Churn_Prediction.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
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      "source": [
        "df = pd.read_csv('WA_Fn-UseC_-Telco-Customer-Churn.csv')  \n",
        "df.head()"
      ],
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              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
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        "df.drop('customerID' , axis = 1 , inplace = True)"
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              "   gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  Female              0     Yes         No       1           No   \n",
              "1    Male              0      No         No      34          Yes   \n",
              "2    Male              0      No         No       2          Yes   \n",
              "3    Male              0      No         No      45           No   \n",
              "4  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
              "0  No phone service             DSL             No          Yes   \n",
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              "2                No             DSL            Yes          Yes   \n",
              "3  No phone service             DSL            Yes           No   \n",
              "4                No     Fiber optic             No           No   \n",
              "\n",
              "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
              "0               No          No          No              No  Month-to-month   \n",
              "1              Yes          No          No              No        One year   \n",
              "2               No          No          No              No  Month-to-month   \n",
              "3              Yes         Yes          No              No        One year   \n",
              "4               No          No          No              No  Month-to-month   \n",
              "\n",
              "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
              "0              Yes           Electronic check           29.85        29.85   \n",
              "1               No               Mailed check           56.95       1889.5   \n",
              "2              Yes               Mailed check           53.85       108.15   \n",
              "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
              "4              Yes           Electronic check           70.70       151.65   \n",
              "\n",
              "  Churn  \n",
              "0    No  \n",
              "1    No  \n",
              "2   Yes  \n",
              "3    No  \n",
              "4   Yes  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ou2JNWVDCL5",
        "outputId": "b1cc2677-babd-45a2-adc5-46755df75db0"
      },
      "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": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.to_numeric(df['TotalCharges'], errors = 'coerce')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6NT-xF2ZDQgR",
        "outputId": "1a0678d8-c1ba-41ff-f545-37cd5c96fa58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         29.85\n",
              "1       1889.50\n",
              "2        108.15\n",
              "3       1840.75\n",
              "4        151.65\n",
              "         ...   \n",
              "7038    1990.50\n",
              "7039    7362.90\n",
              "7040     346.45\n",
              "7041     306.60\n",
              "7042    6844.50\n",
              "Name: TotalCharges, Length: 7043, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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      "cell_type": "code",
      "source": [
        "df1 = df[df.TotalCharges != ' ']\n",
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8iIEqzFE-om",
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      "outputs": [
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          "data": {
            "text/plain": [
              "(7032, 20)"
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          "metadata": {},
          "execution_count": 7
        }
      ]
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    {
      "cell_type": "code",
      "source": [
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          "data": {
            "text/plain": [
              "(7043, 20)"
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          "execution_count": 8
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      "cell_type": "code",
      "source": [
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      "execution_count": null,
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          "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": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.TotalCharges = pd.to_numeric(df1['TotalCharges'])\n",
        "df1.dtypes"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LF37eBmhF3TG",
        "outputId": "bab83bcf-4a2e-443b-e10e-6bc2d34fc046"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:5516: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ]
        },
        {
          "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        float64\n",
              "Churn                object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
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        "outputId": "f2550708-eb08-41e0-c37f-5abc653de1af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0   Female              0     Yes         No       1           No   \n",
              "1     Male              0      No         No      34          Yes   \n",
              "2     Male              0      No         No       2          Yes   \n",
              "3     Male              0      No         No      45           No   \n",
              "4   Female              0      No         No       2          Yes   \n",
              "..     ...            ...     ...        ...     ...          ...   \n",
              "95  Female              0      No         No      12          Yes   \n",
              "96    Male              0     Yes        Yes      71          Yes   \n",
              "97    Male              0      No         No       5          Yes   \n",
              "98    Male              0      No         No      52          Yes   \n",
              "99  Female              1     Yes         No      25          Yes   \n",
              "\n",
              "       MultipleLines InternetService       OnlineSecurity  \\\n",
              "0   No phone service             DSL                   No   \n",
              "1                 No             DSL                  Yes   \n",
              "2                 No             DSL                  Yes   \n",
              "3   No phone service             DSL                  Yes   \n",
              "4                 No     Fiber optic                   No   \n",
              "..               ...             ...                  ...   \n",
              "95               Yes     Fiber optic                  Yes   \n",
              "96               Yes             DSL                  Yes   \n",
              "97                No              No  No internet service   \n",
              "98                No              No  No internet service   \n",
              "99                No     Fiber optic                   No   \n",
              "\n",
              "           OnlineBackup     DeviceProtection          TechSupport  \\\n",
              "0                   Yes                   No                   No   \n",
              "1                    No                  Yes                   No   \n",
              "2                   Yes                   No                   No   \n",
              "3                    No                  Yes                  Yes   \n",
              "4                    No                   No                   No   \n",
              "..                  ...                  ...                  ...   \n",
              "95                   No                   No                   No   \n",
              "96                  Yes                   No                  Yes   \n",
              "97  No internet service  No internet service  No internet service   \n",
              "98  No internet service  No internet service  No internet service   \n",
              "99                  Yes                  Yes                   No   \n",
              "\n",
              "            StreamingTV      StreamingMovies        Contract PaperlessBilling  \\\n",
              "0                    No                   No  Month-to-month              Yes   \n",
              "1                    No                   No        One year               No   \n",
              "2                    No                   No  Month-to-month              Yes   \n",
              "3                    No                   No        One year               No   \n",
              "4                    No                   No  Month-to-month              Yes   \n",
              "..                  ...                  ...             ...              ...   \n",
              "95                   No                   No  Month-to-month              Yes   \n",
              "96                   No                   No        One year              Yes   \n",
              "97  No internet service  No internet service  Month-to-month               No   \n",
              "98  No internet service  No internet service        Two year               No   \n",
              "99                  Yes                  Yes  Month-to-month              Yes   \n",
              "\n",
              "                PaymentMethod  MonthlyCharges  TotalCharges Churn  \n",
              "0            Electronic check           29.85         29.85    No  \n",
              "1                Mailed check           56.95       1889.50    No  \n",
              "2                Mailed check           53.85        108.15   Yes  \n",
              "3   Bank transfer (automatic)           42.30       1840.75    No  \n",
              "4            Electronic check           70.70        151.65   Yes  \n",
              "..                        ...             ...           ...   ...  \n",
              "95           Electronic check           78.95        927.35   Yes  \n",
              "96    Credit card (automatic)           66.85       4748.70    No  \n",
              "97               Mailed check           21.05        113.85   Yes  \n",
              "98  Bank transfer (automatic)           21.00       1107.20    No  \n",
              "99           Electronic check           98.50       2514.50   Yes  \n",
              "\n",
              "[100 rows x 20 columns]"
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>12</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>78.95</td>\n",
              "      <td>927.35</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>71</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>One year</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Credit card (automatic)</td>\n",
              "      <td>66.85</td>\n",
              "      <td>4748.70</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>5</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>Month-to-month</td>\n",
              "      <td>No</td>\n",
              "      <td>Mailed check</td>\n",
              "      <td>21.05</td>\n",
              "      <td>113.85</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>52</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>Bank transfer (automatic)</td>\n",
              "      <td>21.00</td>\n",
              "      <td>1107.20</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Female</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>25</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Fiber optic</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Month-to-month</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Electronic check</td>\n",
              "      <td>98.50</td>\n",
              "      <td>2514.50</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d30f1d5d-ad4c-4528-a17c-c17da4100f5a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d30f1d5d-ad4c-4528-a17c-c17da4100f5a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d30f1d5d-ad4c-4528-a17c-c17da4100f5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tenure_churn_no = df1[df1.Churn == 'No'].tenure\n",
        "tenure_churn_yes = df1[df1.Churn == 'Yes'].tenure\n",
        "\n",
        "plt.xlabel('Tenure years')\n",
        "plt.ylabel('No. of Customers')\n",
        "plt.title('Tenure based churn visualisation')\n",
        "\n",
        "plt.hist([tenure_churn_yes, tenure_churn_no] , color = ['green' , 'red'] , label = ['Churn Yes' , 'Churn No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "UkLHlXDdGIl9",
        "outputId": "143bc93e-fc2b-4124-dd07-45bc68f3f317"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  return asarray(a).size\n",
            "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fad3c0831c0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "charges_churn_no = df1[df1.Churn == 'No'].MonthlyCharges\n",
        "charges_churn_yes = df1[df1.Churn == 'Yes'].MonthlyCharges\n",
        "\n",
        "plt.xlabel('Monthly Charges')\n",
        "plt.ylabel('No. of Customers')\n",
        "plt.title('Monthly Charges based churn visualisation')\n",
        "\n",
        "plt.hist([charges_churn_yes, charges_churn_no] , color = ['green' , 'red'] , label = ['Churn Yes' , 'Churn No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "suN80icAIvcu",
        "outputId": "55e81218-495b-4d52-a57f-fe3b1627a9f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fad3bf5a0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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I4O6IeA1A0t1kQWhqqcptZpUvz2Ww7UkSt9xyC6eccgrnnHMOffv2pbq6mgsvvJBFixYVXW/ixImMHz+eH/7wh6s6vNvTsccey/z589lpp52ICAYOHMitt97K9OnTOffcc+nduzfrrLNOu9UwSvpM7xQwbo+I7dP8KGCfiDhZ0nygJiJelXQ78IuIeCDluxf4T7KA0TcifpLSfwj8KyLOa2RfE8hqJ2y66aY7L1jQ5HNA2pcvqzUrqaeffpptttmmo4vRpTT2nkqaERE1xdYp22W1ktYCvg/8qBTbj4jJEVETETUDBxZ9wqCZmbVSOe/D2AIYAjyRahdVwGOSNgQWAZsU5K1KacXSzcyszMoWMCLiyYj4dERUR0Q1UAfsFBFLgGnAUelqqd2A5RGxGLgTGClp/dTZPTKlmZlZmZXystqpwD+ArSTVSRrfRPY7gBeBucDvgG8CpM7u/wIeTa8f13eAm5lZeZXyKqkjmlleXTAdwIlF8l0BXNGuhTMzsxbzWFJmZpaLA4aZdT5S+75yWLJkCaNHj2aLLbZg55135sADD+S5555j+vTpHHzwwSU+4Ex1dTVf+cpXVs3feOONHH300WXZNzhgmJk1q3548xEjRvDCCy8wY8YMfv7zn7fb8OYtMWPGDObMmdPm/baGA4aZWTMqYXjzeqeeeio//elPV0t/7bXXOPTQQxk2bBi77bYbs2bNaq/DX8UBw8ysGXmGN7/wwguZM2cOL774Ig8++GCz25wzZw733HMPU6dmIx0988wz3HnnnTzyyCOcffbZq8aHaujwww/nscceY+7cuZ9InzhxIjvuuCOzZs3iZz/7GUcddVQLjjAfBwwzszaqH968R48eq4Y3b06x4c0HDBiwanjzxvTs2ZPTTjuNn//8559If+CBBxg7diyQDZa4bNky3nzzzdYfVCMcMMzMmlFJw5sDjB07lvvvv5+FCxfmP4h24IBhZtaMShvevHfv3nznO9/hggsuWJW21157ce211wIwffp0BgwYwKc+9al22yc4YJhZZxTRvq9m1A9vfs8997DFFluw3XbbceaZZ7Lhhhs2ud7EiRM5+eSTqampoWfPnu119ACMHz/+E7WQSZMmMWPGDIYNG8YZZ5zBlClT2nV/UOLhzTtKTU1N1NbWlm+HHt7crKQ8vHn7q+jhzc3MrHNzwDAzs1wcMMysU+iKzecdpbXvpQOGmVW8vn37smzZMgeNdhARLFu2jL59+7Z43ZINb25m1l6qqqqoq6tj6dKlHV2ULqFv375UVVW1eD0HDDOreL1792bIkCEdXYxuz01SZmaWiwOGmZnl4oBhZma5lCxgSLpC0iuSZheknSvpGUmzJN0iqV/BsjMlzZX0rKT9CtL3T2lzJZ1RqvKamVnTSlnDuBLYv0Ha3cD2ETEMeA44E0DStsBoYLu0zqWSekrqCVwCHABsCxyR8pqZWZmVLGBExP3Aaw3S7oqI+tGyHgLqr+saBVwXEe9HxDxgLrBres2NiBcj4gPgupTXzMzKrCP7MI4B/pKmBwOFA7vXpbRi6WZmVmYdEjAknQWsBK5tx21OkFQrqdY395iZtb+yBwxJRwMHA2Pi4/v8FwGbFGSrSmnF0lcTEZMjoiYiagYOHNju5TYz6+7KGjAk7Q+cDhwSEe8WLJoGjJbUR9IQYCjwCPAoMFTSEElrkHWMTytnmc3MLFOyoUEkTQVGAAMk1QETya6K6gPcreyhQw9FxPER8ZSkG4A5ZE1VJ0bEh2k73wLuBHoCV0TEU6Uqs5mZFecn7rUHP3HPzLoAP3HPzMzahQOGmZnl4oBhZma5OGCYmVkuDhhmZpaLA4aZmeXigGFmZrk4YJiZWS4OGGZmlosDhpmZ5eKAYWZmuThgmJlZLg4YZmaWiwOGmZnl4oBhZma5OGCYmVkuzQYMSVtI6pOmR0g6SVK/0hfNzMwqSZ4axk3Ah5K2BCYDmwB/LGmpzMys4uQJGB9FxErgS8CvIuI0YKPSFsvMzCpNnoCxQtIRwDjg9pTWu3RFMjOzSpQnYHwd2B34aUTMkzQEuLq5lSRdIekVSbML0jaQdLek59Pf9VO6JF0saa6kWZJ2KlhnXMr/vKRxLT9EMzNrD00GDEk9gbMi4qSImAoQEfMi4pwc274S2L9B2hnAvRExFLg3zQMcAAxNrwnAZWn/GwATgc8CuwIT64OMmZmVV5MBIyI+BDaTtEZLNxwR9wOvNUgeBUxJ01OAQwvSr4rMQ0A/SRsB+wF3R8RrEfE6cDerByEzMyuDXjnyvAg8KGka8E59YkSc34r9DYqIxWl6CTAoTQ8GFhbkq0tpxdJXI2kCWe2ETTfdtBVFMzOzpuTpw3iBrLO7B7BuwatNIiKAaOt2CrY3OSJqIqJm4MCB7bVZMzNLmq1hRMTZAJLWioh327i/lyVtFBGLU5PTKyl9Edn9HfWqUtoiYESD9OltLIOZmbVCnju9d5c0B3gmze8g6dJW7m8a2eW5pL+3FaQfla6W2g1Ynpqu7gRGSlo/dXaPTGlmZlZmefowLiTrfJ4GEBFPSNq7uZUkTSWrHQyQVEd2tdMvgBskjQcWAIen7HcABwJzgXfJLuUlIl6T9F/AoynfjyOiYUe6mZmVQZ6AQUQslFSY9GGOdY4osmjfRvIGcGKR7VwBXJGjmGZmVkJ5AsZCSf8OhKTewMnA06UtlpmZVZo8V0kdT/brfzBZJ/RwitQGzMys68pzldSrwJgylMXMzCpYswEjjR31baC6MH9EHFK6YpmZWaXJ04dxK3A58N/AR6UtjpmZVao8AeO9iLi45CUxM7OKlidgXCRpInAX8H59YkQ8VrJSmZlZxckTMD4DjAX24eMmqUjzZmbWTeQJGF8FNo+ID0pdGDMzq1x57sOYDfQrdUHMzKyy5alh9AOekfQon+zD8GW1ZmbdSJ6AMbHkpTAzs4qX507v/5E0CNglJT0SEa80tY6ZmXU9eZ6HcTjwCFnn9+HAw5IOK3XBzMyssuRpkjoL2KW+ViFpIHAPcGMpC2ZmZpUlz1VSPRo0QS3LuZ6ZmXUheWoYf5V0JzA1zX8N+EvpimRmZpUoT6f3aZK+DOyZkiZHxC2lLZaZmVWaPMObnxMR/wnc3EiamZl1E3n6Ir7QSNoB7V0QMzOrbEUDhqQTJD0JbCVpVsFrHjCrLTuV9B1JT0maLWmqpL6Shkh6WNJcSddLWiPl7ZPm56bl1W3Zt5mZtU5TNYw/Al8EpqW/9a+dI+LI1u5Q0mDgJKAmIrYHegKjgXOACyJiS+B1YHxaZTzwekq/IOUzM7MyKxowImJ5RMwHfgAsiYgFwBDgSEltHYywF7CmpF7AWsBisuHS6+/tmAIcmqZHpXnS8n0lqY37NzOzFsrTh3ET8KGkLYHJwCZktY9WiYhFwHnAP8kCxXJgBvBGRKxM2eqAwWl6MLAwrbsy5e/fcLuSJkiqlVS7dOnS1hbPzMyKyBMwPkpf1F8GfhURpwEbtXaHktYnqzUMATYG1gb2b+326kXE5IioiYiagQMHtnVzZmbWQJ6AsULSEcBRwO0prXcb9vl5YF5ELI2IFWSX6+4B9EtNVABVwKI0vYisVkNavh7Z3eZmZlZGeQLG14HdgZ9GxDxJQ4Cr27DPfwK7SVor9UXsC8wB7gPqBzUcB9yWpqeledLyv0VEtGH/ZmbWCuqI715JZ5MNMbISeBw4lqyv4jpgg5R2ZES8L6kvWYDaEXgNGB0RLza1/ZqamqitrS3hETTQmj54xzwzqzCSZkRETdHlzQWMdN/FapkiYvO2F680HDDMzFquuYCRZ/DBwpX7kj0XY4O2FszMzDqXZvswImJZwWtRRFwIHFSGspmZWQXJM/jgTgWzPchqHHlqJmZm1oXk+eL/ZcH0SmA+2aNazcysG8nzPIz/V46CmJlZZWtqtNrvShrfSPp4SaeUtlhmZlZpmur0HgNc1Uj61cAxpSmOmZlVqqYCRq80dMcnRMQHgEeLNTPrZpoKGD0kDWqY2FiamZl1fU0FjHOBP0v6nKR102sE2QCE55WldGZmVjGKXiUVEVdJWgr8GNiebHiQp4AfRcRfylQ+MzOrEE1eVpsCg4ODmZnlGt7czMzMAcPMzPJp6sa9k9PfPcpXHDMzq1RN1TC+nv7+qhwFMTOzytZUp/fTkp4HNpY0qyBdQETEsNIWzczMKklTl9UeIWlD4E7gkPIVyczMKlFzl9UuAXaQtAbwbyn52caGDDEzs66t2aukJH0OeB64BLgUeE7S3m3ZqaR+km6U9IykpyXtLmkDSXdLej79XT/llaSLJc2VNKvBA53MzKxM8lxWez4wMiI+FxF7A/sBF7RxvxcBf42IrYEdgKeBM4B7I2IocG+aBzgAGJpeE4DL2rhvMzNrhTwBo3dEPFs/ExHPAb1bu0NJ6wF7A5en7X0QEW8Ao4ApKdsU4NA0PQq4KjIPAf0kbdTa/ZuZWevkCRi1kn4vaUR6/Q6obcM+hwBLgT9Iejxte21gUEQsTnmWAPWj4g4GFhasX5fSPkHSBEm1kmqXLl3ahuKZmVlj8gSME4A5wEnpNSeltVYvYCfgsojYEXiHj5ufgOyaXbLBDnOLiMkRURMRNQMHDmxD8cysUVLLX9al5Hmm9/tk/Rjnt9M+64C6iHg4zd9IFjBelrRRRCxOTU6vpOWLgE0K1q9KaWZmVkZlH0sqXaq7UNJWKWlfslrLNGBcShsH3JampwFHpauldgOWFzRdmZlZmTRbwyiRbwPXpvs7XiQbhqQHcIOk8cAC4PCU9w7gQGAu8C4fD1liZmZl1CEBIyJmAjWNLNq3kbwBnFjyQpmZWZNa1SQlaUJ7F8TMzCpba2sYvvzBzMqnNVdcRYsutLQcWhUwIuK37V0QM8vBX5zWgfKMJVUl6RZJSyW9IukmSVXlKJyZmVWOPH0YfyC7tHUjYGPgv1OamZl1I3kCxsCI+ENErEyvKwHfSm1m1s3kCRjLJB0pqWd6HQksK3XBzMyssuQJGMeQ3US3BFgMHIZvnjMz63byjCW1AD+i1cys2ysaMCT9qIn1IiL+qwTlMTOzCtVUDeOdRtLWBsYD/QEHDDOzbqRowIiIX9ZPS1oXOJms7+I64JfF1jMrC9/AZuXU0s9bF/2sNdmHIWkD4LvAGLLHpu4UEa+Xo2BmZlZZmurDOBf4MjAZ+ExEvF22UpmZWcVp6rLaU8nu7P4B8JKkN9PrLUlvlqd4ZmZWKZrqwyj70/jMzKxyOSiYmVkuDhhmZpaLA4aZmeXigGFmZrl0WMBII98+Lun2ND9E0sOS5kq6XtIaKb1Pmp+blld3VJnNgOwmrpa+zLqAjqxhnAw8XTB/DnBBRGwJvE42BAnp7+sp/YKUz8zMyqxDAkZ6xOtBwO/TvIB9gBtTlinAoWl6VJonLd835TczszLqqBrGhcDpwEdpvj/wRkSsTPN1wOA0PRhYCJCWL0/5P0HSBEm1kmqXLl1ayrKbmXVLZQ8Ykg4GXomIGe253YiYHBE1EVEzcKCfIGtm1t6afYBSCewBHCLpQKAv8CngIqCfpF6pFlEFLEr5FwGbAHWSegHr4UfEmpmVXdlrGBFxZkRURUQ1MBr4W0SMAe4je/wrwDjgtjQ9Lc2Tlv8toouOHWxmVsEq6T6M/wS+K2kuWR/F5Sn9cqB/Sv8ucEYHlc/MrFvriCapVSJiOjA9Tb8I7NpInveAr5a1YGZmtpoODRhm1nF0dsuuTnc7sDlgmHUgf2lbZ1JJfRhmZlbBHDDMzCwXBwwzM8vFfRhmVnbuu+mcXMMwM7NcHDDMzCwXBwwzM8vFAcPMzHJxwDAzs1wcMMzMLBdfVmtm3UpLL+kFX9ZbzzUMMzPLxQHDzMxycZOUmVklUwub0Er4QFIHDGu9ln6QoaQfZjMrLTdJmZlZLg4YZmaWS9kDhqRNJN0naY6kpySdnNI3kHS3pOfT3/VTuiRdLGmupFmSdip3mc3MrGNqGCuBUyNiW2A34ERJ2wJnAPdGxFDg3jQPcAAwNL0mAJeVv8hmZlb2gBERiyPisTT9FvA0MBgYBUxJ2aYAh6bpUcBVkXkI6CdpozIX28ys2+vQPgxJ1cCOwMPAoIhYnBYtAQal6cHAwoLV6lJaw+THDMoAAAm6SURBVG1NkFQrqXbp0qUlK7OZWXfVYQFD0jrATcApEfFm4bKICFp4N35ETI6ImoioGThwYDuW1MzMoIMChqTeZMHi2oi4OSW/XN/UlP6+ktIXAZsUrF6V0szMrIw64iopAZcDT0fE+QWLpgHj0vQ44LaC9KPS1VK7AcsLmq7MzKxMOuJO7z2AscCTkmamtO8DvwBukDQeWAAcnpbdARwIzAXeBb5e3uKamRl0QMCIiAeAYmNK7NtI/gBOLGmhzMysWb7T28zMcvHgg1YRWvpQGw9haFZ+DhiN8JeXmdnq3CRlZma5uIbR2fmZFGZWJq5hmJlZLq5h2CruuzGzpriGYWZmuThgmJlZLg4YZmaWiwOGmZnl4oBhZma5OGCYmVkuDhhmZpaLA4aZmeXiG/cqjG+eKz+/52b5uIZhZma5uIZhZlYmLa3NQmXVaF3DMDOzXBwwzMwsl04TMCTtL+lZSXMlndHR5TEz6246RcCQ1BO4BDgA2BY4QtK2HVsqM7PupVMEDGBXYG5EvBgRHwDXAaM6uExmZt1KZ7lKajCwsGC+DvhsYQZJE4AJafZtSc+2w34HAK82l6kVD0lt3aNVy7dvH3f5951vM+2/71zHXKJ9599M++8793G3av/tdNwl2Hdzx71ZUyt3loDRrIiYDExuz21Kqo2ImvbcZmfg4+4+uuMxg4+7tet3liapRcAmBfNVKc3MzMqkswSMR4GhkoZIWgMYDUzr4DKZmXUrnaJJKiJWSvoWcCfQE7giIp4qw67btYmrE/Fxdx/d8ZjBx90qiqikG8/NzKxSdZYmKTMz62AOGGZmlosDBiBpE0n3SZoj6SlJJ6f0DSTdLen59Hf9ji5rKUjqKelxSben+SGSHk7DsFyfLjToUiT1k3SjpGckPS1p9+5wviV9J33GZ0uaKqlvVzzfkq6Q9Iqk2QVpjZ5fZS5Oxz9L0k4dV/K2KXLc56bP+SxJt0jqV7DszHTcz0rar7ntO2BkVgKnRsS2wG7AiWnokTOAeyNiKHBvmu+KTgaeLpg/B7ggIrYEXgfGd0ipSusi4K8RsTWwA9nxd+nzLWkwcBJQExHbk11AMpqueb6vBPZvkFbs/B4ADE2vCcBlZSpjKVzJ6sd9N7B9RAwDngPOBEjfcaOB7dI6l6ZhmIpywAAiYnFEPJam3yL78hhMNvzIlJRtCnBox5SwdCRVAQcBv0/zAvYBbkxZutxxS1oP2Bu4HCAiPoiIN+gG55vsysg1JfUC1gIW0wXPd0TcD7zWILnY+R0FXBWZh4B+kjYqT0nbV2PHHRF3RcTKNPsQ2X1skB33dRHxfkTMA+aSDcNUlANGA5KqgR2Bh4FBEbE4LVoCDOqgYpXShcDpwEdpvj/wRsEHrI4seHYlQ4ClwB9SU9zvJa1NFz/fEbEIOA/4J1mgWA7MoOuf73rFzm9jQw911ffgGOAvabrFx+2AUUDSOsBNwCkR8WbhssiuP+5S1yBLOhh4JSJmdHRZyqwXsBNwWUTsCLxDg+anLnq+1yf7VTkE2BhYm9WbL7qFrnh+myPpLLLm92tbuw0HjERSb7JgcW1E3JySX66vmqa/r3RU+UpkD+AQSfPJRgDeh6xtv19qsoCuOQxLHVAXEQ+n+RvJAkhXP9+fB+ZFxNKIWAHcTPYZ6Ornu16x89vlhx6SdDRwMDAmPr75rsXH7YDBqnb7y4GnI+L8gkXTgHFpehxwW7nLVkoRcWZEVEVENVnn198iYgxwH3BYytYVj3sJsFDSVilpX2AOXfx8kzVF7SZprfSZrz/uLn2+CxQ7v9OAo9LVUrsBywuarjo9SfuTNTsfEhHvFiyaBoyW1EfSELJO/0ea3FhEdPsXsCdZ9XQWMDO9DiRrz78XeB64B9igo8tawvdgBHB7mt48fXDmAn8C+nR0+UpwvMOB2nTObwXW7w7nGzgbeAaYDVwN9OmK5xuYStZPs4KsRjm+2PklG0H8EuAF4Emyq8g6/Bja8bjnkvVV1H+3/aYg/1npuJ8FDmhu+x4axMzMcnGTlJmZ5eKAYWZmuThgmJlZLg4YZmaWiwOGmZnl4oBhXYqkkHRNwXwvSUvrR+Jtxfb6SfpmwfyIYtuSNF1STQu2vY6k30p6QdKMtP5nJVUXjjZqVikcMKyreQfYXtKaaf4LtO2u3X7AN5vN1Tq/JxsobmhE7Ax8HRjQ1o0W3LVt1q4cMKwruoNsBF6AI8huZgJWPRPh1vRsgIckDUvpk9KzBKZLelHSSWmVXwBbSJop6dyUtk7BszSuTXdNU7CPYyRdWDB/nKQLGuTZAvgs8IOI+AggIuZFxJ9Tlp6SfpeeXXFXfQBM23pU0hOSbpK0Vkq/UtJvJD0M/H9JW6Tje1LSTyS9XbDv09I2Zkk6O6WtLenPabuzJX2ttW++dV0OGNYVXUc25EFfYBjZyMP1zgYej+zZAN8HripYtjWwH9kQzxPT+GJnAC9ExPCIOC3l2xE4BdiW7C7pPRrs/wbgi2l9yGoOVzTIsx0wMyI+LHIMQ4FLImI74A3gKyn95ojYJSLqn+FR+OyKKuDfI+K7ZGOCXRQRnyG74xcASSPTtnclu9t9Z0l7kw1C+FJE7BDZszL+WqRc1o05YFiXExGzgGqy2sUdDRbvSTYkBhHxN6C/pE+lZX+O7NkAr5INTFdsePNHIqIu1Qxmpn0V7v9t4G/AwZK2BnpHxJMtPIx5ETEzTc8o2Mf2kv4u6UlgDFngqfenggC0O9kwHwB/LMgzMr0eBx4jC5JDyYbE+IKkcyTtFRHLW1he6wbc1mld1TSyZz+MIBtDKI/3C6Y/pPj/R558vyerwTwD/KGR5U8BO0jqWaSW0XAf9X0yVwKHRsQTaQTSEQX53ilS3kICfh4Rv11tQfZo0gOBn0i6NyJ+nGN71o24hmFd1RXA2Y38sv872S9zJI0AXo0Gzz5p4C1g3ZbuPLKh0zcB/oOCPpSC5S+QDX54dn0fSLo66qCGeRtYF1icmrvGNJHvIT5uxhpdkH4ncEx69guSBkv6tKSNgXcj4hrgXLLh3s0+wTUM65Iiog64uJFFk4ArJM0C3uXj4a6LbWeZpAfTZa5/Af7cVP4GbgCGR8TrRZYfC/wSmCvpX8CrwGlF8tb7IVmfzNL0t1gwOwW4Jj00569kT9cjIu6StA3wjxSn3gaOBLYEzpX0EdlIpyfkOkLrVjxarVmJpPs1LoiIeztg32sB/4qIkDQaOCIiRpW7HNa1uIZh1s4k9SN7vsQTHREskp2BX6fmrjfInuVs1iauYZiZWS7u9DYzs1wcMMzMLBcHDDMzy8UBw8zMcnHAMDOzXP4PXDaLp3wjnAIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "charges_churn_no = df1[df1.Churn == 'No'].TotalCharges\n",
        "charges_churn_yes = df1[df1.Churn == 'Yes'].TotalCharges\n",
        "\n",
        "plt.xlabel('Total Charges')\n",
        "plt.ylabel('No. of Customers')\n",
        "plt.title('Total Charges based churn visualisation')\n",
        "\n",
        "plt.hist([charges_churn_yes, charges_churn_no] , color = ['green' , 'red'] , label = ['Churn Yes' , 'Churn No'])\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "_5oF1nEQNyDT",
        "outputId": "9b967e47-a302-4393-f7a2-e3424ebaa9fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fad3ba29220>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_unique_col_values(df):\n",
        "  for col in df:\n",
        "    if df[col].dtypes == 'object':\n",
        "      print(f\"{col} : {df[col].unique()}\")\n",
        "\n",
        "print_unique_col_values(df1)      "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIIhWR8KOcBI",
        "outputId": "d62e9b5a-4abc-469e-d8fd-a5ef9177a7cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : ['Female' 'Male']\n",
            "Partner : ['Yes' 'No']\n",
            "Dependents : ['No' 'Yes']\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",
            "Churn : ['No' 'Yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.replace('No phone service' , 'No' , inplace = True)\n",
        "df1.replace('No internet service' , 'No' , inplace = True)"
      ],
      "metadata": {
        "id": "AvQViFfsa7mD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acccd7a9-1114-4ea8-a1a4-0f00a9a18702"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5238: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return super().replace(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print_unique_col_values(df1)      "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kR6w7eHby-0",
        "outputId": "25474065-9988-43e0-db60-92a4d3bd8a9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : ['Female' 'Male']\n",
            "Partner : ['Yes' 'No']\n",
            "Dependents : ['No' 'Yes']\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",
            "Churn : ['No' 'Yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yes_no_column = ['PaperlessBilling', 'StreamingMovies' , 'TechSupport' , 'DeviceProtection' , 'OnlineSecurity', 'OnlineBackup', 'InternetService' , 'MultipleLines' ,'PhoneService', 'Dependents' , 'Partner']\n",
        "for col in yes_no_column:\n",
        "  df1.replace({'Yes' : 1 , 'No' : 0}, inplace = True)\n",
        "\n",
        "print_unique_col_values(df1)      "
      ],
      "metadata": {
        "id": "TTd_h8Rbb-o2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3fbf6ea-37d3-4def-fcb3-849019b80b5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : ['Female' 'Male']\n",
            "InternetService : ['DSL' 'Fiber optic' 0]\n",
            "Contract : ['Month-to-month' 'One year' 'Two year']\n",
            "PaymentMethod : ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
            " 'Credit card (automatic)']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_unique_col_values_all(df):\n",
        "  for col in df:\n",
        "      print(f\"{col} : {df[col].unique()}\")\n",
        "\n",
        "print_unique_col_values_all(df1)           "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xILIbWXCZsWJ",
        "outputId": "82e1acfb-309a-4505-d4d2-2490037d6ed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['gender'].replace({'Female' : 1 , 'Male' : 0}, inplace = True) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYbBiEGPat3h",
        "outputId": "3d83173e-55c4-4856-a7e9-44d1695e2110"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:6619: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return self._update_inplace(result)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print_unique_col_values_all(df1)           "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdszYIbzbKep",
        "outputId": "af5dac67-259d-4b77-b956-a0bd9a832d8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : [1 0]\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['InternetService'].replace({0 : 'No'}, inplace = True) "
      ],
      "metadata": {
        "id": "UmKw_bK7bNCS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_unique_col_values_all(df1)           "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cEND9m4Tbs4g",
        "outputId": "e0600c25-fa41-4c0e-9136-7eaad80bf210"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gender : [1 0]\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' 'No']\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.get_dummies(data = df1 , columns = ['InternetService', 'Contract', 'PaymentMethod'])\n",
        "df2.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e64SL_8Wbu3z",
        "outputId": "4ddf701f-e706-4457-9733-1f17d2a012a1"
      },
      "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_DSL', 'InternetService_Fiber optic',\n",
              "       'InternetService_No', 'Contract_Month-to-month', 'Contract_One year',\n",
              "       'Contract_Two year', 'PaymentMethod_Bank transfer (automatic)',\n",
              "       'PaymentMethod_Credit card (automatic)',\n",
              "       'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JCIVLIdclYD",
        "outputId": "3fdaf8c6-36ae-4ef1-b245-9ffee6583233"
      },
      "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                               float64\n",
              "Churn                                        int64\n",
              "InternetService_DSL                          uint8\n",
              "InternetService_Fiber optic                  uint8\n",
              "InternetService_No                           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": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "need_scale = ['tenure', 'MonthlyCharges' , 'TotalCharges']\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "df2[need_scale] = scaler.fit_transform(df2[need_scale])"
      ],
      "metadata": {
        "id": "d0j8gsbCdD28"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Cp3ebwbzeXDs",
        "outputId": "86845b69-d93b-4251-cd38-4d2c8639a232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      gender  SeniorCitizen  Partner  Dependents    tenure  PhoneService  \\\n",
              "1803       1              0        0           0  0.492958             0   \n",
              "4514       1              0        0           0  0.380282             1   \n",
              "1678       1              1        1           0  0.929577             1   \n",
              "7006       0              0        0           0  0.549296             1   \n",
              "6417       1              1        1           0  0.704225             1   \n",
              "\n",
              "      MultipleLines  OnlineSecurity  OnlineBackup  DeviceProtection  ...  \\\n",
              "1803              0               1             1                 1  ...   \n",
              "4514              0               0             0                 0  ...   \n",
              "1678              1               1             1                 1  ...   \n",
              "7006              1               0             1                 1  ...   \n",
              "6417              1               1             0                 0  ...   \n",
              "\n",
              "      InternetService_DSL  InternetService_Fiber optic  InternetService_No  \\\n",
              "1803                    1                            0                   0   \n",
              "4514                    0                            1                   0   \n",
              "1678                    0                            1                   0   \n",
              "7006                    0                            1                   0   \n",
              "6417                    0                            1                   0   \n",
              "\n",
              "      Contract_Month-to-month  Contract_One year  Contract_Two year  \\\n",
              "1803                        0                  0                  1   \n",
              "4514                        1                  0                  0   \n",
              "1678                        0                  1                  0   \n",
              "7006                        1                  0                  0   \n",
              "6417                        1                  0                  0   \n",
              "\n",
              "      PaymentMethod_Bank transfer (automatic)  \\\n",
              "1803                                        0   \n",
              "4514                                        0   \n",
              "1678                                        0   \n",
              "7006                                        0   \n",
              "6417                                        0   \n",
              "\n",
              "      PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
              "1803                                      0                               0   \n",
              "4514                                      0                               1   \n",
              "1678                                      1                               0   \n",
              "7006                                      1                               0   \n",
              "6417                                      0                               1   \n",
              "\n",
              "      PaymentMethod_Mailed check  \n",
              "1803                           1  \n",
              "4514                           0  \n",
              "1678                           0  \n",
              "7006                           0  \n",
              "6417                           0  \n",
              "\n",
              "[5 rows x 27 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb117dca-2b09-4c23-a1a6-27813f53682a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        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>...</th>\n",
              "      <th>InternetService_DSL</th>\n",
              "      <th>InternetService_Fiber optic</th>\n",
              "      <th>InternetService_No</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>1803</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.492958</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4514</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.380282</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>1</td>\n",
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              "      <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>1678</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.929577</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7006</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.549296</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6417</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.704225</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb117dca-2b09-4c23-a1a6-27813f53682a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bb117dca-2b09-4c23-a1a6-27813f53682a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bb117dca-2b09-4c23-a1a6-27813f53682a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df2.drop('Churn' , axis = 1)\n",
        "y = df2['Churn']"
      ],
      "metadata": {
        "id": "anA1iYfZeafz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train , X_test , y_train , y_test = train_test_split(X , y , test_size= 0.2 , random_state = 5)"
      ],
      "metadata": {
        "id": "HV1RZKLHfInz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MO9tnxlEfn8u",
        "outputId": "33db4756-0e6b-4dfa-d93e-0fdeef2c6c5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5625, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model\n",
        "\n",
        " "
      ],
      "metadata": {
        "id": "qVg6Fnkskaok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(20, input_shape = (26,) ,activation = 'relu'),\n",
        "    keras.layers.Dense(15, input_shape = (26,) ,activation = 'relu'),\n",
        "    keras.layers.Dense(10, input_shape = (26,) ,activation = 'relu'),\n",
        "    keras.layers.Dense(1, activation = 'sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss = 'binary_crossentropy' ,\n",
        "    metrics = ['accuracy']\n",
        "    )\n",
        "\n",
        "model.fit(X_train , y_train , epochs = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4nbbMgQxft9T",
        "outputId": "958fcce1-2285-4253-e8c6-0e126a0b6afd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "176/176 [==============================] - 1s 2ms/step - loss: 0.4894 - accuracy: 0.7595\n",
            "Epoch 2/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4271 - accuracy: 0.7931\n",
            "Epoch 3/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4181 - accuracy: 0.8011\n",
            "Epoch 4/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4141 - accuracy: 0.8053\n",
            "Epoch 5/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4117 - accuracy: 0.8050\n",
            "Epoch 6/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4097 - accuracy: 0.8071\n",
            "Epoch 7/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4069 - accuracy: 0.8107\n",
            "Epoch 8/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4055 - accuracy: 0.8108\n",
            "Epoch 9/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4044 - accuracy: 0.8128\n",
            "Epoch 10/10\n",
            "176/176 [==============================] - 0s 2ms/step - loss: 0.4029 - accuracy: 0.8103\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fad387086a0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwKOewW1k0nj",
        "outputId": "b3c69735-e849-4ebc-911e-2f82fd407e01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 0s 1ms/step - loss: 0.4401 - accuracy: 0.7854\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.44006869196891785, 0.7853589057922363]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yp = model.predict(X_test)\n",
        "yp[:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOKY27XbQILH",
        "outputId": "faeda28a-68ea-4f3f-a849-78fce452776c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "44/44 [==============================] - 0s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.23809974],\n",
              "       [0.4713518 ],\n",
              "       [0.01455446],\n",
              "       [0.68496186],\n",
              "       [0.372089  ],\n",
              "       [0.70534235],\n",
              "       [0.07536376],\n",
              "       [0.64801735],\n",
              "       [0.40212986],\n",
              "       [0.11911481],\n",
              "       [0.24076739],\n",
              "       [0.6307021 ],\n",
              "       [0.16308755],\n",
              "       [0.68661714],\n",
              "       [0.77019787],\n",
              "       [0.22727358],\n",
              "       [0.05556092],\n",
              "       [0.45658904],\n",
              "       [0.00454665],\n",
              "       [0.43128687]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = []\n",
        "for i in yp:\n",
        "  if i > 0.5:\n",
        "    y_pred.append(1)\n",
        "  else:\n",
        "    y_pred.append(0)  "
      ],
      "metadata": {
        "id": "PZHNC1VTQ5EZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lld52z4KS-SW",
        "outputId": "8b0d645b-04ee-4ddc-d281-363a3a9a4068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    999\n",
              "1    408\n",
              "Name: Churn, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xlel4tf5SIPi",
        "outputId": "800d69d5-349e-4302-ab9e-0e4b30ff8a08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 0, 1, 0, 1, 0, 1, 0, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q5Pp1wCCRgfx",
        "outputId": "28806303-b1b0-40c6-cddc-cffffce44792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.88      0.85       999\n",
            "           1       0.65      0.55      0.60       408\n",
            "\n",
            "    accuracy                           0.79      1407\n",
            "   macro avg       0.74      0.72      0.73      1407\n",
            "weighted avg       0.78      0.79      0.78      1407\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "cm= tf.math.confusion_matrix(labels = y_test , predictions= y_pred)\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm , annot = True , fmt = 'd')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "EAKI6VjMSH8L",
        "outputId": "ec8629f6-5c75-470a-c3de-98721aa2568d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}