tpw9btmwB+NQc+r322qtT5zArikPfsjZ69Gg2bNjA9OnTt9UWL17M7373u3aPaWho2PaLYs6cOd3eo1klOfQta5J44IEHeOyxxxgyZAjDhw/n2muv5eCDD273mKlTpzJ58mQaGxvp3bt3Fbs16zptnZHQEzU2NkZzc3PRbXSoYcojRbdQU1Z+/6wuHf/SSy9xxBFHVKib4nX1evzzWTld/dmsFkkLI6KxrW2+0zczy4hD38wsIw59M7OMOPTNzDLi0Dczy4hD38wsI136uERJK4G/AJuBTRHRKGl/4BdAA7ASuDAi3lfp0yB+DIwBPgL+PiJe6Mr3N+uMSk9Z7My0PUl885vf5KabbgLgxhtvZN26dTQ1NVW0F7OdVYk7/VMiYkTZnNApwOMRMRR4PK0DnAkMTV8Tgdsq8L3NeqR+/fpx//33b3sap1lP0R3DO2OBu9Py3cC5ZfV7ouT3wL6SDumG729WuD59+jBx4kRuvvnmT21buXIlo0eP5qijjuLUU0/ljTfeKKBDy1VXQz+A/yNpoaSJqTYgIt5Ky28DA9LyQODNsmNbUu0TJE2U1CypubW1tYvtmRVn0qRJ3Hvvvaxdu/YT9W984xuMHz+exYsXc+mll3LVVVcV1KHlqKuh/7cRcSyloZtJkv6ufGOUnvGwU895iIjpEdEYEY11dXVdbM+sOJ/5zGe47LLLPvXhKc888wxf/epXARg3bhxPPfVUEe1ZproU+hGxKv37LvAAMBJ4Z+uwTfr33bT7KmBQ2eH1qWZWs66++mpmzJjBhx9+WHQrZkAXQl/SXpL22boMnAYsAeYC49Nu44EH0/Jc4DKVnACsLRsGMqtJ+++/PxdeeCEzZszYVvvCF77ArFmzALj33ns5+eSTi2rPMtSVKZsDgAdKMzHpA/xTRDwq6XlgtqTLgdeBC9P+8yhN11xOacrmhC58b7NOK/rJiNdccw0/+clPtq3feuutTJgwgR/84AfU1dVx5513Ftid5WaXQz8iVgBHt1FfDZzaRj2ASbv6/cx2J+vWrdu2PGDAAD766KNt65/73Od44oknimjLzO/INTPLiUPfzCwjDn0zs4w49M3MMuLQNzPLiEPfzCwjXXq0stluoemzFT7f2h1ujghOPvlkvvWtb3HmmWcC8Mtf/pIZM2bw6KOPVrYXs53k0DerMEncfvvtXHDBBZxyyils2rSJ6667zoFvPYJD36wbHHnkkZxzzjnccMMNfPjhh3zta19j2rRpLFmyhI0bN9LU1MTYsWNZunQpEyZM4OOPP2bLli3MmTOHoUOHFt2+1TCHvlk3mTp1Ksceeyx77LEHZ599NqNHj2bmzJl88MEHjBw5ki9+8YvcfvvtTJ48mUsvvZSPP/6YzZs3F9221TiHvlk32WuvvbjooovYe++9mT17Ng899BA33ngjAOvXr+eNN97gxBNPZNq0abS0tPCVr3zFd/nW7Rz6Zt2oV69e9OrVi4hgzpw5HHbYYZ/YfsQRR3D88cfzyCOPMGbMGO644w5Gjx5dULeWA0/ZNKuC008/nVtvvZXScwfhxRdfBGDFihUceuihXHXVVYwdO5bFixcX2aZlwHf6Vvs6mGJZDd/+9re5+uqrOeqoo9iyZQuDBw/m4YcfZvbs2fz85z+nb9++HHzwwVx33XVFt2o1zqFv1o2ampq2Ld9xxx2f2j5lyhSmTJlSxY4sdx7eMTPLiEPfzCwjDn2rSVtfMN3d1cp1WM/h0Lea079/f1avXr3bB2ZEsHr1avr37190K1ZD/EKu1Zz6+npaWlpobW0tupUu69+/P/X19UW3YTXEoW81p2/fvgwePLjoNsx6pKoP70g6Q9IrkpZL8lw1M7MqqmroS+oN/BQ4ExgGXCJpWDV7MDPLWbXv9EcCyyNiRUR8DMwCxla5BzOzbFV7TH8g8GbZegtwfPkOkiYCE9PqOkmvVKm3HBwIvFd0Ex3RDUV3YAXp8T+fu9HP5ufa29DjXsiNiOnA9KL7qEWSmiOiseg+zNrin8/qqPbwzipgUNl6faqZmVkVVDv0nweGShosaQ/gYmBulXswM8tWVYd3ImKTpCuB+UBvYGZELK1mD5nzsJn1ZP75rALt7m9VNzOzzvOzd8zMMuLQNzPLiEPfzCwjDn0zK5SkPSUdVnQfuXDo1ziVfE3S/0jrfyNpZNF9mQFIOgdYBDya1kdI8jTubuTQr30/A04ELknrf6H00DuznqCJ0jO5PgCIiEWAn4vdjXrcYxis4o6PiGMlvQgQEe+nN8aZ9QQbI2KtpPKa55F3I4d+7duYHmkdAJLqgC3FtmS2zVJJXwV6SxoKXAX8a8E91TQP79S+W4AHgIMkTQOeAv5XsS2ZbfMNYDiwAbgP+DNwdaEd1Ti/IzcDkg4HTgUEPB4RLxXckpkVxKFfoyTtv6PtEbGmWr2YbU/SQ+xg7D4ivlzFdrLiMf3atZDS/1Tlr5BtXQ/g0CKaMktuLLqBXPlO38wsI77Tz4Ck/YChQP+ttYj4bXEdmZWkGTvfA4bxyZ9P/yXaTRz6NU7SfwYmU/qUskXACcAzwOgi+zJL7gSmAjcDpwAT8KzCbuX/uLVvMvB54PWIOAU4hvTuR7MeYM+IeJzSUPPrEdEEnFVwTzXNd/q1b31ErJeEpH4R8bIfbmU9yAZJvYBX06fqrQL2LrinmubQr30tkvYFfg0skPQ+8HrBPZltNRn4K0rvxP0upWHH8YV2VOM8eycjkv4j8Fng0Yj4uOh+zKz6HPoZSLN3BlH2l11EvFBcR5a7jh6f7DdndR8P79Q4Sd8F/h5Ywb89aC3w7B0r1onAm5Set/Msn3wToXUj3+nXOEmvAP/BwznWk6Qnv36J0uc8HAU8AtwXEUsLbSwDnrJZ+5YA+xbdhFm5iNgcEY9GxHhK7x1ZDvxzmsFj3ch3+jVOUiPwIKXw37C17jFTK5qkfpTm5F8CNABzgZkRsarIvmqdQ7/GSVoK3AH8kbIPT4mIfymsKcuepHuAI4F5wKyIWFJwS9lw6Nc4Sc9HxOeL7sOsnKQtwIdptTyEBEREfKb6XeXBoV/jJP2Q0rDOXD45vOMpm2YZcujXOElPtlGOiPCUTbMMOfTNzDLiKZs1TtIASTMk/SatD5N0edF9mVkxHPq17y5gPvDXaf3/AlcX1o2ZFcqhX/sOjIjZpOmaEbEJ2FxsS2ZWFId+7ftQ0gGkaXGSTgDWFtuSmRXFD1yrfd+kNF1ziKSngTrg/GJbMrOiePZOjZL0NxHxRlruAxxG6Y0vr0TExkKbM7PCeHindv26bPkXEbE0IpY48M3y5tCvXeXPJz+0sC7MrEdx6NeuaGfZzDLmMf0aJWkzpQdaCdgT+GjrJvxAK7NsOfTNzDLi4R0zs4w49M3MMuLQN6sgSXdJ8pvfrMdy6JsVKL1xzqxqHPqWLUnflvSKpKck3Sfpv0oaIulRSQsl/U7S4WnfuyTdIulfJa3Yejevkp+k8zwGHFR2/uMk/Us613xJh6T6P0v6kaRmYHIR12758l2GZUnS54H/BBwN9AVeABYC04ErIuJVSccDPwO2fsrYIcDfAodTep7Rr4DzKD3iYhgwAFgGzJTUF7gVGBsRrZIuAqYBX0/n2iMiGrv9Qs2249C3XJ0EPBgR64H1kh4C+gNfAH4pbXtDc7+yY34dEVuAZZIGpNrfAfdFxGbg/0l6ItUPA44EFqRz9QbeKjvXL7rhmsw65NA3+ze9gA8iYkQ72zeULaudfcq3L42IE9vZ/uHONmdWCR7Tt1w9DZwjqb+kvYGzKb1r+TVJF8C28fqjOzjPb4GLJPVOY/anpPorQJ2kE9O5+koa3i1XYrYTHPqWpYh4ntK4/GLgN8AfKX24zKXA5ZL+ACwFxnZwqgeAVymN5d8DPJPO/zGlzy24IZ1rEaWhI7NC+TEMli1Je0fEOkl/RemOfWJEvFB0X2bdyWP6lrPpkoZRegH3bge+5cB3+mZmGfGYvplZRhz6ZmYZceibmWXEoW9mlhGHvplZRv4/xuZvmUtk5NkAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can extract the following conclusions by analyzing demographic attributes:\n",
        "\n",
        "The churn rate of senior citizens is almost double that of young citizens.\n",
        "\n",
        "We do not expect gender to have significant predictive power. A similar percentage of churn is shown both when a customer is a man or a woman.\n",
        "\n",
        "Customers with a partner churn less than customers with no partner.\n",
        "\n",
        "Customers with dependents churn less than customers with no dependents."
      ],
      "metadata": {
        "id": "yL2pQP2FiKc3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Customer Account Information — Categorical variables**"
      ],
      "metadata": {
        "id": "2cxf64kojmBW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we evaluate the Churn for each category of the customer account attributes (Contract, PaperlessBilling, PaymentMethod)."
      ],
      "metadata": {
        "id": "A4J8Ne5yj4xg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for x in ['Contract', 'PaperlessBilling', 'PaymentMethod']:\n",
        "  pd.crosstab(data[x],data.Churn).plot(kind='bar',stacked=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "id": "tmSwW9EmghHB",
        "outputId": "de68718b-ba13-4c4e-d19c-e34c0fc92e7d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAFNCAYAAADsA7/8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5QV5Z3u8e8jIBjxbgcN7dhoMBEcRe2gxjgHMfFCHMEs4zXI0awhmcEgo3MmYE4GkgyJrnjJ6CQ6zAFHHSODUVfwcmTwNtHEKE1E5GKOhBBtjhdEZUQPyOV3/qi3mS3py+5bVbf1fNbaq6veqtr129nm2cVbb1UpIjAzs3LYpegCzMwsPw59M7MSceibmZWIQ9/MrEQc+mZmJeLQNzMrkb7VriipD9AArI2IMyUNAeYC+wGLgfER8YGk/sDtwLHAeuC8iFiT3mMa8FVgGzA5Iha0ts/9998/6urq2v2hzMzKbPHixW9GRE1zy6oOfeByYCWwZ5q/BrghIuZKuoUszG9Of9+OiE9KOj+td56kYcD5wHDgE8Ajkg6LiG0t7bCuro6GhoZ2lGhmZpL+0NKyqrp3JNUCXwT+V5oXMBr4WVrlNmBcmh6b5knLT0nrjwXmRsTmiPg9sAoY2b6PYmZmnVFtn/6PgL8Ftqf5/YB3ImJrmm8EBqfpwcArAGn5hrT+jvZmttlB0kRJDZIa1q1b146PYmZmbWkz9CWdCbwREYtzqIeImBUR9RFRX1PTbJeUmZl1UDV9+icCZ0kaAwwg69P/B2BvSX3T0XwtsDatvxY4CGiU1BfYi+yEblN7k8ptzMxysWXLFhobG9m0aVPRpXTagAEDqK2tpV+/flVv02boR8Q0YBqApFHA30TERZLuBs4hG8EzAfh52mR+mn86LX8sIkLSfOCnkq4nO5E7FHi26krNzLpAY2Mje+yxB3V1dWSnG3uniGD9+vU0NjYyZMiQqrfrzDj9bwJXSFpF1mc/O7XPBvZL7VcAU1OBy4F5wArgYWBSayN3zMy6w6ZNm9hvv/16deADSGK//fZr979Y2jNkk4h4AngiTa+mmdE3EbEJ+HIL288EZrarQjOzLtbbA79JRz6Hr8g1MyuRdh3pf+TN2KvoCrrXjA1FV2DWK7322mtMmTKFRYsWsffeezNo0CDGjRvH/PnzeeCBB4our118pG9m1oqI4Oyzz2bUqFH87ne/Y/HixfzgBz/g9ddf79T7bt26te2VuoFD38ysFY8//jj9+vXj61//+o62o446ipNOOomNGzdyzjnn8OlPf5qLLrqIpsfP1tXV8eabbwLQ0NDAqFGjAJgxYwbjx4/nxBNPZPz48cyYMYNLL72UUaNGccghh3DjjTd2++dx6JuZtWLZsmUce+yxzS577rnn+NGPfsSKFStYvXo1v/zlL9t8vxUrVvDII49w1113AfDiiy+yYMECnn32Wb7zne+wZcuWLq1/Zw59M7MOGjlyJLW1teyyyy6MGDGCNWvWtLnNWWedxW677bZj/otf/CL9+/dn//335+Mf/3inu43a4tA3M2vF8OHDWby4+bvQ9O/ff8d0nz59dvTT9+3bl+3bs1uV7TyOfvfdd6/qPbqLQ9/MrBWjR49m8+bNzJo1a0fb0qVLefLJJ1vcpq6ubscPxT333NPtNbaHQ9/MrBWSuO+++3jkkUc49NBDGT58ONOmTeOAAw5ocZvp06dz+eWXU19fT58+fXKstm1qOtvcE9XX10euD1HxOH2zj7yVK1dy+OGHF11Gl2nu80haHBH1za3vI30zsxJx6JuZlYhD38ysRBz6ZmYl4tA3MysRh76ZWYn41spmVmp1Ux/s0vdbc/UX21xHEldccQXXXXcdANdeey0bN25kxowZXVpLc3ykb2aWs/79+3PvvffuuBNnntoMfUkDJD0r6XlJyyV9J7X/i6TfS1qSXiNSuyTdKGmVpKWSjql4rwmSXkqvCd33sczMeq6+ffsyceJEbrjhhj9atmbNGkaPHs2RRx7JKaecwssvv9yl+67mSH8zMDoijgJGAKdLOj4t+x8RMSK9lqS2M4Ch6TURuBlA0r7AdOA4smfrTpe0T9d9FDOz3mPSpEnceeedbNjw4Svlv/GNbzBhwgSWLl3KRRddxOTJk7t0v22GfmQ2ptl+6dXavRvGAren7X4N7C3pQOA0YGFEvBURbwMLgdM7V76ZWe+05557cvHFF//Rg1OefvppLrzwQgDGjx/PU0891aX7rapPX1IfSUuAN8iC+5m0aGbqwrlBUtP9QQcDr1Rs3pjaWmrfeV8TJTVIali3bl07P46ZWe8xZcoUZs+ezXvvvZfbPqsK/YjYFhEjgFpgpKQjgGnAp4HPAPsC3+yKgiJiVkTUR0R9TU1NV7ylmVmPtO+++3Luuecye/bsHW2f/exnmTt3LgB33nknJ510Upfus11DNiPiHUmPA6dHxLWpebOkW4G/SfNrgYMqNqtNbWuBUTu1P9GBms3Mukw1Qyy705VXXsk//uM/7pi/6aabuOSSS/jhD39ITU0Nt956a5fur83Ql1QDbEmBvxvwBeAaSQdGxKuSBIwDlqVN5gOXSZpLdtJ2Q1pvAfD9ipO3p5L9a8HMrFQ2bty4Y3rQoEG8//77O+YPPvhgHnvssW7bdzVH+gcCt0nqQ9YdNC8iHpD0WPpBELAEaHpU/EPAGGAV8D5wCUBEvCXpe8CitN53I+KtrvsoZmbWljZDPyKWAkc30z66hfUDmNTCsjnAnHbWaGZmXcRX5JqZlYhD38ysRBz6ZmYl4tA3MysR31rZzMptxl5d/H4bWl0cEZx00kl861vf4owzzgDg7rvvZvbs2Tz88MNdW0szHPpmZjmSxC233MKXv/xlTj75ZLZu3cpVV12VS+CDQ9/MLHdHHHEEf/7nf84111zDe++9x1e+8hVmzpzJsmXL2LJlCzNmzGDs2LEsX76cSy65hA8++IDt27dzzz33MHTo0E7t26FvZlaA6dOnc8wxx7Drrrty5plnMnr0aObMmcM777zDyJEj+fznP88tt9zC5ZdfzkUXXcQHH3zAtm3bOr1fh76ZWQF23313zjvvPAYOHMi8efO4//77ufba7JZmmzZt4uWXX+aEE05g5syZNDY28qUvfanTR/ng0DczK8wuu+zCLrvsQkRwzz338KlPfepDyw8//HCOO+44HnzwQcaMGcM//dM/MXp0szdDqH6fndrazMw67bTTTuOmm24iu4sNPPfccwCsXr2aQw45hMmTJzN27FiWLl3a6X35SN/Myq2NIZZ5+Pa3v82UKVM48sgj2b59O0OGDOGBBx5g3rx53HHHHfTr148DDjiAq666qtP7UtMvS09UX18fDQ0N+e2wq8fr9jQ94D9us6KtXLmSww8/vOgyukxzn0fS4oiob259d++YmZWIQ9/MrEQc+mZWOj25W7s9OvI5HPpmVioDBgxg/fr1vT74I4L169czYMCAdm3n0TtmViq1tbU0Njaybt26okvptAEDBlBbW9uubap5MPoA4BdA/7T+zyJiuqQhwFxgP2AxMD4iPpDUH7gdOBZYD5wXEWvSe00DvgpsAyZHxIJ2VWtm1kn9+vVjyJAhRZdRmGq6dzYDoyPiKGAEcLqk44FrgBsi4pPA22RhTvr7dmq/Ia2HpGHA+cBw4HTgJ+lh62ZmlpM2Qz8yG9Nsv/QKYDTws9R+GzAuTY9N86Tlp0hSap8bEZsj4vfAKmBkl3wKMzOrSlUnciX1kbQEeANYCPwOeCcitqZVGoHBaXow8ApAWr6BrAtoR3sz21Tua6KkBkkNH4U+NzOznqSq0I+IbRExAqglOzr/dHcVFBGzIqI+Iupramq6azdmZqXUriGbEfEO8DhwArC3pKYTwbXA2jS9FjgIIC3fi+yE7o72ZrYxM7MctBn6kmok7Z2mdwO+AKwkC/9z0moTgJ+n6flpnrT8scgGxM4HzpfUP438GQo821UfxMzM2lbNOP0DgdvSSJtdgHkR8YCkFcBcSX8PPAfMTuvPBu6QtAp4i2zEDhGxXNI8YAWwFZgUEZ1/DIyZmVWtzdCPiKXA0c20r6aZ0TcRsQn4cgvvNROY2f4yzcysK/g2DGZmJeLQNzMrEYe+mVmJOPTNzErEoW9mViIOfTOzEnHom5mViEPfzKxEHPpmZiXi0DczKxGHvplZiTj0zcxKxKFvZlYiDn0zsxJx6JuZlYhD38ysRBz6ZmYlUs0zcg+S9LikFZKWS7o8tc+QtFbSkvQaU7HNNEmrJP1W0mkV7aentlWSpnbPRzIzs5ZU84zcrcCVEfEbSXsAiyUtTMtuiIhrK1eWNIzsubjDgU8Aj0g6LC3+MdmD1RuBRZLmR8SKrvggZmbWtmqekfsq8GqaflfSSmBwK5uMBeZGxGbg9+kB6U3P0l2Vnq2LpLlpXYe+mVlO2tWnL6mO7CHpz6SmyyQtlTRH0j6pbTDwSsVmjamtpfad9zFRUoOkhnXr1rWnPDMza0PVoS9pIHAPMCUi/hO4GTgUGEH2L4HruqKgiJgVEfURUV9TU9MVb2lmZkk1ffpI6kcW+HdGxL0AEfF6xfJ/Bh5Is2uBgyo2r01ttNJuZmY5qGb0joDZwMqIuL6i/cCK1c4GlqXp+cD5kvpLGgIMBZ4FFgFDJQ2RtCvZyd75XfMxzMysGtUc6Z8IjAdekLQktV0FXCBpBBDAGuBrABGxXNI8shO0W4FJEbENQNJlwAKgDzAnIpZ34WcxM7M2VDN65ylAzSx6qJVtZgIzm2l/qLXtzMyse/mKXDOzEnHom5mViEPfzKxEHPpmZiXi0DczKxGHvplZiTj0zcxKxKFvZlYiDn0zsxKp6oZrZVG36adFl9Ct1hRdgJkVzkf6ZmYl4tA3MysRh76ZWYk49M3MSsShb2ZWIg59M7MSceibmZWIQ9/MrESqeTD6QZIel7RC0nJJl6f2fSUtlPRS+rtPapekGyWtkrRU0jEV7zUhrf+SpAnd97HMzKw51RzpbwWujIhhwPHAJEnDgKnAoxExFHg0zQOcAQxNr4nAzZD9SADTgeOAkcD0ph8KMzPLR5uhHxGvRsRv0vS7wEpgMDAWuC2tdhswLk2PBW6PzK+BvSUdCJwGLIyItyLibWAhcHqXfhozM2tVu/r0JdUBRwPPAIMi4tW06DVgUJoeDLxSsVljamupfed9TJTUIKlh3bp17SnPzMzaUHXoSxoI3ANMiYj/rFwWEQFEVxQUEbMioj4i6mtqarriLc3MLKkq9CX1Iwv8OyPi3tT8euq2If19I7WvBQ6q2Lw2tbXUbmZmOalm9I6A2cDKiLi+YtF8oGkEzgTg5xXtF6dRPMcDG1I30ALgVEn7pBO4p6Y2MzPLSTX30z8RGA+8IGlJarsKuBqYJ+mrwB+Ac9Oyh4AxwCrgfeASgIh4S9L3gEVpve9GxFtd8inMzKwqbYZ+RDwFqIXFpzSzfgCTWnivOcCc9hRoZmZdx1fkmpmViEPfzKxEHPpmZiXi0DczKxGHvplZiTj0zcxKxKFvZlYiDn0zsxJx6JuZlYhD38ysRBz6ZmYlUs0N18zMut+MvYquoPvM2FB0BTv4SN/MrEQc+mZmJeLuHTPrEeo2/bToErrNmqILqOAjfTOzEnHom5mViEPfzKxEqnkw+hxJb0haVtE2Q9JaSUvSa0zFsmmSVkn6raTTKtpPT22rJE3t+o9iZmZtqeZI/1+A05tpvyEiRqTXQwCShgHnA8PTNj+R1EdSH+DHwBnAMOCCtK6ZmeWomgej/0JSXZXvNxaYGxGbgd9LWgWMTMtWRcRqAElz07or2l2xmZl1WGf69C+TtDR1/+yT2gYDr1Ss05jaWmr/I5ImSmqQ1LBu3bpOlGdmZjvr6Dj9m4HvAZH+Xgdc2hUFRcQsYBZAfX19dMV7Wkl8lC/jhx51Kb/1Xh0K/Yh4vWla0j8DD6TZtcBBFavWpjZaaTczs5x0qHtH0oEVs2cDTSN75gPnS+ovaQgwFHgWWAQMlTRE0q5kJ3vnd7xsMzPriDaP9CXdBYwC9pfUCEwHRkkaQda9swb4GkBELJc0j+wE7VZgUkRsS+9zGbAA6APMiYjlXf5pzMysVdWM3rmgmebZraw/E5jZTPtDwEPtqs7MzLqUr8g1MysRh76ZWYk49M3MSsShb2ZWIn6Iin1kfJQfwgE960Ec1nv5SN/MrEQc+mZmJeLQNzMrEYe+mVmJOPTNzErEoW9mViIOfTOzEnHom5mViEPfzKxEHPpmZiXi0DczKxGHvplZiTj0zcxKpM3QlzRH0huSllW07StpoaSX0t99Ursk3ShplaSlko6p2GZCWv8lSRO65+OYmVlrqjnS/xfg9J3apgKPRsRQ4NE0D3AGMDS9JgI3Q/YjQfZA9eOAkcD0ph8KMzPLT5uhHxG/AN7aqXkscFuavg0YV9F+e2R+Dewt6UDgNGBhRLwVEW8DC/njHxIzM+tmHe3THxQRr6bp14BBaXow8ErFeo2praX2PyJpoqQGSQ3r1q3rYHlmZtacTp/IjYgAogtqaXq/WRFRHxH1NTU1XfW2ZmZGx0P/9dRtQ/r7RmpfCxxUsV5tamup3czMctTR0J8PNI3AmQD8vKL94jSK53hgQ+oGWgCcKmmfdAL31NRmZmY5avPB6JLuAkYB+0tqJBuFczUwT9JXgT8A56bVHwLGAKuA94FLACLiLUnfAxal9b4bETufHDYzs27WZuhHxAUtLDqlmXUDmNTC+8wB5rSrOjMz61K+ItfMrEQc+mZmJeLQNzMrEYe+mVmJOPTNzErEoW9mViIOfTOzEnHom5mViEPfzKxEHPpmZiXi0DczKxGHvplZiTj0zcxKxKFvZlYiDn0zsxJx6JuZlYhD38ysRDoV+pLWSHpB0hJJDaltX0kLJb2U/u6T2iXpRkmrJC2VdExXfAAzM6teVxzpnxwRIyKiPs1PBR6NiKHAo2ke4AxgaHpNBG7ugn2bmVk7dEf3zljgtjR9GzCuov32yPwa2FvSgd2wfzMza0FnQz+Af5e0WNLE1DYoIl5N068Bg9L0YOCVim0bU9uHSJooqUFSw7p16zpZnpmZVerbye0/FxFrJX0cWCjpxcqFERGSoj1vGBGzgFkA9fX17drWzMxa16kj/YhYm/6+AdwHjAReb+q2SX/fSKuvBQ6q2Lw2tZmZWU46HPqSdpe0R9M0cCqwDJgPTEirTQB+nqbnAxenUTzHAxsquoHMzCwHneneGQTcJ6npfX4aEQ9LWgTMk/RV4A/AuWn9h4AxwCrgfeCSTuzbzMw6oMOhHxGrgaOaaV8PnNJMewCTOro/MzPrPF+Ra2ZWIg59M7MSceibmZWIQ9/MrEQc+mZmJeLQNzMrEYe+mVmJOPTNzErEoW9mViIOfTOzEnHom5mViEPfzKxEHPpmZiXi0DczKxGHvplZiTj0zcxKxKFvZlYiDn0zsxLJPfQlnS7pt5JWSZqa9/7NzMos19CX1Af4MXAGMAy4QNKwPGswMyuzvI/0RwKrImJ1RHwAzAXG5lyDmVlp9c15f4OBVyrmG4HjKleQNBGYmGY3SvptTrUVYX/gzbx2pmvy2lNp+PvrvT7q393BLS3IO/TbFBGzgFlF15EHSQ0RUV90HdYx/v56rzJ/d3l376wFDqqYr01tZmaWg7xDfxEwVNIQSbsC5wPzc67BzKy0cu3eiYitki4DFgB9gDkRsTzPGnqYUnRjfYT5++u9SvvdKSKKrsHMzHLiK3LNzErEoW9mViIOfTOzEnHom1VBUh9J1xZdh7Vf+u4eL7qOnsKhb1aFiNgGfK7oOqz90ne3XdJeRdfSE/S4K3I/6iSdCMwgu0y6LyAgIuKQIuuyqjwnaT5wN/BeU2NE3FtcSValjcALkhby4e9ucnElFcOhn7/ZwF8Di4FtBddi7TMAWA+MrmgLwKHf892LvyfA4/RzJ+mZiDiu7TXNzLqeQz8nko5Jk+eSXY18L7C5aXlE/KaIuqx6kgYAXwWGkx31AxARlxZWlFVF0lDgB2TP8aj87krXrerunfxct9N85R3+gg93GVjPdAfwInAa8F3gImBloRVZtW4FpgM3ACcDl1DSgSw+0s+ZpEMiYnVbbdbzSHouIo6WtDQijpTUD3gyIo4vujZrnaTFEXGspBci4k8r24quLW+l/KUr2M+aabs79yqsI7akv+9IOgLYC/h4gfVY9TZL2gV4SdJlks4GBhZdVBHcvZMTSZ8m6wveS9KXKhbtSUUfo/VosyTtA3yb7JbgA4G/K7Ykq9LlwMeAycD3yLp4JhRaUUHcvZMTSWOBccBZfPgZAu8CcyPiV4UUZlYikj4WEe8XXUeRHPo5k3RCRDxddB3WfpIGAd8HPhERZ0gaBpwQEbMLLs3aIOkEsmtkBkbEn0g6CvhaRPxVwaXlzqGfM0k1wF8AdVR0r3nYX88n6X+TjQL5VkQcJakv8FzTiUHruSQ9A5wDzI+Io1Pbsog4otjK8uc+/fz9HHgSeARfkdvb7B8R8yRNgx1PgvN32EtExCuSKptK+d059PP3sYj4ZtFFWIe8J2k/susqkHQ8sKHYkqxKr0j6LBBpqO3llPQaCw/ZzN8DksYUXYR1yJVkJ+EPlfRL4HbgG8WWZFX6OjAJGAysBUak+dJxn37OJL0L7A58wH+N+46I2LO4qqxaqR//U2R3R/1tRGxpYxPrASTtFxHri66jJ3Dom1VJ0mKyESB3RcTbRddj1ZP0ErAEmAM8HCUOPnfvFEDSWZKuTa8zi67HqnYeWffAIklzJZ2mnc4MWo91GDALuJjsqtzvSzqs4JoK4SP9nEm6GvgMcGdqugBoiIhpxVVl7ZEu5z8TuJlsBMitwD9ExFuFFmZVkXQy8K9k3azPA1PLdO2MQz9nkpYCIyJie5rvQzbW+8hiK7NqSDqS7A6NY4AFZD/enwPGR8SIImuzlqVRV18BxgOvk3XTzSc7oXt3RAwpsLxcechmMfYGmo4K/dzOXiL16b9DFhhTI6LpeQjPpMdgWs/1NNmtscdFRGNFe4OkWwqqqRA+0s+ZpAuAq4HHyUaA/BlZgPxboYVZm3wL7N5Lksp88raSQ78Akg4k69cHeDYiXiuyHjMrD4/eKUZN+tsX+OxOt1o2M+s27tPPmaQ5wJHAcmB7ag6yZ+aamXUrd+/kTNKKiBhWdB3WfpI+RnYrhj+JiL9ID9v+VEQ8UHBp1gZJtcBNZCOtguymh5fvdFK3FNy9k7+n033Yrfe5FdgMnJDm1wJ/X1w51g63kg3RPBD4BHB/aisdH+nnTNJ/I/uP7zWyABHZvXc8Tr+Hk9QQEfVND0hPbc9HxFFF12atk7Rk5+sommsrA/fp52822QUiL/BfffrWO3wgaTf+69bKh5L9cFvPt17SV4C70vwFQClvwOYj/ZxJejoiTmh7TetpJH0B+J/AMODfgROB/x4RTxRZl7VN0sFkffonkP1o/wqYHBEvF1pYARz6OZP0E7Ircu+n4igxIjx6pxdIl/MfT9Yt9+uIeLPgkqwKkgZExKai6+gJ3L2Tv93Iwv7UijYP2ew9BgBvk/1/Z5gkIuIXBddkbVsm6XWyUTtPAk9FRCmfeuYjfbMqSbqG7PbKH7rGIiLOKq4qq5akPwFOIuuWGwO84xO5litJv4mIY4quw6o2jmxcvk/e9jJpnP6JZKF/FNkP91OFFlUQh36x/ACO3mU10A+P2OmNXgYWAd+PiK8XXUyRHPrFerDoAqxd3geWSHqUD5+En1xcSdYaSX0jYitwNNnVuBdKmgq8BPxHRMwutMACuE+/AJJ2JXt8G/jh2r2GpAnNtUfEbXnXYtWp7EKVNJAs+E8ie6AKEXFwgeUVwkf6OZM0CrgNWEPWvXOQpAkeAdIr/BvwyTS9ykMAew9JDUB/svH5vwD+LCL+UGxVxfCRfs7S05cujIjfpvnDgLsi4thiK7OWSOoLfB+4FPgD6cea7N4t3/K/1HouSY3A9UAfPnxX22wi4voi6iqSb7iWv35NgQ8QEf+H7OSg9Vw/BPYFhkTEsam74FCyi+yuLbQya0sfYCDwsfR3ILBHxat0fKSfs3Q//e3Av6ami4A+EXFpcVVZayS9BBy28+P20kPtX4yIocVUZm3xsOg/5j79/P0lMAloGvHxJPDj4sqxKkRzz1eNiG2SfNTUs3lY9E7cvZO/r0fE9RHxpfS6geyHwHquFZIu3rkx3bXxxQLqseqdUnQBPY27d3LW3D83K+/Pbj2PpMFk90b6f8Di1FxPdh+lsyNibVG1mbWXQz8nki4ALiQbJ/xkxaI9gO0R4SOSHk7SaGB4ml0REY8WWY9ZRzj0c5Lu5z0E+AEwtWLRu8DSdNWgmVm3cugXSNKZfqi2meXJoV8gDyczs7x59E6xPJzMzHLl0C/W14ouwMzKxd07BZD0WaCOiovjIuL2wgoys9LwFbk5k3QH2X1blgDbUnMADn0z63Y+0s+ZpJXAsOYu6zcz627u08/fMuCAoosws3Jy905OJN1P1o2zB9m9XJ7lw4/cO6uo2sysPBz6+fF9182scO7Tz5mkayLim221mZl1B/fp5+8LzbSdkXsVZlZK7t7JiaS/BP4KOETS0opFe5A9rNnMrNu5eycnkvYC9qGZu2xGxFvFVGVmZePQL0B6tuogPnxF7svFVWRmZeHunZxJugyYAbxO9oB0yIZyHllUTWZWHj7Sz5mkVcBxEbG+6FrMrHw8eid/rwAbii7CzMrJ3Tv5Ww08IelBPnxF7vXFlWRmZeHQz9/L6bVrepmZ5cZ9+gWRNBAgIjYWXYuZlYf79HMm6QhJzwHLgeWSFksaXnRdZlYODv38zQKuiIiDI+Jg4ErgnwuuycxKwqGfv90j4vGmmYh4Ati9uHLMrEx8Ijd/qyV9G7gjzX+FbESPmVm385F+/i4FaoB706smtZmZdTuP3jEzKxF37+RE0vzWlvtxiWaWB4d+fk4guwXDXcAzgIotx8zKyN07OUm3U/4CcAHZHTUfBO6KiOWFFmZmpeITuTmJiG0R8XBETACOB1aR3YPnsoJLM7MScfdOjiT1B75IdrRfB9wI3FdkTWZWLu7eyYmk24EjgIeAuRGxrOCSzKyEHHuL0DMAAAJISURBVPo5kbQdeC/NVv6PLiAiYs/8qzKzsnHom5mViE/kmpmViEPfzKxEHPpWGpIOkDRX0u/ScwweknRYB95niqSPdVFNdZIu7Ir3MquGQ99KQZLIhsc+ERGHRsSxwDRgUAfebgrQbOini/Daow5w6FtuHPpWFicDWyLilqaGiHgeeErSDyUtk/SCpPMAJI2S9ISkn0l6UdKdykwGPgE8LunxtO5GSddJeh44QdLfSVqU3nNW+sFB0iclPSLpeUm/kXQocDVwkqQlkv465/9NrIR8cZaVxRHA4mbavwSMAI4C9gcWSfpFWnY0MBz4v8AvgRMj4kZJVwAnR8Sbab3dgWci4koASSsi4rtp+g7gTOB+4E7g6oi4T9IAsoOuqcDfRMSZXf6JzZrhI30ru8+R3QNpW0S8DvwH8Jm07NmIaIyI7cASsq6Y5mwD7qmYP1nSM5JeAEYDwyXtAQyOiPsAImJTRLzfDZ/HrFUOfSuL5cCx7dxmc8X0Nlr+l/GmiNgGkI7gfwKcExF/Svb84wHt3K9Zt3HoW1k8BvSXNLGpQdKRwDvAeZL6SKoB/gx4to33ehfYo4VlTQH/pqSBwDkAEfEu0ChpXNp3/zQCqLX3MutyDn0rhcguPT8b+Hwasrkc+AHwU2Ap8DzZD8PfRsRrbbzdLODhphO5O+3nHbKj+2XAAmBRxeLxwGRJS4FfAQekfW9LJ3d9Ite6nW/DYGZWIj7SNzMrEYe+mVmJOPTNzErEoW9mViIOfTOzEnHom5mViEPfzKxE/j93uI1Ec9S1ewAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can extract the following conclusions by analyzing customer account attributes:\n",
        "\n",
        "Customers with month-to-month contracts have higher churn rates compared to clients with yearly contracts.\n",
        "\n",
        "Customers who opted for an electronic check as paying method are more likely to leave the company.\n",
        "\n",
        "Customers subscribed to paperless billing churn more than those who are not subscribed."
      ],
      "metadata": {
        "id": "potDFGVJk-dR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Customer Account Information — Numerical variables**"
      ],
      "metadata": {
        "id": "ls-X57pKqlr9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The following plots show the distribution of tenure, MontlyCharges, TotalCharges by Churn"
      ],
      "metadata": {
        "id": "AWggYns3830K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(data.tenure,data.Churn).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "6x22iX-z9ROQ",
        "outputId": "720b8318-2073-4ac6-c6cc-b6f29108c7c5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0d8664290>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Churn']=='No']['tenure'].plot(kind = 'hist',alpha=0.5, color = 'yellow',label='No')\n",
        "data[data['Churn']=='Yes']['tenure'].plot(kind = 'hist',alpha=0.5,color='green', label = 'Yes')\n",
        "plt.legend(title = 'Churn')\n",
        "plt.title('Tenure Churn');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "IYaSxSqA_BDA",
        "outputId": "8b0d5abf-fa02-4823-d63b-2f48e490841f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEICAYAAACuxNj9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAaoUlEQVR4nO3dfZQV9Z3n8fcHGmnFBwQ66NBqo+H4lFGDBDWGGQKZqPiAyagxQWWNO2zOkghrdkbUJLSz8Zy4MdFoslE2aNQ4KopGYpwYH2djNtFAUIIQV8agNOMDEp9AEZDv/nF/TW46jXUb+t6q2/fzOqdPV/2qbtUHzpWP9XDrKiIwMzN7P/3yDmBmZsXnsjAzs0wuCzMzy+SyMDOzTC4LMzPL5LIwM7NMLguzOiCpXdKP8s5hjctlYX2OpHVlP1skvVM2PyXvfNsi6XOSFqacL0r6V0kfyzuXGUBT3gHMeltE7No5LWkl8J8j4sFa7V9SU0Rs7uFrLgBmAV8A7gc2AscDk4HH8s5n5iMLaxiS+kmaJenfJa2VNE/SkLSsTVJImirpBUmvSrqk7LU/lPT1svnxkjrK5ldKulDSEmC9pCZJR0v6v5Jel/SUpPHbyLUH8M/A9Ii4KyLWR8SmiPhJRPxj2ao7SbpJ0luSnpY0pmwbIemD3eXtzJryvQTckE5rzdvW9sy6cllYI/kScCrwt8BfAa8B3+uyzseAA4GJwNckHdyD7X8WOBEYDAwHfgp8HRgC/HdgvqSWbl53DNAM3J2x/VOA29L2FwDf7UG2vVKO/YBpvbA9azAuC2skXwAuiYiOiHgXaAdOk1R+OvbSiHgnIp4CngIO78H2r46IVRHxDnAWcF9E3BcRWyLiAWAhMKmb1w0FXq3g1NBjaXvvATf3MNsWYHZEvJvy7ej2rMH4moU1kv2AuyVtKRt7j9JRQKeXyqbfBnalcqu67Ot0SSeXjQ0AHunmdWuBYRVcS+iarbkH1x/WRMSGXtyeNRgfWVgjWQWcEBGDy36aI2J1Ba9dD+xSNr9XN+uUP8J5FXBzl30NiohvdPO6XwHvUjpFtr3ezsjnx0vbDnFZWCO5FrhM0n4AklokTa7wtU8CkyQNkbQXMDNj/R8BJ0s6TlJ/Sc3pQnNr1xUj4g3ga8D3JJ0qaRdJAySdIOl/9iDf59K+jqd0Xcas17gsrJF8h9KF3J9Legv4NXBUha+9mdI1jJXAz4Hb32/liFhF6bbXi4E1lI40/pFt/DcXEd8CLgC+Urb+F4EfV5hvBnAy8DowpQevM6uI/OVHZmaWxUcWZmaWyWVhZmaZXBZmZpbJZWFmZpn65Ifyhg0bFm1tbXnHMDOrK4sWLXo1Irp7JE3fLIu2tjYWLlyYdwwzs7oi6fltLfNpKDMzy+SyMDOzTC4LMzPL1CevWZhtj02bNtHR0cGGDV0fzlp/mpubaW1tZcCAAXlHsT7CZWGWdHR0sNtuu9HW1oakvONst4hg7dq1dHR0MHLkyLzjWB/h01BmyYYNGxg6dGhdFwWAJIYOHdonjpCsOFwWZmXqvSg69ZU/hxWHy8LMzDK5LMy200svvcSZZ57JAQccwJFHHsmkSZOYM2cOJ510Ut7RzHqdL3CbbYeI4FOf+hRTp07ltttuA+Cpp55iwYIFO7TdzZs309Tk/yzrX3uf27ePLMy2wyOPPMKAAQP4whe+sHXs8MMPZ9y4caxbt47TTjuNgw46iClTptD5BWNtbW28+uqrACxcuJDx48cD0N7eztlnn82xxx7L2WefTXt7O5///OcZP348+++/P1dffXXN/3xmXbkszLbD0qVLOfLII7tdtnjxYq666iqWLVvGc889xy9/+cvM7S1btowHH3yQW2+9FYDf//733H///TzxxBNceumlbNq0qVfzm/WUy8Ksl40dO5bW1lb69evHEUccwcqVKzNfc8opp7DzzjtvnT/xxBMZOHAgw4YN4wMf+AAvv/xyFRObZataWUi6XtIrkpaWjQ2R9ICkZ9PvPdO4JF0taYWkJZJGl71malr/WUlTq5XXrCcOPfRQFi1a1O2ygQMHbp3u378/mzdvBqCpqYktW7YA/MVnIAYNGlTRNszyUs0jix8Cx3cZmwU8FBGjgIfSPMAJwKj0Mw34PpTKBZgNHAWMBWZ3FoxZniZMmMC7777LnDlzto4tWbKEX/ziF9t8TVtb29aCmT9/ftUzmvWmqpVFRPwf4I9dhicDN6bpG4FTy8ZvipJfA4Ml7Q0cBzwQEX+MiNeAB/jLAjKrOUncfffdPPjggxxwwAEceuihXHTRRey1117bfM3s2bOZMWMGY8aMoX///jVMa7bj1HmnRlU2LrUB90bEh9L86xExOE0LeC0iBku6F/hGRDyWlj0EXAiMB5oj4utp/KvAOxFxRTf7mkbpqIR99933yOef3+Z3eJh1a/ny5Rx88MF5x+g1fe3PU1/a63LfkhZFxJjuluV2gTtKLdVrTRURcyJiTESMaWnp9lsBzcxsO9W6LF5Op5dIv19J46uBfcrWa01j2xo3M7MaqnVZLAA672iaCtxTNn5OuivqaOCNiHgRuB/4pKQ904XtT6YxMzOroao9V0DSrZSuOQyT1EHprqZvAPMknQc8D5yRVr8PmASsAN4GzgWIiD9K+h/Ab9J6/xwRXS+am5lZlVWtLCLis9tYNLGbdQOYvo3tXA9c34vRzMysh/wJbjMzy+THW5ptU3vNtyeJCy64gG9961sAXHHFFaxbt4729t7OYtYzPrIwK5CBAwdy1113bX06rVlRuCzMCqSpqYlp06Zx5ZVX/sWylStXMmHCBA477DAmTpzICy+8kENCa1QuC7OCmT59OrfccgtvvPHGn41/6UtfYurUqSxZsoQpU6Zw/vnn55TQGpHLwqxgdt99d84555y/+NKjX/3qV3zuc58D4Oyzz+axxx7LI541KJeFWQHNnDmTuXPnsn79+ryjmAEuC7NCGjJkCGeccQZz587dOvbRj3506/d933LLLYwbNy6veNaAfOus2Ta157r3L3/5y3z3u9/dOn/NNddw7rnn8s1vfpOWlhZuuOGGHNNZo3FZmBXIunXrtk4PHz6ct99+e+v8fvvtx8MPP5xHLDOfhjIzs2wuCzMzy+SyMDOzTC4LMzPL5LIwM7NMLgszM8vkW2fNtqH90fbe3d74999eRDBu3DguueQSTjjhBADuuOMO5s6dy89+9rNezWLWUy4Ls4KQxLXXXsvpp5/Oxz/+cTZv3szFF1/sorBCcFmYFciHPvQhTj75ZC6//HLWr1/PWWedxWWXXcbSpUvZtGkT7e3tTJ48maeffppzzz2XjRs3smXLFubPn8+oUaPyjm99mMuiG719+qHi/WacprDGMHv2bEaPHs1OO+3ESSedxIQJE7j++ut5/fXXGTt2LJ/4xCe49tprmTFjBlOmTGHjxo289957ece2Ps5lYVYwgwYN4jOf+Qy77ror8+bN4yc/+QlXXHEFABs2bOCFF17gmGOO4bLLLqOjo4NPf/rTPqqwqnNZmBVQv3796NevHxHB/PnzOfDAA/9s+cEHH8xRRx3FT3/6UyZNmsR1113HhAkTckprjcC3zpoV2HHHHcc111xDRACwePFiAJ577jn2339/zj//fCZPnsySJUvyjGkNwEcWZttQhGtIX/3qV5k5cyaHHXYYW7ZsYeTIkdx7773MmzePm2++mQEDBrDXXntx8cUX5x3V+jiXhVkBtbe3b52+7rrr/mL5rFmzmDVrVg0TWaNzWXTr0bwDmJkViq9ZmJlZJpeFWZnOC8n1rq/8Oaw4XBZmSXNzM2vXrq37f2gjgrVr19Lc3Jx3FOtDfM3CLGltbaWjo4M1a9bkHWWHNTc309ramncM60NcFmbJgAEDGDlyZN4xzAopl9NQkv6bpKclLZV0q6RmSSMlPS5phaTbJe2U1h2Y5lek5W15ZDYza2Q1LwtJI4DzgTER8SGgP3AmcDlwZUR8EHgNOC+95DzgtTR+ZVrPzMxqKK8L3E3AzpKagF2AF4EJwJ1p+Y3AqWl6cponLZ8oSTXMambW8GpeFhGxGrgCeIFSSbwBLAJej4jNabUOYESaHgGsSq/dnNYf2nW7kqZJWihpYV+4QGlmViR5nIbak9LRwkjgr4BBwPE7ut2ImBMRYyJiTEtLy45uzszMyuRxGuoTwB8iYk1EbALuAo4FBqfTUgCtwOo0vRrYByAt3wNYW9vIZmaNLY+yeAE4WtIu6drDRGAZ8AhwWlpnKnBPml6Q5knLH456/9SUmVmdyeOaxeOULlT/FvhdyjAHuBC4QNIKStck5qaXzAWGpvELAD9q08ysxnL5UF5EzAZmdxl+DhjbzbobgNNrkcvMzLrnZ0OZmVkml4WZmWVyWZiZWSaXhZmZZXJZmJlZJpeFmZllclmYmVkml4WZmWVyWZiZWSaXhZmZZXJZmJlZJpeFmZllclmYmVkml4WZmWVyWZiZWSaXhZmZZXJZmJlZJpeFmZllclmYmVkml4WZmWVyWZiZWSaXhZmZZaqoLCT9dbWDmJlZcVV6ZPG/JD0h6b9K2qOqiczMrHAqKouIGAdMAfYBFkn6F0l/V9VkZmZWGBVfs4iIZ4GvABcCfwtcLen3kj5drXBmZlYMlV6zOEzSlcByYAJwckQcnKavrGI+MzMrgKYK17sG+AFwcUS80zkYEf8h6StVSWZmZoVRaVmcCLwTEe8BSOoHNEfE2xFxc9XSmZlZIVR6zeJBYOey+V3SmJmZNYBKy6I5ItZ1zqTpXaoTyczMiqbSslgvaXTnjKQjgXfeZ/33JWmwpDvT3VTLJR0jaYikByQ9m37vmdaVpKslrZC0pDyHmZnVRqVlMRO4Q9IvJD0G3A58cQf2+x3gZxFxEHA4pbusZgEPRcQo4KE0D3ACMCr9TAO+vwP7NTOz7VDRBe6I+I2kg4AD09AzEbFpe3aYPgH+N8B/StveCGyUNBkYn1a7EXiU0mc6JgM3RUQAv05HJXtHxIvbs38zM+u5Su+GAvgI0JZeM1oSEXHTduxzJLAGuEHS4cAiYAYwvKwAXgKGp+kRwKqy13eksT8rC0nTKB15sO+++25HLDMz25ZKP5R3M3AF8DFKpfERYMx27rMJGA18PyI+DKznT6ecAEhHEdGTjUbEnIgYExFjWlpatjOamZl1p9IjizHAIekf8R3VAXRExONp/k5KZfFy5+klSXsDr6Tlqyk9k6pTaxozM7MaqfQC91Jgr97YYUS8BKyS1Hn9YyKwDFgATE1jU4F70vQC4Jx0V9TRwBu+XmFmVluVHlkMA5ZJegJ4t3MwIk7Zzv1+CbhF0k7Ac8C5lIprnqTzgOeBM9K69wGTgBXA22ldMzOroUrLor03dxoRT9L9NY+J3awbwPTe3L+ZmfVMpbfO/puk/YBREfGgpF2A/tWNZmZmRVHp3VD/QOlC9HVpaATw42qFMjOzYqn0Avd04FjgTdj6RUgfqFYoMzMrlkrL4t30SWsAJDXRw89BmJlZ/aq0LP5N0sXAzum7t+8AflK9WGZmViSVlsUsSo/o+B3wXyjdzupvyDMzaxCV3g21Bfjf6cfMzBpMRWUh6Q90c40iIvbv9URmZlY4PXk2VKdm4HRgSO/HMTOzIqromkVErC37WR0RVwEnVjmbmZkVRKWnocq/yrQfpSONnnwXhpmZ1bFK/8H/Vtn0ZmAlf3rQn5mZ9XGV3g318WoHMTOz4qr0NNQF77c8Ir7dO3HMzKyIenI31EcofRERwMnAE8Cz1QhlZmbFUmlZtAKjI+ItAEntwE8j4qxqBTMzs+Ko9HEfw4GNZfMb05iZmTWASo8sbgKekHR3mj8VuLE6kczMrGgqvRvqMkn/CoxLQ+dGxOLqxTIzsyLpyQfrdgHejIgbJLVIGhkRf6hWsMbU3mD7NbN6UenXqs4GLgQuSkMDgB9VK5SZmRVLpRe4PwWcAqwHiIj/AHarVigzMyuWSstiY0QE6THlkgZVL5KZmRVNpdcs5km6Dhgs6R+Az+MvQjKzwmvPO0CfkVkWkgTcDhwEvAkcCHwtIh6ocjYzMyuIzLKIiJB0X0T8NeCCMDNrQJVes/itpI9UNYmZmRVWpdcsjgLOkrSS0h1RonTQcVi1gpmZWXG8b1lI2jciXgCOq1EeMzMroKwjix9Tetrs85LmR8Tf1yKUNZL2Bttvo2rPO4DtoKxrFiqb3r+aQczMrLiyyiK2Mb3DJPWXtFjSvWl+pKTHJa2QdLukndL4wDS/Ii1v680cZmaWLassDpf0pqS3gMPS9JuS3pL05g7uewawvGz+cuDKiPgg8BpwXho/D3gtjV+Z1jMzsxp637KIiP4RsXtE7BYRTWm6c3737d2ppFbgROAHaV7ABODOtMqNlL4zA2Ayf/rujDuBiWl9MzOrkZ48orw3XQX8E396GOFQ4PWI2JzmO4ARaXoEsAogIjZLeiOt/2r5BiVNA6YB7LvvvlUN3/e05x3AzAqu0g/l9RpJJwGvRMSi3txuRMyJiDERMaalpaU3N21m1vDyOLI4FjhF0iSgGdgd+A6lhxQ2paOLVmB1Wn81sA/QIakJ2ANYW/vYZmaNq+ZHFhFxUUS0RkQbcCbwcERMAR4BTkurTQXuSdML0jxp+cPpcelmZlYjeV2z6M6FwG2Svg4sBuam8bnAzZJWAH+kVDBm1mPteQewOpZrWUTEo8Cjafo5YGw362wATq9pMDMz+zNFOrIwq6H2Bt232fap+TULMzOrPy4LMzPL5LIwM7NMLgszM8vkC9xmNdeedwCzHvORhZmZZXJZmJlZJpeFmZllclmYmVkml4WZmWVyWZiZWSaXhZmZZXJZmJlZJpeFmZllclmYmVkml4WZmWVyWZiZWSaXhZmZZXJZmJlZJpeFmZllclmYmVkmf/lRgbQ/+mg++x0/Ppf9mln98JGFmZllclmYmVkml4WZmWVyWZiZWSaXhZmZZXJZmJlZJpeFmZllclmYmVmmmpeFpH0kPSJpmaSnJc1I40MkPSDp2fR7zzQuSVdLWiFpiaTRtc5sZtbo8jiy2Ax8OSIOAY4Gpks6BJgFPBQRo4CH0jzACcCo9DMN+H7tI5uZNbaal0VEvBgRv03TbwHLgRHAZODGtNqNwKlpejJwU5T8Ghgsae8axzYza2i5XrOQ1AZ8GHgcGB4RL6ZFLwHD0/QIYFXZyzrSWNdtTZO0UNLCNWvWVC2zmVkjyq0sJO0KzAdmRsSb5csiIoDoyfYiYk5EjImIMS0tLb2Y1MzMcikLSQMoFcUtEXFXGn658/RS+v1KGl8N7FP28tY0ZmZmNZLH3VAC5gLLI+LbZYsWAFPT9FTgnrLxc9JdUUcDb5SdrjIzsxrI4/ssjgXOBn4n6ck0djHwDWCepPOA54Ez0rL7gEnACuBt4NzaxjUzs5qXRUQ8Bmgbiyd2s34A06sayszM3pc/wW1mZplcFmZmlsnfwW25ffc3+Pu/zeqFjyzMzCyTy8LMzDK5LMzMLJPLwszMMrkszMwsk8vCzMwyuSzMzCyTy8LMzDL5Q3mWq7w+EOgPA5r1jI8szMwsk48srCH5ESdmPeOyMKsxn3qzeuTTUGZmlsllYWZmmVwWZmaWyWVhZmaZfIHbrEH4DrDayffvujrbdVmYWdX5DrD659NQZmaWyWVhZmaZXBZmZpbJZWFmZpl8gdvM+qw870rqa3xkYWZmmVwWZmaWyWVhZmaZXBZmZpbJZWFmZpnqpiwkHS/pGUkrJM3KO4+ZWSOpi7KQ1B/4HnACcAjwWUmH5JvKzKxx1EVZAGOBFRHxXERsBG4DJuecycysYdTLh/JGAKvK5juAo8pXkDQNmJZm10l6psJtDwNe3eGEteGs1eGs1VEvWeslJ1SQ9VK0I9vfb1sL6qUsMkXEHGBOT18naWFEjKlCpF7nrNXhrNVRL1nrJSfkm7VeTkOtBvYpm29NY2ZmVgP1Uha/AUZJGilpJ+BMYEHOmczMGkZdnIaKiM2SvgjcD/QHro+Ip3tp8z0+dZUjZ60OZ62OeslaLzkhx6yKiLz2bWZmdaJeTkOZmVmOXBZmZpapocuiyI8QkXS9pFckLS0bGyLpAUnPpt975pkxZdpH0iOSlkl6WtKMAmdtlvSEpKdS1kvT+EhJj6f3we3pJopCkNRf0mJJ96b5QmaVtFLS7yQ9KWlhGivcewBA0mBJd0r6vaTlko4pYlZJB6a/z86fNyXNzCtrw5ZFHTxC5IfA8V3GZgEPRcQo4KE0n7fNwJcj4hDgaGB6+nssYtZ3gQkRcThwBHC8pKOBy4ErI+KDwGvAeTlm7GoGsLxsvshZPx4RR5R9DqCI7wGA7wA/i4iDgMMp/f0WLmtEPJP+Po8AjgTeBu4mr6wR0ZA/wDHA/WXzFwEX5Z2rS8Y2YGnZ/DPA3ml6b+CZvDN2k/ke4O+KnhXYBfgtpScBvAo0dfe+yDljK6V/DCYA9wIqcNaVwLAuY4V7DwB7AH8g3dxT5Kxd8n0S+GWeWRv2yILuHyEyIqcslRoeES+m6ZeA4XmG6UpSG/Bh4HEKmjWd1nkSeAV4APh34PWI2JxWKdL74Crgn4AtaX4oxc0awM8lLUqP3oFivgdGAmuAG9LpvR9IGkQxs5Y7E7g1TeeStZHLoq5F6X8rCnPfs6RdgfnAzIh4s3xZkbJGxHtROqxvpfSAyoNyjtQtSScBr0TEoryzVOhjETGa0mnd6ZL+pnxhgd4DTcBo4PsR8WFgPV1O4xQoKwDputQpwB1dl9UyayOXRT0+QuRlSXsDpN+v5JwHAEkDKBXFLRFxVxouZNZOEfE68AilUzmDJXV+QLUo74NjgVMkraT0lOUJlM61FzErEbE6/X6F0nn1sRTzPdABdETE42n+TkrlUcSsnU4AfhsRL6f5XLI2clnU4yNEFgBT0/RUStcHciVJwFxgeUR8u2xREbO2SBqcpnemdG1lOaXSOC2tVoisEXFRRLRGRBul9+bDETGFAmaVNEjSbp3TlM6vL6WA74GIeAlYJenANDQRWEYBs5b5LH86BQV5Zc37wk3OF40mAf+P0nnrS/LO0yXbrcCLwCZK/zd0HqVz1g8BzwIPAkMKkPNjlA6DlwBPpp9JBc16GLA4ZV0KfC2N7w88AaygdKg/MO+sXXKPB+4tataU6an083Tnf0tFfA+kXEcAC9P74MfAngXOOghYC+xRNpZLVj/uw8zMMjXyaSgzM6uQy8LMzDK5LMzMLJPLwszMMrkszMwsk8vCzMwyuSzMzCzT/wdhVO2ecNfOKwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Churn']=='No']['MonthlyCharges'].plot(kind = 'hist',alpha=0.5, color = 'blue',label='No')\n",
        "data[data['Churn']=='Yes']['MonthlyCharges'].plot(kind = 'hist',alpha=0.5,color='green', label = 'Yes')\n",
        "plt.legend(title = 'Churn')\n",
        "plt.title('MonthlyCharges churn');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "1ZtG_qLJAYBA",
        "outputId": "aacb5f62-da85-4e1a-c30e-6bee5faa9194"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data['Churn']=='No']['TotalCharges'].plot(kind = 'hist',alpha=0.5, color = 'blue',label='No')\n",
        "data[data['Churn']=='Yes']['TotalCharges'].plot(kind = 'hist',alpha=0.5,color='green', label = 'Yes')\n",
        "plt.legend(title = 'Churn')\n",
        "plt.title('Total Charges churn');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "NRFiYWn9D7QB",
        "outputId": "fac25045-424f-43a1-aeb8-b36769895ace"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can extract the following conclusions by analyzing the histograms above:\n",
        "\n",
        "The **churn rate** tends to be larger when **monthly charges are high.**\n",
        "\n",
        "**New customers (low tenure)** are more likely to churn.\n",
        "\n",
        "Clients with **high total charges** are less likely to leave the company.\n"
      ],
      "metadata": {
        "id": "xwgV1qrzEc2R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Services Information**"
      ],
      "metadata": {
        "id": "k0QgfpEwFMxA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**services_columns** = ['PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',\n",
        "                   'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']"
      ],
      "metadata": {
        "id": "TSMyihv_FZdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "services_columns = ['PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',\n",
        "                   'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n",
        "\n",
        "for x in services_columns:\n",
        "  pd.crosstab(data[x],data.Churn).plot(kind='bar',stacked=True)\n",
        "  plt.xticks(rotation= 360)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "27lXyId-EIwc",
        "outputId": "a2cccf1d-7db0-4ce9-d6f0-17c449b44575"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can extract the following conclusions by evaluating **services attributes**:\n",
        "\n",
        "We do not expect phone attributes (PhoneService and MultipleLines) to have significant predictive power. The percentage of churn for all classes in both independent variables is nearly the same.\n",
        "Clients with online security churn less than those without it.\n",
        "Customers with no tech support tend to churn more often than those with tech support.\n",
        "By looking at the plots above, we can identify the most relevant attributes for detecting churn. We expect these attributes to be discriminative in our future models.\n",
        "\n"
      ],
      "metadata": {
        "id": "CMf41e1bHg_n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mutual_info_score"
      ],
      "metadata": {
        "id": "JTAaVOGVGkfa"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Mutual information** measures the mutual dependency between two variables based on entropy estimations.\n",
        "\n",
        " In machine learning, we are interested in evaluating the degree of dependency between each independent variable and the response variable. Higher values of mutual information show a higher degree of dependency which indicates that the independent variable will be useful for predicting the target."
      ],
      "metadata": {
        "id": "eoiOEAoEexOr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **mutual information** extends the notion of **correlation to nonlinear relationships** since, unlike Pearson’s correlation coefficient, this method is able to detect not only linear relationships but also nonlinear ones."
      ],
      "metadata": {
        "id": "VQnZ70eIf2Cl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mutual_info_score(data.tenure, data.Churn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTozJswpTevl",
        "outputId": "0f617e1b-990e-45cc-a611-a5fe6f3456b7"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07895028043866212"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_variables = data.select_dtypes(include=['object','number']).drop('Churn', axis=1)\n",
        "# data.drop('Churn',axis=1).apply(mutual_info_sc).sort_values(ascending= False)\n",
        "def compute_mutual_information(cat):\n",
        "  return mutual_info_score(cat,data.Churn)"
      ],
      "metadata": {
        "id": "Z-mvCscEVkbX"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance = categorical_variables.apply(compute_mutual_information).sort_values(ascending=False)\n",
        "feature_importance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9psYqUc-X5Te",
        "outputId": "c8489222-073c-4649-e525-15927cc27118"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/cluster/_supervised.py:65: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/cluster/_supervised.py:65: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TotalCharges        0.537530\n",
              "MonthlyCharges      0.173835\n",
              "Contract            0.098182\n",
              "tenure              0.078950\n",
              "OnlineSecurity      0.064528\n",
              "TechSupport         0.062873\n",
              "InternetService     0.055394\n",
              "OnlineBackup        0.046659\n",
              "PaymentMethod       0.044423\n",
              "DeviceProtection    0.043784\n",
              "StreamingMovies     0.031918\n",
              "StreamingTV         0.031803\n",
              "PaperlessBilling    0.019119\n",
              "Dependents          0.014270\n",
              "Partner             0.011383\n",
              "SeniorCitizen       0.010533\n",
              "MultipleLines       0.000798\n",
              "PhoneService        0.000069\n",
              "gender              0.000037\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance.plot(kind='barh',figsize=(10,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "QnhVnljwX6T5",
        "outputId": "58f7fb61-cf67-41dd-eaa2-8ea13ddc983b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0d7c10f50>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As shown above, **gender, PhoneService, and MultipleLines** have a mutual information score really close to 0, meaning those variables do not have a strong relationship with the target. This information is in line with the conclusions we have previously drawn by visualizing the data. In the following steps, we should consider removing those variables from the data set before training as they do not provide useful information for predicting the outcome."
      ],
      "metadata": {
        "id": "rlQYA3wpfK2S"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KO6qmsx4qiBr"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Feature Engineering**"
      ],
      "metadata": {
        "id": "3xFPdnTnf_1f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature engineering** is the process of extracting features from the data and transforming them into a format that is suitable for the machine learning model."
      ],
      "metadata": {
        "id": "ll5PzFFIikf4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this project, we need to transform both **numerical** and **categorical** variables. Most machine learning algorithms require numerical values; therefore, all categorical attributes available in the dataset should be encoded into numerical labels before training the model. In addition, we need to transform numeric columns into a common scale. This will prevent that the columns with large values dominate the learning process."
      ],
      "metadata": {
        "id": "_63tTGGbjy4l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_transformed_full = pd.get_dummies(data.drop('Churn',axis=1))\n",
        "data_transformed_full.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "ps2M0puEckHc",
        "outputId": "c6a69a9e-4ef1-4049-f08f-690b3773b71f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges  gender_Female  \\\n",
              "0              0       1           29.85         29.85              1   \n",
              "1              0      34           56.95       1889.50              0   \n",
              "2              0       2           53.85        108.15              0   \n",
              "3              0      45           42.30       1840.75              0   \n",
              "4              0       2           70.70        151.65              1   \n",
              "\n",
              "   gender_Male  Partner_No  Partner_Yes  Dependents_No  Dependents_Yes  ...  \\\n",
              "0            0           0            1              1               0  ...   \n",
              "1            1           1            0              1               0  ...   \n",
              "2            1           1            0              1               0  ...   \n",
              "3            1           1            0              1               0  ...   \n",
              "4            0           1            0              1               0  ...   \n",
              "\n",
              "   StreamingMovies_Yes  Contract_Month-to-month  Contract_One year  \\\n",
              "0                    0                        1                  0   \n",
              "1                    0                        0                  1   \n",
              "2                    0                        1                  0   \n",
              "3                    0                        0                  1   \n",
              "4                    0                        1                  0   \n",
              "\n",
              "   Contract_Two year  PaperlessBilling_No  PaperlessBilling_Yes  \\\n",
              "0                  0                    0                     1   \n",
              "1                  0                    1                     0   \n",
              "2                  0                    0                     1   \n",
              "3                  0                    1                     0   \n",
              "4                  0                    0                     1   \n",
              "\n",
              "   PaymentMethod_Bank transfer  PaymentMethod_Credit card  \\\n",
              "0                            0                          0   \n",
              "1                            0                          0   \n",
              "2                            0                          0   \n",
              "3                            1                          0   \n",
              "4                            0                          0   \n",
              "\n",
              "   PaymentMethod_Electronic check  PaymentMethod_Mailed check  \n",
              "0                               1                           0  \n",
              "1                               0                           1  \n",
              "2                               0                           1  \n",
              "3                               0                           0  \n",
              "4                               1                           0  \n",
              "\n",
              "[5 rows x 45 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34c9824e-3bd4-408f-a985-03fde8346281\">\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>TotalCharges</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>...</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</th>\n",
              "      <th>PaymentMethod_Credit card</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</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>...</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.50</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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</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>...</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</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>...</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>1</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>151.65</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>...</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34c9824e-3bd4-408f-a985-03fde8346281')\"\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-34c9824e-3bd4-408f-a985-03fde8346281 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-34c9824e-3bd4-408f-a985-03fde8346281');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "One-hot encoding creates a new binary column for each level of the categorical variable. The new column contains zeros and ones indicating the absence or presence of the category in the data"
      ],
      "metadata": {
        "id": "YmHMLL3-mkfG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Normalization**\n",
        "\n",
        "Data Normalization is a common practice in machine learning which consists of transforming numeric columns to a common scale.\n",
        "\n",
        "After normalization, all variables have a similar influence on the model, improving the stability and performance of the learning algorithm.\n",
        "\n",
        "In this project, we will use the **min-max method** to rescale the numeric columns (**tenure, MontlyCharges, and TotalCharges**) to a common scale. The min-max approach (often called **normalization**) rescales the feature to a fixed range of [0,1] by subtracting the minimum value of the feature and then dividing by the range."
      ],
      "metadata": {
        "id": "k2kP-2KOmx4j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "02nJmZUflg_0"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# data.tenure.plot(kind='kde')\n",
        "# data.MonthlyCharges.plot(kind='kde')\n",
        "# data.TotalCharges.plot(kind='kde')\n",
        "# These variables does not show gussian dist so we use normalization tech"
      ],
      "metadata": {
        "id": "MJt40Jvknb4I"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_max_columns = ['tenure',\t'MonthlyCharges',\t'TotalCharges']\n",
        "data_transformed_full[min_max_columns] = MinMaxScaler().fit_transform(data_transformed_full[min_max_columns])\n",
        "data_transformed_full.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "g8r1Rpe9njzq",
        "outputId": "621e8716-2093-4dfc-d707-56c493f2c5a6"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SeniorCitizen    tenure  MonthlyCharges  TotalCharges  gender_Female  \\\n",
              "0              0  0.000000        0.115423      0.001275              1   \n",
              "1              0  0.464789        0.385075      0.215867              0   \n",
              "2              0  0.014085        0.354229      0.010310              0   \n",
              "3              0  0.619718        0.239303      0.210241              0   \n",
              "4              0  0.014085        0.521891      0.015330              1   \n",
              "\n",
              "   gender_Male  Partner_No  Partner_Yes  Dependents_No  Dependents_Yes  ...  \\\n",
              "0            0           0            1              1               0  ...   \n",
              "1            1           1            0              1               0  ...   \n",
              "2            1           1            0              1               0  ...   \n",
              "3            1           1            0              1               0  ...   \n",
              "4            0           1            0              1               0  ...   \n",
              "\n",
              "   StreamingMovies_Yes  Contract_Month-to-month  Contract_One year  \\\n",
              "0                    0                        1                  0   \n",
              "1                    0                        0                  1   \n",
              "2                    0                        1                  0   \n",
              "3                    0                        0                  1   \n",
              "4                    0                        1                  0   \n",
              "\n",
              "   Contract_Two year  PaperlessBilling_No  PaperlessBilling_Yes  \\\n",
              "0                  0                    0                     1   \n",
              "1                  0                    1                     0   \n",
              "2                  0                    0                     1   \n",
              "3                  0                    1                     0   \n",
              "4                  0                    0                     1   \n",
              "\n",
              "   PaymentMethod_Bank transfer  PaymentMethod_Credit card  \\\n",
              "0                            0                          0   \n",
              "1                            0                          0   \n",
              "2                            0                          0   \n",
              "3                            1                          0   \n",
              "4                            0                          0   \n",
              "\n",
              "   PaymentMethod_Electronic check  PaymentMethod_Mailed check  \n",
              "0                               1                           0  \n",
              "1                               0                           1  \n",
              "2                               0                           1  \n",
              "3                               0                           0  \n",
              "4                               1                           0  \n",
              "\n",
              "[5 rows x 45 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-943cee0e-9d02-408b-930e-f82b81de00f8\">\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>TotalCharges</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>...</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</th>\n",
              "      <th>PaymentMethod_Credit card</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.115423</td>\n",
              "      <td>0.001275</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>...</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.464789</td>\n",
              "      <td>0.385075</td>\n",
              "      <td>0.215867</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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.014085</td>\n",
              "      <td>0.354229</td>\n",
              "      <td>0.010310</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>...</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.619718</td>\n",
              "      <td>0.239303</td>\n",
              "      <td>0.210241</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>...</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>1</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>0.014085</td>\n",
              "      <td>0.521891</td>\n",
              "      <td>0.015330</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>...</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-943cee0e-9d02-408b-930e-f82b81de00f8')\"\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-943cee0e-9d02-408b-930e-f82b81de00f8 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-943cee0e-9d02-408b-930e-f82b81de00f8');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have all feature in a standard way to train a model but before that we also remove some features having  very low mutual info likes **gender**, **PhoneService**, and **MultipleLines** "
      ],
      "metadata": {
        "id": "9WDU-8POqxpS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_remvd = data.drop(columns = ['gender', 'PhoneService', 'MultipleLines'])\n"
      ],
      "metadata": {
        "id": "jRZkLRZ5pt-Q"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#encode and normalize the above dataset\n",
        "data_remvd_transformed = pd.get_dummies(data_remvd.drop('Churn',axis=1))\n",
        "min_max_columns = ['tenure',\t'MonthlyCharges',\t'TotalCharges']\n",
        "data_remvd_transformed[min_max_columns] = MinMaxScaler().fit_transform(data_remvd_transformed[min_max_columns])\n",
        "data_remvd_transformed.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "az5hhJHlpw0e",
        "outputId": "a6cd08e5-b0d1-45f8-8cff-2c4a393f7227"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   SeniorCitizen    tenure  MonthlyCharges  TotalCharges  Partner_No  \\\n",
              "0              0  0.000000        0.115423      0.001275           0   \n",
              "1              0  0.464789        0.385075      0.215867           1   \n",
              "2              0  0.014085        0.354229      0.010310           1   \n",
              "3              0  0.619718        0.239303      0.210241           1   \n",
              "4              0  0.014085        0.521891      0.015330           1   \n",
              "\n",
              "   Partner_Yes  Dependents_No  Dependents_Yes  InternetService_DSL  \\\n",
              "0            1              1               0                    1   \n",
              "1            0              1               0                    1   \n",
              "2            0              1               0                    1   \n",
              "3            0              1               0                    1   \n",
              "4            0              1               0                    0   \n",
              "\n",
              "   InternetService_Fiber optic  ...  StreamingMovies_Yes  \\\n",
              "0                            0  ...                    0   \n",
              "1                            0  ...                    0   \n",
              "2                            0  ...                    0   \n",
              "3                            0  ...                    0   \n",
              "4                            1  ...                    0   \n",
              "\n",
              "   Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
              "0                        1                  0                  0   \n",
              "1                        0                  1                  0   \n",
              "2                        1                  0                  0   \n",
              "3                        0                  1                  0   \n",
              "4                        1                  0                  0   \n",
              "\n",
              "   PaperlessBilling_No  PaperlessBilling_Yes  PaymentMethod_Bank transfer  \\\n",
              "0                    0                     1                            0   \n",
              "1                    1                     0                            0   \n",
              "2                    0                     1                            0   \n",
              "3                    1                     0                            1   \n",
              "4                    0                     1                            0   \n",
              "\n",
              "   PaymentMethod_Credit card  PaymentMethod_Electronic check  \\\n",
              "0                          0                               1   \n",
              "1                          0                               0   \n",
              "2                          0                               0   \n",
              "3                          0                               0   \n",
              "4                          0                               1   \n",
              "\n",
              "   PaymentMethod_Mailed check  \n",
              "0                           0  \n",
              "1                           1  \n",
              "2                           1  \n",
              "3                           0  \n",
              "4                           0  \n",
              "\n",
              "[5 rows x 38 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-272dbe75-aead-4188-bfe5-7897f6ece388\">\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>TotalCharges</th>\n",
              "      <th>Partner_No</th>\n",
              "      <th>Partner_Yes</th>\n",
              "      <th>Dependents_No</th>\n",
              "      <th>Dependents_Yes</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>...</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</th>\n",
              "      <th>PaymentMethod_Credit card</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.115423</td>\n",
              "      <td>0.001275</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>...</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.464789</td>\n",
              "      <td>0.385075</td>\n",
              "      <td>0.215867</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>...</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.014085</td>\n",
              "      <td>0.354229</td>\n",
              "      <td>0.010310</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>...</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.619718</td>\n",
              "      <td>0.239303</td>\n",
              "      <td>0.210241</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>...</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>1</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>0.014085</td>\n",
              "      <td>0.521891</td>\n",
              "      <td>0.015330</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>...</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 38 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-272dbe75-aead-4188-bfe5-7897f6ece388')\"\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-272dbe75-aead-4188-bfe5-7897f6ece388 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-272dbe75-aead-4188-bfe5-7897f6ece388');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "churn = data.Churn.map({'No':0,'Yes':1})"
      ],
      "metadata": {
        "id": "BvWfjdniFLED"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "churn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "segAqvpjGtv1",
        "outputId": "63f6771a-689a-43d6-e2ba-e5dc67c2e2c0"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0\n",
              "1       0\n",
              "2       1\n",
              "3       0\n",
              "4       1\n",
              "       ..\n",
              "7038    0\n",
              "7039    0\n",
              "7040    0\n",
              "7041    1\n",
              "7042    0\n",
              "Name: Churn, Length: 7032, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5. Splitting the data in training and testing sets**\n"
      ],
      "metadata": {
        "id": "e2cMF8YDt_Lz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first step when building a model is to split the data into two groups, which are typically referred to as training and testing sets. "
      ],
      "metadata": {
        "id": "81fuMeQAuRVx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "0YT8NyGxuHHd"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data_remvd_transformed, churn,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "jhIqXPC00D5x"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape,y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kj7DCfzv2NjI",
        "outputId": "cd828363-44cb-4a23-8d03-640ec79a2584"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5625, 38), (5625,))"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QldajSID2Sr_",
        "outputId": "08d57ee9-d1cf-494a-e480-5981d1d76283"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1407, 38)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **6. Assessing multiple algorithms**\n",
        "\n",
        "Algorithm selection is a key challenge in any machine learning project since there is not an algorithm that is the best across all projects. Generally, we need to evaluate a set of potential candidates and select for further evaluation those that provide better performance."
      ],
      "metadata": {
        "id": "UP993jmT3hKY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier,AdaBoostClassifier\n",
        "import xgboost as xgb"
      ],
      "metadata": {
        "id": "AeWKdt6E2ce6"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_models(seed=42):\n",
        "  modells = []\n",
        "  modells.append(('dummy_classifier', DummyClassifier(random_state=seed, strategy='most_frequent')))\n",
        "  modells.append(('k_nearest_neighbors', KNeighborsClassifier()))\n",
        "  modells.append(('Logestic_Regression', LogisticRegression(random_state=seed)))\n",
        "  modells.append(('Logestic_Regression', LogisticRegression(random_state=seed,class_weight='balanced')))\n",
        "  modells.append(('support_vector_machines', SVC(random_state=seed)))\n",
        "  modells.append(('random_forest', RandomForestClassifier(random_state=seed)))\n",
        "  modells.append(('gradient_boosting', GradientBoostingClassifier(random_state=seed)))\n",
        "  modells.append(('Xgb_classifier', xgb.XGBClassifier(random_state=seed)))\n",
        "  modells.append(('ExtraTree_classifier', ExtraTreesClassifier(random_state=seed)))\n",
        "  modells.append(('AdaBoost_classifier', AdaBoostClassifier(random_state=seed)))\n",
        "\n",
        "\n",
        "  return modells\n",
        "\n",
        "models = create_models(42)"
      ],
      "metadata": {
        "id": "T9esZ_rj8R0f"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JJLcK9F9WtM",
        "outputId": "af914498-9913-4f5c-d6f2-93307eb3c34c"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('dummy_classifier',\n",
              "  DummyClassifier(random_state=42, strategy='most_frequent')),\n",
              " ('k_nearest_neighbors', KNeighborsClassifier()),\n",
              " ('Logestic_Regression', LogisticRegression(random_state=42)),\n",
              " ('Logestic_Regression',\n",
              "  LogisticRegression(class_weight='balanced', random_state=42)),\n",
              " ('support_vector_machines', SVC(random_state=42)),\n",
              " ('random_forest', RandomForestClassifier(random_state=42)),\n",
              " ('gradient_boosting', GradientBoostingClassifier(random_state=42)),\n",
              " ('Xgb_classifier', XGBClassifier(random_state=42)),\n",
              " ('ExtraTree_classifier', ExtraTreesClassifier(random_state=42)),\n",
              " ('AdaBoost_classifier', AdaBoostClassifier(random_state=42))]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "results = []\n",
        "names = []\n",
        "for name , model in models:\n",
        "  model.fit(X_train,y_train)\n",
        "  prediction = model.predict(X_test)\n",
        "  accuracy = accuracy_score(y_test,prediction)\n",
        "  results.append(accuracy)\n",
        "  names.append(name)\n",
        "  print('Classifier : {} , Acuuracy : {}'.format(name,accuracy))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6L1j2lsx9YYp",
        "outputId": "0da4cabe-7921-44d8-bd27-8c10fc9f328f"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classifier : dummy_classifier , Acuuracy : 0.7341862117981521\n",
            "Classifier : k_nearest_neighbors , Acuuracy : 0.7356076759061834\n",
            "Classifier : Logestic_Regression , Acuuracy : 0.7917555081734187\n",
            "Classifier : Logestic_Regression , Acuuracy : 0.7334754797441365\n",
            "Classifier : support_vector_machines , Acuuracy : 0.775408670931059\n",
            "Classifier : random_forest , Acuuracy : 0.7775408670931059\n",
            "Classifier : gradient_boosting , Acuuracy : 0.7853589196872779\n",
            "Classifier : Xgb_classifier , Acuuracy : 0.7945984363894811\n",
            "Classifier : ExtraTree_classifier , Acuuracy : 0.7732764747690121\n",
            "Classifier : AdaBoost_classifier , Acuuracy : 0.7839374555792467\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is important to bear in mind that we have trained all the algorithms using the default hyperparameters. The accuracy of many machine learning algorithms is highly sensitive to the hyperparameters chosen for training the model."
      ],
      "metadata": {
        "id": "t00cth3dB5_Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Lets do it again by NN**"
      ],
      "metadata": {
        "id": "jAQOLE2us5rH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.models import Sequential\n"
      ],
      "metadata": {
        "id": "n60_wxaps21F"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    Dense(128,input_shape=(38,),activation='relu'),\n",
        "    Dense(256,activation='relu'),\n",
        "    Dense(256,activation='relu'),\n",
        "    Dense(32,activation='relu'),\n",
        "    Dense(38,activation='relu'),\n",
        "    Dense(1,activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer ='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "model.fit(X_train,y_train,epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-1waWvyTt80w",
        "outputId": "1e41ce36-3975-415e-93c7-6c4255dfea7c"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "176/176 [==============================] - 2s 3ms/step - loss: 0.4494 - accuracy: 0.7817\n",
            "Epoch 2/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.4241 - accuracy: 0.7973\n",
            "Epoch 3/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.4183 - accuracy: 0.8030\n",
            "Epoch 4/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.4131 - accuracy: 0.8050\n",
            "Epoch 5/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.4096 - accuracy: 0.8055\n",
            "Epoch 6/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.4058 - accuracy: 0.8096\n",
            "Epoch 7/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.4040 - accuracy: 0.8069\n",
            "Epoch 8/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.4028 - accuracy: 0.8119\n",
            "Epoch 9/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3939 - accuracy: 0.8124\n",
            "Epoch 10/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3927 - accuracy: 0.8137\n",
            "Epoch 11/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3875 - accuracy: 0.8153\n",
            "Epoch 12/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3791 - accuracy: 0.8199\n",
            "Epoch 13/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3699 - accuracy: 0.8222\n",
            "Epoch 14/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3624 - accuracy: 0.8263\n",
            "Epoch 15/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3529 - accuracy: 0.8308\n",
            "Epoch 16/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3501 - accuracy: 0.8313\n",
            "Epoch 17/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3353 - accuracy: 0.8361\n",
            "Epoch 18/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3219 - accuracy: 0.8420\n",
            "Epoch 19/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3176 - accuracy: 0.8455\n",
            "Epoch 20/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.3018 - accuracy: 0.8588\n",
            "Epoch 21/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2925 - accuracy: 0.8652\n",
            "Epoch 22/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2775 - accuracy: 0.8676\n",
            "Epoch 23/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2731 - accuracy: 0.8700\n",
            "Epoch 24/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2634 - accuracy: 0.8761\n",
            "Epoch 25/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2589 - accuracy: 0.8830\n",
            "Epoch 26/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2437 - accuracy: 0.8864\n",
            "Epoch 27/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2416 - accuracy: 0.8869\n",
            "Epoch 28/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2273 - accuracy: 0.8949\n",
            "Epoch 29/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2383 - accuracy: 0.8905\n",
            "Epoch 30/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2220 - accuracy: 0.8962\n",
            "Epoch 31/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2166 - accuracy: 0.9012\n",
            "Epoch 32/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2157 - accuracy: 0.8988\n",
            "Epoch 33/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2096 - accuracy: 0.9028\n",
            "Epoch 34/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2067 - accuracy: 0.9028\n",
            "Epoch 35/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2086 - accuracy: 0.9056\n",
            "Epoch 36/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2019 - accuracy: 0.9060\n",
            "Epoch 37/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2038 - accuracy: 0.9058\n",
            "Epoch 38/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1988 - accuracy: 0.9070\n",
            "Epoch 39/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2049 - accuracy: 0.9065\n",
            "Epoch 40/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.2022 - accuracy: 0.9044\n",
            "Epoch 41/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1949 - accuracy: 0.9068\n",
            "Epoch 42/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1830 - accuracy: 0.9124\n",
            "Epoch 43/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1820 - accuracy: 0.9163\n",
            "Epoch 44/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1778 - accuracy: 0.9143\n",
            "Epoch 45/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1841 - accuracy: 0.9161\n",
            "Epoch 46/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1781 - accuracy: 0.9157\n",
            "Epoch 47/50\n",
            "176/176 [==============================] - 1s 3ms/step - loss: 0.1755 - accuracy: 0.9172\n",
            "Epoch 48/50\n",
            "176/176 [==============================] - 1s 4ms/step - loss: 0.1839 - accuracy: 0.9150\n",
            "Epoch 49/50\n",
            "176/176 [==============================] - 1s 4ms/step - loss: 0.1902 - accuracy: 0.9106\n",
            "Epoch 50/50\n",
            "176/176 [==============================] - 1s 4ms/step - loss: 0.1763 - accuracy: 0.9156\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdff468bbd0>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9R-Hdylht8xn",
        "outputId": "80b919a9-2465-44b2-9e15-adb47a617cb8"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 0s 3ms/step - loss: 1.4808 - accuracy: 0.7420\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4807853698730469, 0.7420042753219604]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDKSkTt4t8ej",
        "outputId": "8ec489f5-9023-4fc9-8f1d-7d55be6e15e1"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjFOYJE3t8bW",
        "outputId": "1ba69cd8-2772-45c7-db60-17390fd4adeb"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.5191470e-07],\n",
              "       [3.1805915e-01],\n",
              "       [3.3553399e-02],\n",
              "       ...,\n",
              "       [8.1916788e-04],\n",
              "       [2.6253793e-11],\n",
              "       [8.5873090e-08]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_list= []\n",
        "for i in y_pred:\n",
        "  if i <0.5:\n",
        "    y_pred_list.append(0)\n",
        "  else:\n",
        "    y_pred_list.append(1)\n"
      ],
      "metadata": {
        "id": "F0e_qZEgMJgq"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "np.argmax(y_pred,axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPbwE0mhLxPn",
        "outputId": "9e8c7922-58cf-4069-8e60-43542d1d6869"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AlKl0k7Et8Ze",
        "outputId": "5cfdae5e-e625-431c-d3e9-83ab6895455c"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.84      0.83      1033\n",
            "           1       0.52      0.46      0.49       374\n",
            "\n",
            "    accuracy                           0.74      1407\n",
            "   macro avg       0.66      0.65      0.66      1407\n",
            "weighted avg       0.73      0.74      0.74      1407\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **7.Hyperparameter tuning**"
      ],
      "metadata": {
        "id": "mITHKhvac6oc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The selection of hyperparameters consists of testing the performance of the model against different combinations of hyperparameters, selecting those that perform best according to a chosen metric and a validation method."
      ],
      "metadata": {
        "id": "tAefLAMEdQ-x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For **hyperparameter tuning**, we need to split our training data again into a set for training and a set for testing the hyperparameters (often called validation set). It is a very common practice to use **k-fold cross-validation** for hyperparameter tuning. **The training set is divided again into k equal-sized samples,** 1 sample is used for testing and the remaining k-1 samples are used for training the model, repeating the process k times. Then, the k evaluation metrics (in this case the accuracy) are averaged to produce a single estimator."
      ],
      "metadata": {
        "id": "wkyiVO-PdiN2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "There are multiple techniques to find the best hyperparameters for a model. The most popular methods are **(1) grid search**, **(2) random search**, and **(3) bayesian optimization**. \n",
        "\n",
        "**Grid search **test all combinations of hyperparameters and select the best performing one. It is a really time-consuming method, particularly when the number of hyperparameters and values to try are really high.\n",
        "\n",
        "In random search, you specify a grid of hyperparameters, and random combinations are selected where each combination of hyperparameters has an equal chance of being sampled. We do not analyze all combinations of hyperparameters, but only random samples of those combinations. This approach is much more computationally efficient than trying all combinations; however, it also has some disadvantages. The main drawback of random search is that not all areas of the grid are evenly covered, especially when the number of combinations selected from the grid is low."
      ],
      "metadata": {
        "id": "xL9WgDiIhx4X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV,GridSearchCV"
      ],
      "metadata": {
        "id": "7VZqg4pFBp6A"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hyperparameter tunning for GradientBoosting Classifier**"
      ],
      "metadata": {
        "id": "jvgg5BWhiUo5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# params\n",
        "grid_params = {'n_estimators' : [80, 90, 100, 110, 115, 120],\n",
        "               'learning_rate':[0.1,0.05,0.5,0.3,0.7,0.06,0.002,0.9,0.005],\n",
        "               'min_samples_split': [2,3,4,5,6,7,8],\n",
        "               'max_depth':[2,3,4,5,6,7],\n",
        "               'max_features':[None, 'auto', 'sqrt', 'log2']\n",
        "               }\n",
        "random_search = RandomizedSearchCV(estimator=GradientBoostingClassifier(),\n",
        "                                   param_distributions=grid_params,\n",
        "                                   n_iter=200,\n",
        "                                   cv=5,\n",
        "                                   n_jobs=-1)\n",
        "random_search.fit(X_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7I7HPRXBrDp",
        "outputId": "6de17eaa-5088-4475-dbc8-eef372332307"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_iter=200,\n",
              "                   n_jobs=-1,\n",
              "                   param_distributions={'learning_rate': [0.1, 0.05, 0.5, 0.3,\n",
              "                                                          0.7, 0.06, 0.002, 0.9,\n",
              "                                                          0.005],\n",
              "                                        'max_depth': [2, 3, 4, 5, 6, 7],\n",
              "                                        'max_features': [None, 'auto', 'sqrt',\n",
              "                                                         'log2'],\n",
              "                                        'min_samples_split': [2, 3, 4, 5, 6, 7,\n",
              "                                                              8],\n",
              "                                        'n_estimators': [80, 90, 100, 110, 115,\n",
              "                                                         120]})"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POIyJWnTBuBN",
        "outputId": "e83bb00b-d392-4216-b3eb-167c99a5b35f"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_estimators': 90,\n",
              " 'min_samples_split': 4,\n",
              " 'max_features': 'log2',\n",
              " 'max_depth': 3,\n",
              " 'learning_rate': 0.1}"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **8.Performace of the model**\n",
        "\n",
        "The last step of the machine learning process is to check the performance of the model (best hyperparameters ) by using the **confusion matrix** and some evaluation metrics."
      ],
      "metadata": {
        "id": "kP_BY1hRmdmT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that the model is trained, it is time to evaluate its performance using the testing set. First, we use the previous model (gradient boosting classifier with best hyperparameters) to predict the class labels of the testing data (with the predict method)."
      ],
      "metadata": {
        "id": "nBsnGQ3ym3lx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_search_predict = random_search.predict(X_test)\n",
        "accuracy_score(y_test,random_search_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fT2ezRilmrSY",
        "outputId": "3ae838bf-020a-4f89-e048-d5e6df8c2937"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7924662402274343"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report"
      ],
      "metadata": {
        "id": "ixcum42spbCC"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,random_search_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iqEEAn0LpkX-",
        "outputId": "3e0e89a1-d251-4233-e569-94409866b5c8"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[929, 104],\n",
              "       [188, 186]])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,random_search_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvJh64-mpw8g",
        "outputId": "162e66a4-fbd9-43a6-8655-7071ca5e2168"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.90      0.86      1033\n",
            "           1       0.64      0.50      0.56       374\n",
            "\n",
            "    accuracy                           0.79      1407\n",
            "   macro avg       0.74      0.70      0.71      1407\n",
            "weighted avg       0.78      0.79      0.78      1407\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Drawing conclusions — Summary**"
      ],
      "metadata": {
        "id": "ouVBNQrUqjR6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this project, we have walked through a complete end-to-end machine learning project using the Telco customer Churn dataset. We started by cleaning the data and analyzing it with visualization. Then, to be able to build a machine learning model, we transformed the categorical data into numeric variables (feature engineering). After transforming the data, we tried  different machine learning algorithms using default parameters. Finally, we tuned the hyperparameters of the Gradient Boosting Classifier (best performance model) for model optimization, obtaining an accuracy of nearly 80% (close to 6% higher than the baseline)"
      ],
      "metadata": {
        "id": "msvGcbvZqogZ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MW3C8e80p69I"
      },
      "execution_count": 67,
      "outputs": []
    }
  ]
}