Customer_Churn_Prediction_using_Logistic_Regression.ipynb

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      "name": "Customer Churn Prediction using Logistic Regression.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mayorofdata/Customer-Churn-Prediction-using-Logistic-Regression/blob/master/Customer_Churn_Prediction_using_Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
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      "source": [
        "# Background Statement\n",
        "\n",
        "Customer attrition, which is described in this project as customer churn is an important business metrics in most organizations. Organizations such as Telephone service companies, Internet service providers and TV subscription companies are the most affected as customer churn has direct impact on their revenues. Companies from these sectors, therefore tends to need the help of data analytics to better understand the rates of which their customer churn and the factors or variables that are responsible for the defect.  \n",
        "\n",
        "Relevant visualization with dashboard and analysis is performed using Tableau or Power BI to better understand the behavior of customer and underlying patterns in the dataset. The dataset will be used to establish causal links within the different variables. \n",
        "\n",
        "Furthermore, the future of the business and the likelihood of customer churning will be predicted using Python by estimating the relationship between the variables. Focus will be made on variables that are important in customer retention and promptly detecting customers who are most venerable to churn. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6AicLSxuIL9",
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      "source": [
        "# Problem Statement\n",
        "The aim of this project is to analyse the customers behaviour by predicting which customers will churn the company. \n",
        "\n",
        "The dataset contains the following attributes:\n",
        "\n",
        "Inputs:\n",
        "customerID, gender, SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines, InternetService, OnlineSecurity,\n",
        "OnlineBackup, DeviceProtection, TechSupport, StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod,   MonthlyCharges, TotalCharges         \n",
        "\n",
        "Outputs:\n",
        "- Churn(No: customers who stay, Yes: Customers who churn)\n",
        "\n",
        "The following steps are followed for the data analysis and Prediction:\n",
        "- Step 1: Import Libraries and datasets\n",
        "- Step 2: EDA - Explore/Visualize Dataset\n",
        "- Step 3: Prepare the data for training\n",
        "- Step 4: Model Training\n",
        "- Step 5: Model Testing\n",
        "- Step 6: Model Accuracy\n",
        "\n",
        "![](https://drive.google.com/uc?export=view&id=1pt__WHzBlTxEgC4L7mWTxB4gpz6baCaF)"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# The model is created using logistic regresssion\n",
        "- Logistic regression is a statistical and machine learning technique for classifying records of a dataset, based on the values of the input fields.\n",
        "- Logistic regression is analogous to linear regression but tries to predict a categorical or discrete target field instead of a numeric one.\n",
        "- In logistic regression, we predict a variable which is binary, such as, Yes/No, TRUE/FALSE, successful or Not successful, pregnant/Not pregnant, and so on, all of which can all be coded as 0 or 1.\n",
        "- Logistic regression returns a probability score between 0 and 1 for a given sample of data. It predicts the probability of that sample, and we map the cases to a discrete class based on that probability.\n",
        "\n",
        "![](https://drive.google.com/uc?export=view&id=1ORVrPxyvnGFmbjarr0iOQJr3adDqZd8p)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pi6PNYzmuIME",
        "colab_type": "text"
      },
      "source": [
        "# Step #1: Import Libraries and datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qt37ZAXfuIMH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gf6ZsG6zurLz",
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          "height": 34
        },
        "outputId": "a9b9dcaf-e687-4c78-af19-5026dcacdff2"
      },
      "source": [
        "# read the data using pandas dataframe\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "df = pd.read_csv(\"/content/drive/My Drive/DATA-475/Telco-Customer-Churn.csv\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HmV-FtmluIMS",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "35fc7e12-050a-479b-d6bc-b319a9ee053e"
      },
      "source": [
        "# Show the data first five rows\n",
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7590-VHVEG</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5575-GNVDE</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3668-QPYBK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7795-CFOCW</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9237-HQITU</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn\n",
              "0  7590-VHVEG  Female              0  ...          29.85        29.85     No\n",
              "1  5575-GNVDE    Male              0  ...          56.95       1889.5     No\n",
              "2  3668-QPYBK    Male              0  ...          53.85       108.15    Yes\n",
              "3  7795-CFOCW    Male              0  ...          42.30      1840.75     No\n",
              "4  9237-HQITU  Female              0  ...          70.70       151.65    Yes\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
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    {
      "cell_type": "code",
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          "height": 309
        },
        "outputId": "7c36cf63-221e-414a-a8a8-22969f509d8c"
      },
      "source": [
        "# Show the data last five rows\n",
        "df.tail()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>6840-RESVB</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>24</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>84.80</td>\n",
              "      <td>1990.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>2234-XADUH</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>72</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>103.20</td>\n",
              "      <td>7362.9</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>4801-JZAZL</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</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>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>29.60</td>\n",
              "      <td>346.45</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>8361-LTMKD</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>4</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>74.40</td>\n",
              "      <td>306.6</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>3186-AJIEK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>66</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Two year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>105.65</td>\n",
              "      <td>6844.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      customerID  gender  SeniorCitizen  ... MonthlyCharges TotalCharges  Churn\n",
              "7038  6840-RESVB    Male              0  ...          84.80       1990.5     No\n",
              "7039  2234-XADUH  Female              0  ...         103.20       7362.9     No\n",
              "7040  4801-JZAZL  Female              0  ...          29.60       346.45     No\n",
              "7041  8361-LTMKD    Male              1  ...          74.40        306.6    Yes\n",
              "7042  3186-AJIEK    Male              0  ...         105.65       6844.5     No\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrXylZCguIMe",
        "colab_type": "code",
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          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "e6b434b4-b63e-4595-d896-4cff0745bb1c"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customerID           object\n",
              "gender               object\n",
              "SeniorCitizen         int64\n",
              "Partner              object\n",
              "Dependents           object\n",
              "tenure                int64\n",
              "PhoneService         object\n",
              "MultipleLines        object\n",
              "InternetService      object\n",
              "OnlineSecurity       object\n",
              "OnlineBackup         object\n",
              "DeviceProtection     object\n",
              "TechSupport          object\n",
              "StreamingTV          object\n",
              "StreamingMovies      object\n",
              "Contract             object\n",
              "PaperlessBilling     object\n",
              "PaymentMethod        object\n",
              "MonthlyCharges      float64\n",
              "TotalCharges         object\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "# Step #2: EDA - Explore/Visualize Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQJn600_uIMn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "b60fc690-fb0f-4dda-f2fe-b32a4d271b18"
      },
      "source": [
        "plt.figure(figsize=(5, 5))\n",
        "sns.boxplot(x='Churn', y='tenure',data=df)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b60f0a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nb8IGcQSuIMt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "d0594fde-1cf9-4732-ede0-4f715e0fc509"
      },
      "source": [
        "plt.figure(figsize=(5, 5))\n",
        "sns.boxplot(x='Churn', y='MonthlyCharges',data=df)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b605e2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RW57K5ZuIMz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f36440e1-8c06-47c1-a2c5-18d9aa7f8b8a"
      },
      "source": [
        "df['SeniorCitizen'].hist()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b5b469e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ymMFTOJ8GHkiyHXgRuK51fwi4BpgBXgNuAqiqY0luBx5r/W6rqtffHJYkLbMFQ7+qPjrPok2n6VvAzfNsZzewe1GjkySNlJ/IlaSOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1JGxh36SLUm+mWQmya3j3r8k9WysoZ/kLOBXgauBS4CPJrlknGOQpJ6N+0z/cmCmql6oqj8D7ge2jnkMktStNWPe3zrgpTnzh4Er5nZIsgPY0WZnk3xzCfu7APjTJax/RnLHuPf4QytS7wqz5j50V/MH71hSzX9lvgXjDv0FVdUuYNcotpXk8aqaHMW2VoPe6gVr7oU1j864L+8cAS6aM7++tUmSxmDcof8YsDHJxUnOBq4H9o15DJLUrbFe3qmqk0n+IfAwcBawu6qeWcZdjuQy0SrSW71gzb2w5hFJVS3HdiVJb0F+IleSOmLoS1JHVn3oL/S1DknekeSLbfmjSTaMf5SjNUTN/yTJs0meSnIgybzP7K4Ww359R5K/m6SSrPrH+4apOcl17W/9TJLfGvcYR22If9t/OckjSZ5s/76vWYlxjkqS3UmOJvnGPMuT5K72+3gqyWVL3mlVrdofBjeD/wj4q8DZwB8Al7yuzz8Afr1NXw98caXHPYaaPwj8pTb9Sz3U3Pq9C/gqcBCYXOlxj+HvvBF4Ejivzf/4So97DDXvAn6pTV8CHFrpcS+x5p8DLgO+Mc/ya4D/BAS4Enh0qftc7Wf6w3ytw1ZgT5t+ENiUJGMc46gtWHNVPVJVr7XZgww+D7GaDfv1HbcDdwDfG+fglskwNf994Fer6jhAVR0d8xhHbZiaC/jRNv1u4H+OcXwjV1VfBY69SZetwL01cBBYm+TCpexztYf+6b7WYd18farqJHACeM9YRrc8hql5ru0MzhRWswVrbm97L6qqL49zYMtomL/zTwI/meS/JjmYZMvYRrc8hqn5XwG/mOQw8BDw8fEMbcUs9v/7gt5yX8Og0Unyi8Ak8DdXeizLKcnbgF8BPrbCQxm3NQwu8UwxeDf31SSXVtWrKzqq5fVR4PNVdWeSDwBfSPIzVfV/V3pgq8VqP9Mf5msdftgnyRoGbwm/M5bRLY+hvsoiyd8C/iXw4ar6/pjGtlwWqvldwM8A00kOMbj2uW+V38wd5u98GNhXVf+nqr4F/A8GB4HVapiatwMPAFTVfwPeyeDL2P6iGvlX16z20B/max32Adva9EeAr1S7Q7JKLVhzkvcDv8Eg8Ff7dV5YoOaqOlFVF1TVhqrawOA+xoer6vGVGe5IDPNv+z8yOMsnyQUMLve8MM5BjtgwNf8xsAkgyV9nEPp/MtZRjtc+4Mb2FM+VwImqenkpG1zVl3dqnq91SHIb8HhV7QPuYfAWcIbBDZPrV27ESzdkzf8G+BHgd9o96z+uqg+v2KCXaMia/0IZsuaHgc1JngV+APzTqlq172KHrPkW4DeT/GMGN3U/tppP4pL8NoMD9wXtPsVO4O0AVfXrDO5bXAPMAK8BNy15n6v49yVJWqTVfnlHkrQIhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyP8DTiI9YrO0KwYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3S99UgvkuIM3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "de1b15d8-3405-4d76-98f1-1e18a5e36c22"
      },
      "source": [
        "df['tenure'].hist(bins = 40)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b5a5e358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "AihzGt0yuIM7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "71cc1981-603e-4d25-9585-2ab663c1578e"
      },
      "source": [
        "df['MonthlyCharges'].hist(bins = 30)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b5a7d240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOZaLuXkuIM_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Converting the categorical values used as input for the prediction into numeric variables.\n",
        "#This approach used for the conversion is called one hot encoding\n",
        "\n",
        "df = pd.get_dummies(df, columns=[\"PaperlessBilling\", \"PaymentMethod\", \"Contract\", \"InternetService\", \"TechSupport\"])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "d39VqPR6uINH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "outputId": "cae8db96-d225-4d93-a9a6-e7bf4a7bf376"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>PaperlessBilling_No</th>\n",
              "      <th>PaperlessBilling_Yes</th>\n",
              "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>TechSupport_No</th>\n",
              "      <th>TechSupport_No internet service</th>\n",
              "      <th>TechSupport_Yes</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>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </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>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </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>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>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>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customerID  gender  ...  TechSupport_No internet service TechSupport_Yes\n",
              "0  7590-VHVEG  Female  ...                                0               0\n",
              "1  5575-GNVDE    Male  ...                                0               0\n",
              "2  3668-QPYBK    Male  ...                                0               0\n",
              "3  7795-CFOCW    Male  ...                                0               1\n",
              "4  9237-HQITU  Female  ...                                0               0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5cyA8jguINL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "non_churner = df[df[\"Churn\"]==\"No\"]\n",
        "churner = df[df[\"Churn\"]==\"Yes\"]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "uCrNQ3aTuINP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "23e62070-60f5-44b2-e59d-41a545bf2460"
      },
      "source": [
        "print(\"Total =\", len(df))\n",
        "\n",
        "print(\"Number of customers that stay =\", len(non_churner))\n",
        "print(\"Percentage of customers that stay =\", '%.2f'%(1.*len(non_churner)/len(df)*100.0), \"%\")\n",
        " \n",
        "print(\"Number of customers that churn =\", len(churner))\n",
        "print(\"Percentage of Number of customers that churn =\", '%.2f'%(1.*len(churner)/len(df)*100.0), \"%\")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total = 7043\n",
            "Number of customers that stay = 5174\n",
            "Percentage of customers that stay = 73.46 %\n",
            "Number of customers that churn = 1869\n",
            "Percentage of Number of customers that churn = 26.54 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rkIxQXvuINT",
        "colab_type": "text"
      },
      "source": [
        "# Step #3: Prepare the data for training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HkwWuZuwuINU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "6fc4b1c8-1b60-488f-a421-79ae096f856a"
      },
      "source": [
        "df"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customerID</th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>PaperlessBilling_No</th>\n",
              "      <th>PaperlessBilling_Yes</th>\n",
              "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>TechSupport_No</th>\n",
              "      <th>TechSupport_No internet service</th>\n",
              "      <th>TechSupport_Yes</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>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </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>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </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>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>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>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>6840-RESVB</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>24</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>84.80</td>\n",
              "      <td>1990.5</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <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>7039</th>\n",
              "      <td>2234-XADUH</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>72</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>103.20</td>\n",
              "      <td>7362.9</td>\n",
              "      <td>No</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>4801-JZAZL</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>11</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>29.60</td>\n",
              "      <td>346.45</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>8361-LTMKD</td>\n",
              "      <td>Male</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>4</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>No</td>\n",
              "      <td>74.40</td>\n",
              "      <td>306.6</td>\n",
              "      <td>Yes</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>3186-AJIEK</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>66</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>105.65</td>\n",
              "      <td>6844.5</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7043 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      customerID  gender  ...  TechSupport_No internet service TechSupport_Yes\n",
              "0     7590-VHVEG  Female  ...                                0               0\n",
              "1     5575-GNVDE    Male  ...                                0               0\n",
              "2     3668-QPYBK    Male  ...                                0               0\n",
              "3     7795-CFOCW    Male  ...                                0               1\n",
              "4     9237-HQITU  Female  ...                                0               0\n",
              "...          ...     ...  ...                              ...             ...\n",
              "7038  6840-RESVB    Male  ...                                0               1\n",
              "7039  2234-XADUH  Female  ...                                0               0\n",
              "7040  4801-JZAZL  Female  ...                                0               0\n",
              "7041  8361-LTMKD    Male  ...                                0               0\n",
              "7042  3186-AJIEK    Male  ...                                0               1\n",
              "\n",
              "[7043 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rB_G5vaeuINZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Let's drop the attributes not needed\n",
        "df.drop([\"customerID\",\n",
        "         \"gender\",\n",
        "         \"SeniorCitizen\",\n",
        "         \"Partner\", \n",
        "         \"Dependents\",\n",
        "         \"PhoneService\",\n",
        "         \"MultipleLines\",\n",
        "         \"OnlineSecurity\",\n",
        "         \"OnlineBackup\",\n",
        "         \"DeviceProtection\",\n",
        "         \"StreamingTV\",\n",
        "         \"StreamingMovies\",\n",
        "         \"TotalCharges\"\n",
        "         \n",
        "        ],axis=1,inplace=True\n",
        "       )\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAS0M-zFuINd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "outputId": "72ec35a5-9720-423b-c6ce-1b140dfdb767"
      },
      "source": [
        "df"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>PaperlessBilling_No</th>\n",
              "      <th>PaperlessBilling_Yes</th>\n",
              "      <th>PaymentMethod_Bank transfer (automatic)</th>\n",
              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
              "      <th>PaymentMethod_Electronic check</th>\n",
              "      <th>PaymentMethod_Mailed check</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</th>\n",
              "      <th>TechSupport_No</th>\n",
              "      <th>TechSupport_No internet service</th>\n",
              "      <th>TechSupport_Yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>34</td>\n",
              "      <td>56.95</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>Yes</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>42.30</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>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>4</th>\n",
              "      <td>2</td>\n",
              "      <td>70.70</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>24</td>\n",
              "      <td>84.80</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <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>7039</th>\n",
              "      <td>72</td>\n",
              "      <td>103.20</td>\n",
              "      <td>No</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>11</td>\n",
              "      <td>29.60</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>4</td>\n",
              "      <td>74.40</td>\n",
              "      <td>Yes</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>66</td>\n",
              "      <td>105.65</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>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>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7043 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      tenure  MonthlyCharges  ... TechSupport_No internet service  TechSupport_Yes\n",
              "0          1           29.85  ...                               0                0\n",
              "1         34           56.95  ...                               0                0\n",
              "2          2           53.85  ...                               0                0\n",
              "3         45           42.30  ...                               0                1\n",
              "4          2           70.70  ...                               0                0\n",
              "...      ...             ...  ...                             ...              ...\n",
              "7038      24           84.80  ...                               0                1\n",
              "7039      72          103.20  ...                               0                0\n",
              "7040      11           29.60  ...                               0                0\n",
              "7041       4           74.40  ...                               0                0\n",
              "7042      66          105.65  ...                               0                1\n",
              "\n",
              "[7043 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5M18P6NuuINh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Let's drop the target coloumn before we do train test split\n",
        "X = df.drop('Churn',axis=1).values\n",
        "y = df['Churn'].values"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8GtYpgNuINm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature Scaling to normalize the dataset:\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X = sc.fit_transform(X)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Q1Z0BTSuINp",
        "colab_type": "text"
      },
      "source": [
        "# Step #4: Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGQQ-tTEuINr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting the dataset into train and test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbesoKGquINu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "09b3c31b-184e-47d0-9c70-f877c433a56a"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1761,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7n_M0j2HuINy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "23d8a0d5-fb48-4263-efcc-b337e7aa9f26"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5282,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qgWaa9njuIN2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "fa3dfe45-6524-4b38-a9d4-7b1d99d6fef0"
      },
      "source": [
        "X_train"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.34087622, -0.51388388, -0.8297975 , ...,  1.0138687 ,\n",
              "        -0.52592737, -0.63943864],\n",
              "       [ 0.88073469,  0.01622936,  1.20511329, ..., -0.98632101,\n",
              "        -0.52592737,  1.56387171],\n",
              "       [-1.19600386, -0.81799273, -0.8297975 , ...,  1.0138687 ,\n",
              "        -0.52592737, -0.63943864],\n",
              "       ...,\n",
              "       [ 1.04361615, -1.46276997,  1.20511329, ..., -0.98632101,\n",
              "         1.90140322, -0.63943864],\n",
              "       [-1.27744458,  0.16579109, -0.8297975 , ...,  1.0138687 ,\n",
              "        -0.52592737, -0.63943864],\n",
              "       [-1.15528349, -1.47440255,  1.20511329, ..., -0.98632101,\n",
              "         1.90140322, -0.63943864]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qAsMZbMuIN6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3b0107a8-f651-4193-9552-90ab9a90fc76"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'No', 'Yes', ..., 'No', 'Yes', 'No'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blkgx07QuIN_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "d6c654bf-0aaf-4505-c8f7-6748e6b56a18"
      },
      "source": [
        "# Fitting Logistic Regression to the Training set\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VbePzt7nuIOO",
        "colab_type": "text"
      },
      "source": [
        "# Step #5: Model Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "45aGln0huIOP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "4add9809-7c55-4cde-c5fd-24f4b26900f5"
      },
      "source": [
        "# y_proba returns estimates for all classes, ordered by the label of classes.\n",
        "# So, the first column is the probability of non churn class, P(Y=No|X), \n",
        "# and second column is probability of churn class, P(Y=Yes|X):\n",
        "\n",
        "y_proba = classifier.predict_proba(X_train)\n",
        "y_proba"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.75525729, 0.24474271],\n",
              "       [0.96925158, 0.03074842],\n",
              "       [0.59628138, 0.40371862],\n",
              "       ...,\n",
              "       [0.99222344, 0.00777656],\n",
              "       [0.24800549, 0.75199451],\n",
              "       [0.85083446, 0.14916554]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ic9uTpB8uIOS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0d607c7e-185c-46f6-8924-66aeb01b0f32"
      },
      "source": [
        "y_predict_train = classifier.predict(X_train)\n",
        "y_predict_train"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'No', 'No', ..., 'No', 'Yes', 'No'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZCpIg6FuIOW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ebed4e86-27aa-4a3f-bece-66ee9fb97507"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'No', 'Yes', ..., 'No', 'Yes', 'No'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZCitHiBuIOb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "77b05fb4-df08-4fda-9aeb-9197f3bcf053"
      },
      "source": [
        "y_predict_test = classifier.predict(X_test)\n",
        "y_predict_test"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No', 'No', 'No', ..., 'No', 'No', 'No'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SFvVhA3uIOh",
        "colab_type": "text"
      },
      "source": [
        "# Step #6: Model Accuracy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tFhTQVrtuIOh",
        "colab_type": "text"
      },
      "source": [
        "A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known. \n",
        "\n",
        "It allows the visualization of the performance of an algorithm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "BPuVrF89uIOi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "b8e90dc5-7b80-493d-a06c-2c1ec81e7d72"
      },
      "source": [
        "# Checking the accuracy of classifier using confusion matrix.\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_predict_test)\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f09b004ab00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "AVUHkLI2uIOk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "0e23c596-9c1e-437e-bbb6-d9fc16f7297d"
      },
      "source": [
        "# we can calculate precision and recall as well as the F1-score of each label\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_predict_test))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "          No       0.84      0.90      0.87      1298\n",
            "         Yes       0.64      0.51      0.57       463\n",
            "\n",
            "    accuracy                           0.80      1761\n",
            "   macro avg       0.74      0.70      0.72      1761\n",
            "weighted avg       0.79      0.80      0.79      1761\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BesU9GAWuIOn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}