Telco_Customer_Churn_Dataset.ipynb

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      "name": "Telco Customer Churn Dataset.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hargurjeet/DeepLearning/blob/main/Telco_Customer_Churn_Dataset.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",
      "metadata": {
        "id": "z1tNwgHJuc22"
      },
      "source": [
        "# Telco Customer Churn Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33FGX6ZnuwGm"
      },
      "source": [
        "# 1: Introduction\n",
        "\n",
        "Context\n",
        "<br>\"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs.\" \n",
        "\n",
        "Content\n",
        "<br>Each row represents a customer, each column contains customer’s attributes described on the column Metadata.\n",
        "\n",
        "The data set includes information about:\n",
        "<br> 1.Customers who left within the last month – the column is called Churn\n",
        "<br> 2.Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies\n",
        "<br> 3.Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges\n",
        "<br> 4.Demographic info about customers – gender, age range, and if they have partners and dependents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-G1DXgbPvhm9"
      },
      "source": [
        "# 2: Data preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcDbQYskvmc4"
      },
      "source": [
        "## 2.1 Downloading the Dataset from Kaggle"
      ]
    },
    {
      "cell_type": "code",
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        "id": "Jq2pQIfkQbqN",
        "outputId": "d1c5b40f-4c8a-45a2-b6b4-93ce5207446f"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      Upload widget is only available when the cell has been executed in the\n",
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"hargurjeet\",\"key\":\"c3882bdbb49388021171402c7018655e\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFBeNfM2WCsO",
        "outputId": "86295bed-68af-4d85-f56e-a48e9e27f4df"
      },
      "source": [
        "! pip install opendatasets --upgrade\n",
        "import opendatasets as od\n",
        "\n",
        "dataset_url = 'https://www.kaggle.com/blastchar/telco-customer-churn'\n",
        "od.download('https://www.kaggle.com/blastchar/telco-customer-churn')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting opendatasets\n",
            "  Downloading https://files.pythonhosted.org/packages/18/99/aaa3ebec81dc347302e730e0daff61735ed2f3e736129553fb3f9bf67ed3/opendatasets-0.1.10-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.6/dist-packages (from opendatasets) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: click in /usr/local/lib/python3.6/dist-packages (from opendatasets) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: kaggle in /usr/local/lib/python3.6/dist-packages (from opendatasets) (1.5.10)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: urllib3 in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (4.0.1)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle->opendatasets) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle->opendatasets) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle->opendatasets) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: text-unidecode>=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 172k/172k [00:00<00:00, 34.7MB/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading telco-customer-churn.zip to ./telco-customer-churn\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_9Nrgd1iv1FE"
      },
      "source": [
        "## 2.2 Reading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syOFyun_McRa"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('/content/telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "w-0V9NLRMir5",
        "outputId": "cb417bd8-e5cb-477f-d7d8-fec3eec36c68"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcSREWJXv6ru"
      },
      "source": [
        "## 2.3 Cleaning the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEMlekskMkCt"
      },
      "source": [
        "# Removing the unwanted columns\n",
        "df.drop('customerID', axis = 1, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "u3E6PeqZNJFT",
        "outputId": "d0e20532-fd7d-4989-ef2c-6dcd1f8a50c5"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.85</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.5</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.15</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>45</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>One year</td>\n",
              "      <td>No</td>\n",
              "      <td>Bank transfer (automatic)</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.75</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.65</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0  Female              0     Yes  ...          29.85         29.85    No\n",
              "1    Male              0      No  ...          56.95        1889.5    No\n",
              "2    Male              0      No  ...          53.85        108.15   Yes\n",
              "3    Male              0      No  ...          42.30       1840.75    No\n",
              "4  Female              0      No  ...          70.70        151.65   Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wF5cIm-lNRJr",
        "outputId": "a872144e-826e-4355-e7e3-56f0d78c6c05"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDdNFM5kNn-v"
      },
      "source": [
        "# converting the TotalCharges to float\n",
        "df.TotalCharges = pd.to_numeric(df.TotalCharges, errors='coerce', downcast='float')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8wrm09kOExE",
        "outputId": "1abee048-e216-4749-83a1-81042c4377ed"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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        float32\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCkvGWcuOsL8",
        "outputId": "60c640eb-ef3d-4844-833f-d9cfec7458fd"
      },
      "source": [
        "pd.notna(df.TotalCharges)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       True\n",
              "1       True\n",
              "2       True\n",
              "3       True\n",
              "4       True\n",
              "        ... \n",
              "7038    True\n",
              "7039    True\n",
              "7040    True\n",
              "7041    True\n",
              "7042    True\n",
              "Name: TotalCharges, Length: 7043, dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "wRqpQI4HQWl1",
        "outputId": "31d3287c-3830-4ac7-8164-921ac1eef46e"
      },
      "source": [
        "# These are the rows having TotalCharges as null\n",
        "df[pd.notna(df.TotalCharges)==False]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>488</th>\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>No</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>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>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>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>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>Yes</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>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>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>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>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>Yes</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>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>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>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>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>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>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>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>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>Two year</td>\n",
              "      <td>No</td>\n",
              "      <td>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>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>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>No internet service</td>\n",
              "      <td>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>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>Yes</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>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>Yes</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",
              "</div>"
            ],
            "text/plain": [
              "      gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "488   Female              0     Yes  ...          52.55           NaN    No\n",
              "753     Male              0      No  ...          20.25           NaN    No\n",
              "936   Female              0     Yes  ...          80.85           NaN    No\n",
              "1082    Male              0     Yes  ...          25.75           NaN    No\n",
              "1340  Female              0     Yes  ...          56.05           NaN    No\n",
              "3331    Male              0     Yes  ...          19.85           NaN    No\n",
              "3826    Male              0     Yes  ...          25.35           NaN    No\n",
              "4380  Female              0     Yes  ...          20.00           NaN    No\n",
              "5218    Male              0     Yes  ...          19.70           NaN    No\n",
              "6670  Female              0     Yes  ...          73.35           NaN    No\n",
              "6754    Male              0      No  ...          61.90           NaN    No\n",
              "\n",
              "[11 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoPoFiIwdEU6",
        "outputId": "947ae45a-f4bb-4f79-9d10-3c9a3cd8c45b"
      },
      "source": [
        "df[pd.notna(df.TotalCharges)==False].index"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([488, 753, 936, 1082, 1340, 3331, 3826, 4380, 5218, 6670, 6754], dtype='int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SchqWmtQ8bd"
      },
      "source": [
        "# Dropping these records\n",
        "df.drop(df[pd.notna(df.TotalCharges)==False].index, inplace =True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HM73GSLmRjli",
        "outputId": "fdbe01c3-72d8-4ab7-d1e2-17a572f24c5e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7032, 20)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "JkB074lYYyDH",
        "outputId": "2aad9ec3-b2ab-499e-c1f7-ce3275e66819"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>InternetService</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>Contract</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>PaymentMethod</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>1</td>\n",
              "      <td>No</td>\n",
              "      <td>No phone service</td>\n",
              "      <td>DSL</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.850000</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>1889.500000</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>108.150002</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>1840.750000</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>151.649994</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   gender  SeniorCitizen Partner  ... MonthlyCharges  TotalCharges Churn\n",
              "0  Female              0     Yes  ...          29.85     29.850000    No\n",
              "1    Male              0      No  ...          56.95   1889.500000    No\n",
              "2    Male              0      No  ...          53.85    108.150002   Yes\n",
              "3    Male              0      No  ...          42.30   1840.750000    No\n",
              "4  Female              0      No  ...          70.70    151.649994   Yes\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpOFqNFUw3_k"
      },
      "source": [
        "## 2.4 Processing the column values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8H8VYul1dQRF",
        "outputId": "272f6e43-e94f-443f-a5a9-173f3f9f632a"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
              "       'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity',\n",
              "       'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',\n",
              "       'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod',\n",
              "       'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wO94jLN7gbc2",
        "outputId": "5f39ee48-d456-43a7-be1f-1fb6656e1ff3"
      },
      "source": [
        "for columns in df.columns:\n",
        "  print(f'{columns}: {pd.unique(df[columns])}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: ['Female' 'Male']\n",
            "SeniorCitizen: [0 1]\n",
            "Partner: ['Yes' 'No']\n",
            "Dependents: ['No' 'Yes']\n",
            "tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
            "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
            " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
            "PhoneService: ['No' 'Yes']\n",
            "MultipleLines: ['No phone service' 'No' 'Yes']\n",
            "InternetService: ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity: ['No' 'Yes' 'No internet service']\n",
            "OnlineBackup: ['Yes' 'No' 'No internet service']\n",
            "DeviceProtection: ['No' 'Yes' 'No internet service']\n",
            "TechSupport: ['No' 'Yes' 'No internet service']\n",
            "StreamingTV: ['No' 'Yes' 'No internet service']\n",
            "StreamingMovies: ['No' 'Yes' 'No internet service']\n",
            "Contract: ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling: ['Yes' 'No']\n",
            "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
            "TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
            "Churn: ['No' 'Yes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIxgyAGnW7ED"
      },
      "source": [
        "df.replace('No internet service','No',inplace=True)\n",
        "df.replace('No phone service','No',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zE74FOPTk6gr",
        "outputId": "9cb1558a-e17d-40e8-f13e-459412c1c14b"
      },
      "source": [
        "for columns in df.columns:\n",
        "  print(f'{columns}: {pd.unique(df[columns])}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: ['Female' 'Male']\n",
            "SeniorCitizen: [0 1]\n",
            "Partner: ['Yes' 'No']\n",
            "Dependents: ['No' 'Yes']\n",
            "tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
            "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
            " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
            "PhoneService: ['No' 'Yes']\n",
            "MultipleLines: ['No' 'Yes']\n",
            "InternetService: ['DSL' 'Fiber optic' 'No']\n",
            "OnlineSecurity: ['No' 'Yes']\n",
            "OnlineBackup: ['Yes' 'No']\n",
            "DeviceProtection: ['No' 'Yes']\n",
            "TechSupport: ['No' 'Yes']\n",
            "StreamingTV: ['No' 'Yes']\n",
            "StreamingMovies: ['No' 'Yes']\n",
            "Contract: ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling: ['Yes' 'No']\n",
            "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
            "TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
            "Churn: ['No' 'Yes']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nyf9PE6ZYmr2"
      },
      "source": [
        "object_columns = df.loc[:, df.dtypes==object].columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "decyS74EnWQU"
      },
      "source": [
        "for col in object_columns:\n",
        "    df[col].replace({'Yes': 1,'No': 0},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OO03teUSYbyY",
        "outputId": "3f14fb83-087f-4da3-d802-59c9397818f4"
      },
      "source": [
        "for columns in df.columns:\n",
        "  print(f'{columns}: {pd.unique(df[columns])}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: ['Female' 'Male']\n",
            "SeniorCitizen: [0 1]\n",
            "Partner: [1 0]\n",
            "Dependents: [0 1]\n",
            "tenure: [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
            "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
            " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26 39]\n",
            "PhoneService: [0 1]\n",
            "MultipleLines: [0 1]\n",
            "InternetService: ['DSL' 'Fiber optic' 0]\n",
            "OnlineSecurity: [0 1]\n",
            "OnlineBackup: [1 0]\n",
            "DeviceProtection: [0 1]\n",
            "TechSupport: [0 1]\n",
            "StreamingTV: [0 1]\n",
            "StreamingMovies: [0 1]\n",
            "Contract: ['Month-to-month' 'One year' 'Two year']\n",
            "PaperlessBilling: [1 0]\n",
            "PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n",
            "MonthlyCharges: [29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
            "TotalCharges: [  29.85 1889.5   108.15 ...  346.45  306.6  6844.5 ]\n",
            "Churn: [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cw9AwQv1aGPL"
      },
      "source": [
        "df['gender'].replace({'Female':1,'Male':0},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXIjNhdZbCHy",
        "outputId": "486c0e69-bd7c-4f7f-ddde-df0cc0c2465d"
      },
      "source": [
        "df1 = pd.get_dummies(data=df, columns=['InternetService','Contract','PaymentMethod'])\n",
        "df1.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure',\n",
              "       'PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup',\n",
              "       'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n",
              "       'PaperlessBilling', 'MonthlyCharges', 'TotalCharges', 'Churn',\n",
              "       'InternetService_0', 'InternetService_DSL',\n",
              "       'InternetService_Fiber optic', 'Contract_Month-to-month',\n",
              "       'Contract_One year', 'Contract_Two year',\n",
              "       'PaymentMethod_Bank transfer (automatic)',\n",
              "       'PaymentMethod_Credit card (automatic)',\n",
              "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "YowK2Xg2bq76",
        "outputId": "5e7b6cca-4911-4a0b-d12d-28806fd8040f"
      },
      "source": [
        "df1.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>InternetService_0</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3226</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.75</td>\n",
              "      <td>141.100006</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2767</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>84.80</td>\n",
              "      <td>3958.850098</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1105</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79.75</td>\n",
              "      <td>769.099976</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>103.80</td>\n",
              "      <td>4327.500000</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3333</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>19.35</td>\n",
              "      <td>212.300003</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gender  ...  PaymentMethod_Mailed check\n",
              "3226       0  ...                           1\n",
              "2767       1  ...                           0\n",
              "1105       1  ...                           0\n",
              "83         1  ...                           0\n",
              "3333       0  ...                           0\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vl2RM08ObyIK",
        "outputId": "5614442f-ccfc-4384-b2f0-5d2597afc4e6"
      },
      "source": [
        "df1.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender                                       int64\n",
              "SeniorCitizen                                int64\n",
              "Partner                                      int64\n",
              "Dependents                                   int64\n",
              "tenure                                       int64\n",
              "PhoneService                                 int64\n",
              "MultipleLines                                int64\n",
              "OnlineSecurity                               int64\n",
              "OnlineBackup                                 int64\n",
              "DeviceProtection                             int64\n",
              "TechSupport                                  int64\n",
              "StreamingTV                                  int64\n",
              "StreamingMovies                              int64\n",
              "PaperlessBilling                             int64\n",
              "MonthlyCharges                             float64\n",
              "TotalCharges                               float32\n",
              "Churn                                        int64\n",
              "InternetService_0                            uint8\n",
              "InternetService_DSL                          uint8\n",
              "InternetService_Fiber optic                  uint8\n",
              "Contract_Month-to-month                      uint8\n",
              "Contract_One year                            uint8\n",
              "Contract_Two year                            uint8\n",
              "PaymentMethod_Bank transfer (automatic)      uint8\n",
              "PaymentMethod_Credit card (automatic)        uint8\n",
              "PaymentMethod_Electronic check               uint8\n",
              "PaymentMethod_Mailed check                   uint8\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "qN6vwuEBcgey",
        "outputId": "f8a75182-9a78-4b5f-8dd9-63942dbb2f7b"
      },
      "source": [
        "df1[['tenure','MonthlyCharges','TotalCharges']].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>29.85</td>\n",
              "      <td>29.850000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>34</td>\n",
              "      <td>56.95</td>\n",
              "      <td>1889.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>53.85</td>\n",
              "      <td>108.150002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>45</td>\n",
              "      <td>42.30</td>\n",
              "      <td>1840.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>70.70</td>\n",
              "      <td>151.649994</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   tenure  MonthlyCharges  TotalCharges\n",
              "0       1           29.85     29.850000\n",
              "1      34           56.95   1889.500000\n",
              "2       2           53.85    108.150002\n",
              "3      45           42.30   1840.750000\n",
              "4       2           70.70    151.649994"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tx2WptW4xI1l"
      },
      "source": [
        "## 2.5 Normalizing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "8MMxaRMcdCJu",
        "outputId": "555f38bb-fde3-4298-fc78-b4995d881cd4"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "std_scaler = StandardScaler()\n",
        "\n",
        "df1[['tenure','MonthlyCharges','TotalCharges']] = std_scaler.fit_transform(df1[['tenure','MonthlyCharges','TotalCharges']])\n",
        "df1[['tenure','MonthlyCharges','TotalCharges']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.280248</td>\n",
              "      <td>-1.161694</td>\n",
              "      <td>-0.994194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.064303</td>\n",
              "      <td>-0.260878</td>\n",
              "      <td>-0.173740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.239504</td>\n",
              "      <td>-0.363923</td>\n",
              "      <td>-0.959649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.512486</td>\n",
              "      <td>-0.747850</td>\n",
              "      <td>-0.195248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.239504</td>\n",
              "      <td>0.196178</td>\n",
              "      <td>-0.940457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7038</th>\n",
              "      <td>-0.343137</td>\n",
              "      <td>0.664868</td>\n",
              "      <td>-0.129180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7039</th>\n",
              "      <td>1.612573</td>\n",
              "      <td>1.276493</td>\n",
              "      <td>2.241056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7040</th>\n",
              "      <td>-0.872808</td>\n",
              "      <td>-1.170004</td>\n",
              "      <td>-0.854514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7041</th>\n",
              "      <td>-1.158016</td>\n",
              "      <td>0.319168</td>\n",
              "      <td>-0.872095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7042</th>\n",
              "      <td>1.368109</td>\n",
              "      <td>1.357932</td>\n",
              "      <td>2.012344</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7032 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        tenure  MonthlyCharges  TotalCharges\n",
              "0    -1.280248       -1.161694     -0.994194\n",
              "1     0.064303       -0.260878     -0.173740\n",
              "2    -1.239504       -0.363923     -0.959649\n",
              "3     0.512486       -0.747850     -0.195248\n",
              "4    -1.239504        0.196178     -0.940457\n",
              "...        ...             ...           ...\n",
              "7038 -0.343137        0.664868     -0.129180\n",
              "7039  1.612573        1.276493      2.241056\n",
              "7040 -0.872808       -1.170004     -0.854514\n",
              "7041 -1.158016        0.319168     -0.872095\n",
              "7042  1.368109        1.357932      2.012344\n",
              "\n",
              "[7032 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "Dm9jD3-Zd6Uv",
        "outputId": "bde65865-73b5-4a2b-b0da-c2eada8bf02f"
      },
      "source": [
        "df1.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>Partner</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>tenure</th>\n",
              "      <th>PhoneService</th>\n",
              "      <th>MultipleLines</th>\n",
              "      <th>OnlineSecurity</th>\n",
              "      <th>OnlineBackup</th>\n",
              "      <th>DeviceProtection</th>\n",
              "      <th>TechSupport</th>\n",
              "      <th>StreamingTV</th>\n",
              "      <th>StreamingMovies</th>\n",
              "      <th>PaperlessBilling</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalCharges</th>\n",
              "      <th>Churn</th>\n",
              "      <th>InternetService_0</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>Contract_Month-to-month</th>\n",
              "      <th>Contract_One year</th>\n",
              "      <th>Contract_Two year</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5393</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.192854</td>\n",
              "      <td>-0.976216</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6793</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.612573</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.449343</td>\n",
              "      <td>2.419450</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>4125</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.430998</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>0.708081</td>\n",
              "      <td>0.559580</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3804</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.913552</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.500328</td>\n",
              "      <td>-0.655627</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3336</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.919926</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.479140</td>\n",
              "      <td>-0.531300</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      gender  ...  PaymentMethod_Mailed check\n",
              "5393       1  ...                           0\n",
              "6793       0  ...                           0\n",
              "4125       0  ...                           0\n",
              "3804       0  ...                           0\n",
              "3336       1  ...                           1\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKAkzzlgd_Ws",
        "outputId": "0c68b775-4dcf-4b58-ab4e-fa76e3b994bd"
      },
      "source": [
        "for columns in df1.columns:\n",
        "  print(f'{columns}: {pd.unique(df1[columns])}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gender: [1 0]\n",
            "SeniorCitizen: [0 1]\n",
            "Partner: [1 0]\n",
            "Dependents: [0 1]\n",
            "tenure: [-1.28024804  0.06430269 -1.23950408  0.51248626 -0.99504031 -0.42462485\n",
            " -0.91355239 -0.18016108  1.2051336  -0.7913205  -0.66908862  1.04215776\n",
            "  0.6754621  -0.30239297  1.49034133  0.79769399  1.57182925 -0.46536881\n",
            " -0.83206446 -0.09867316  0.59397418  1.61257321 -0.62834466 -0.22090504\n",
            " -1.11727219  0.55323022 -0.87280842  1.53108529  1.24587756  0.43099834\n",
            " -0.70983258  1.12364568 -0.5876007   1.36810945 -0.95429635 -1.19876011\n",
            " -0.0579292   0.71620607  1.28662152  0.96066983 -1.03578427  0.39025438\n",
            "  0.10504665  0.63471814 -0.13941712  1.32736548  0.22727853  1.44959737\n",
            " -0.01718524  0.91992587  0.18653457  0.14579061  0.34951041 -1.07652823\n",
            " -1.15801615  0.02355872  1.40885341 -0.38388089  1.00141379  1.16438964\n",
            " -0.75057654 -0.50611277  0.83843795  0.30876645  1.08290172 -0.34313693\n",
            "  0.4717423  -0.54685673  0.87918191  0.75695003 -0.26164901  0.26802249]\n",
            "PhoneService: [0 1]\n",
            "MultipleLines: [0 1]\n",
            "OnlineSecurity: [0 1]\n",
            "OnlineBackup: [1 0]\n",
            "DeviceProtection: [0 1]\n",
            "TechSupport: [0 1]\n",
            "StreamingTV: [0 1]\n",
            "StreamingMovies: [0 1]\n",
            "PaperlessBilling: [1 0]\n",
            "MonthlyCharges: [-1.16169394 -0.26087792 -0.36392329 ... -0.05644919 -0.68469357\n",
            "  0.46210172]\n",
            "TotalCharges: [-0.99419409 -0.17373982 -0.95964911 ... -0.85451413 -0.87209545\n",
            "  2.01234407]\n",
            "Churn: [0 1]\n",
            "InternetService_0: [0 1]\n",
            "InternetService_DSL: [1 0]\n",
            "InternetService_Fiber optic: [0 1]\n",
            "Contract_Month-to-month: [1 0]\n",
            "Contract_One year: [0 1]\n",
            "Contract_Two year: [0 1]\n",
            "PaymentMethod_Bank transfer (automatic): [0 1]\n",
            "PaymentMethod_Credit card (automatic): [0 1]\n",
            "PaymentMethod_Electronic check: [1 0]\n",
            "PaymentMethod_Mailed check: [0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUQKkNpwgDaT"
      },
      "source": [
        "X = df1.drop('Churn', axis = 1, inplace=False)\n",
        "y = df1.Churn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e21gPtp4fwl6"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxRHbVBPgxzJ",
        "outputId": "508dd1d0-5b3d-453b-8d00-580da75d4b46"
      },
      "source": [
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(5625, 26) (1407, 26) (5625,) (1407,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mlo1PjpQfDyC"
      },
      "source": [
        "# 3: Building an ANN network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKm1K5yce0dC",
        "outputId": "db6c826d-7207-4913-812a-ea528af6ca50"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(26,)),\n",
        "    keras.layers.Dense(20, activation='relu'),\n",
        "    keras.layers.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=100)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "176/176 [==============================] - 1s 1ms/step - loss: 0.6481 - accuracy: 0.6597\n",
            "Epoch 2/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4353 - accuracy: 0.7877\n",
            "Epoch 3/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4250 - accuracy: 0.8002\n",
            "Epoch 4/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4165 - accuracy: 0.8035\n",
            "Epoch 5/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4099 - accuracy: 0.8061\n",
            "Epoch 6/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4199 - accuracy: 0.8009\n",
            "Epoch 7/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4162 - accuracy: 0.8031\n",
            "Epoch 8/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4213 - accuracy: 0.7984\n",
            "Epoch 9/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4035 - accuracy: 0.8065\n",
            "Epoch 10/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4019 - accuracy: 0.8108\n",
            "Epoch 11/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4002 - accuracy: 0.8145\n",
            "Epoch 12/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4121 - accuracy: 0.8070\n",
            "Epoch 13/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4083 - accuracy: 0.8095\n",
            "Epoch 14/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4070 - accuracy: 0.8119\n",
            "Epoch 15/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4011 - accuracy: 0.8144\n",
            "Epoch 16/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4013 - accuracy: 0.8132\n",
            "Epoch 17/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4053 - accuracy: 0.8153\n",
            "Epoch 18/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4154 - accuracy: 0.8034\n",
            "Epoch 19/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4102 - accuracy: 0.8011\n",
            "Epoch 20/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4003 - accuracy: 0.8074\n",
            "Epoch 21/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4017 - accuracy: 0.8124\n",
            "Epoch 22/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4000 - accuracy: 0.8111\n",
            "Epoch 23/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4034 - accuracy: 0.8098\n",
            "Epoch 24/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3938 - accuracy: 0.8166\n",
            "Epoch 25/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4023 - accuracy: 0.8118\n",
            "Epoch 26/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3975 - accuracy: 0.8071\n",
            "Epoch 27/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3958 - accuracy: 0.8116\n",
            "Epoch 28/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3864 - accuracy: 0.8170\n",
            "Epoch 29/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3990 - accuracy: 0.8181\n",
            "Epoch 30/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4000 - accuracy: 0.8117\n",
            "Epoch 31/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3922 - accuracy: 0.8150\n",
            "Epoch 32/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3937 - accuracy: 0.8097\n",
            "Epoch 33/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4055 - accuracy: 0.8082\n",
            "Epoch 34/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4004 - accuracy: 0.8148\n",
            "Epoch 35/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3913 - accuracy: 0.8099\n",
            "Epoch 36/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3871 - accuracy: 0.8127\n",
            "Epoch 37/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3894 - accuracy: 0.8173\n",
            "Epoch 38/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3888 - accuracy: 0.8216\n",
            "Epoch 39/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3850 - accuracy: 0.8188\n",
            "Epoch 40/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4021 - accuracy: 0.8145\n",
            "Epoch 41/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3854 - accuracy: 0.8177\n",
            "Epoch 42/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3834 - accuracy: 0.8164\n",
            "Epoch 43/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3915 - accuracy: 0.8101\n",
            "Epoch 44/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3909 - accuracy: 0.8171\n",
            "Epoch 45/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3971 - accuracy: 0.8052\n",
            "Epoch 46/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3835 - accuracy: 0.8247\n",
            "Epoch 47/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3907 - accuracy: 0.8094\n",
            "Epoch 48/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3845 - accuracy: 0.8217\n",
            "Epoch 49/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3757 - accuracy: 0.8283\n",
            "Epoch 50/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3867 - accuracy: 0.8163\n",
            "Epoch 51/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3865 - accuracy: 0.8159\n",
            "Epoch 52/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3836 - accuracy: 0.8206\n",
            "Epoch 53/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3876 - accuracy: 0.8191\n",
            "Epoch 54/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3813 - accuracy: 0.8229\n",
            "Epoch 55/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3864 - accuracy: 0.8154\n",
            "Epoch 56/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3758 - accuracy: 0.8295\n",
            "Epoch 57/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3826 - accuracy: 0.8223\n",
            "Epoch 58/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3894 - accuracy: 0.8238\n",
            "Epoch 59/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3943 - accuracy: 0.8166\n",
            "Epoch 60/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3892 - accuracy: 0.8195\n",
            "Epoch 61/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3910 - accuracy: 0.8172\n",
            "Epoch 62/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3985 - accuracy: 0.8192\n",
            "Epoch 63/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3761 - accuracy: 0.8295\n",
            "Epoch 64/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3904 - accuracy: 0.8078\n",
            "Epoch 65/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3674 - accuracy: 0.8311\n",
            "Epoch 66/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3906 - accuracy: 0.8175\n",
            "Epoch 67/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.4035 - accuracy: 0.8117\n",
            "Epoch 68/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3809 - accuracy: 0.8241\n",
            "Epoch 69/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3831 - accuracy: 0.8197\n",
            "Epoch 70/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3820 - accuracy: 0.8195\n",
            "Epoch 71/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3736 - accuracy: 0.8313\n",
            "Epoch 72/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3841 - accuracy: 0.8177\n",
            "Epoch 73/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3840 - accuracy: 0.8266\n",
            "Epoch 74/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3823 - accuracy: 0.8152\n",
            "Epoch 75/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3633 - accuracy: 0.8316\n",
            "Epoch 76/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3841 - accuracy: 0.8224\n",
            "Epoch 77/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3757 - accuracy: 0.8247\n",
            "Epoch 78/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3875 - accuracy: 0.8179\n",
            "Epoch 79/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3727 - accuracy: 0.8267\n",
            "Epoch 80/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3862 - accuracy: 0.8164\n",
            "Epoch 81/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3868 - accuracy: 0.8229\n",
            "Epoch 82/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3737 - accuracy: 0.8299\n",
            "Epoch 83/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3881 - accuracy: 0.8158\n",
            "Epoch 84/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3960 - accuracy: 0.8185\n",
            "Epoch 85/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3718 - accuracy: 0.8281\n",
            "Epoch 86/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3852 - accuracy: 0.8207\n",
            "Epoch 87/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3870 - accuracy: 0.8266\n",
            "Epoch 88/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3725 - accuracy: 0.8302\n",
            "Epoch 89/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3766 - accuracy: 0.8283\n",
            "Epoch 90/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3705 - accuracy: 0.8297\n",
            "Epoch 91/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3687 - accuracy: 0.8296\n",
            "Epoch 92/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3693 - accuracy: 0.8273\n",
            "Epoch 93/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3759 - accuracy: 0.8320\n",
            "Epoch 94/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3865 - accuracy: 0.8213\n",
            "Epoch 95/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3797 - accuracy: 0.8158\n",
            "Epoch 96/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3687 - accuracy: 0.8285\n",
            "Epoch 97/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3807 - accuracy: 0.8242\n",
            "Epoch 98/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3780 - accuracy: 0.8217\n",
            "Epoch 99/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3787 - accuracy: 0.8261\n",
            "Epoch 100/100\n",
            "176/176 [==============================] - 0s 1ms/step - loss: 0.3696 - accuracy: 0.8268\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd8b008e278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CL3A8JDcxarT"
      },
      "source": [
        "## 3.1 Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLpAFgy3Jk6Z",
        "outputId": "b7544dc1-f55a-40e7-833b-d1fabf237aff"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "44/44 [==============================] - 0s 867us/step - loss: 0.4497 - accuracy: 0.7868\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.44968217611312866, 0.7867803573608398]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJtD_Lowxf7U"
      },
      "source": [
        "## 3.2 Prediction of the trained model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zsc-J2ysi4QE",
        "outputId": "f1d3caf9-1679-49f2-8696-746c2e518d7e"
      },
      "source": [
        "yp = model.predict(X_test)\n",
        "yp[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.01928231],\n",
              "       [0.06140578],\n",
              "       [0.7297491 ],\n",
              "       [0.04457125],\n",
              "       [0.23115075]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZapkyZCi4M8"
      },
      "source": [
        "y_pred = []\n",
        "for element in yp:\n",
        "    if element > 0.5:\n",
        "        y_pred.append(1)\n",
        "    else:\n",
        "        y_pred.append(0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PykennlVi4KF",
        "outputId": "83efaaa3-e82a-4748-b726-8e74cd7b9612"
      },
      "source": [
        "y_pred[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 1, 0, 0, 0, 0, 1, 0, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcFQiN3ei4HK",
        "outputId": "78b53ac4-58d0-4aaa-ffc5-8398aae021fa"
      },
      "source": [
        "y_test[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2481    0\n",
              "6784    0\n",
              "6125    1\n",
              "3052    0\n",
              "4099    0\n",
              "3223    0\n",
              "3774    0\n",
              "3469    0\n",
              "3420    0\n",
              "1196    0\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsjxGVfbxlBz"
      },
      "source": [
        "## 3.3 Classification report"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqYkWoU-i4EP",
        "outputId": "9e87fd86-75ee-4dca-e374-c1c0f53289f1"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix , classification_report\n",
        "\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.89      0.86      1033\n",
            "           1       0.62      0.50      0.55       374\n",
            "\n",
            "    accuracy                           0.79      1407\n",
            "   macro avg       0.73      0.69      0.71      1407\n",
            "weighted avg       0.78      0.79      0.78      1407\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "k14FWe1di4Bb",
        "outputId": "c939f9bb-331c-4e68-e2ce-2dcf9de8f553"
      },
      "source": [
        "import seaborn as sn\n",
        "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_pred)\n",
        "\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}