Bank_Customer_Churn_Prediction.ipynb

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    {
      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/Shilpa-shilu-03/Bank_Customer_Churn/blob/main/Bank_Customer_Churn_Prediction.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",
      "source": [
        "# **Bank Customers Churn Prediction**"
      ],
      "metadata": {
        "id": "grn9FQskK-sl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Aim** - Predict the Customer Churn for a Bank."
      ],
      "metadata": {
        "id": "MTbhYBa0FVwv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **About Dataset**"
      ],
      "metadata": {
        "id": "fIwilBXjUsKL"
      }
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    {
      "cell_type": "markdown",
      "source": [
        " Customer churn refers to the phenomenon where customers discontinue their relationship with a company or stop using its products or services. Understanding the factors that contribute to churn is crucial for businesses to retain customers and maintain long-term profitability.\n",
        "\n",
        "Getting a new client costs substantially more than keeping an old one.Knowing the factors that influence a customer's choice to quit a firm is useful for banks.Churn prevention enables businesses to create loyalty programs and retention efforts to retain as many customers as feasible.\n",
        "\n",
        " The dataset used in this analysis contains information about bank customers, including their demographic details, credit score, account balance, tenure, and various other features. The target variable is \"Exited,\" which indicates whether a customer has churned (1) or not (0)."
      ],
      "metadata": {
        "id": "DeGnUzDnU5W_"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "This dataset contains the data of a bank with following columns:\n",
        "\n",
        "\n",
        "1. **RowNumber** : Corresponds to the row number and has no effect on the output (unused variable).\n",
        "2. **CustomerId** : An identifier for each customer and has no effect on them leaving the bank (unused variable).\n",
        "3. **Surname** : Surname of the customer has no impact on their decision to leave the bank (unused variable).\n",
        "4. **CreditScore** : Credit Score of the customer (used as input).\n",
        "5. **Geography** : The geographic location of the customer (used as input).\n",
        "6. **Gender** : The gender of the customer (used as input).\n",
        "7. **Age** : The age of the customer (used as input).\n",
        "8. **Tenure** : Time period the customer has been a client of the bank (used as input).\n",
        "9. **Balance** : The account balance of the customer (used as input).\n",
        "10. **NumOfProducts** : Number of products that a customer has purchased through the bank like loans, credit card, debit card,... (used as input).\n",
        "11. **HasCrCard** : Whether the customer has a credit card or not (used as input).\n",
        "12. **IsActiveMember** : Whether the customer is an active member (used as input).\n",
        "13. **EstimatedSalary** : Salary of the customer (used as input).\n",
        "14. **Complain** : Whether the customer has complaint or not (used as input).\n",
        "15. **Satisfaction Score** : Score provided by the customer for their complaint resolution (used as input).\n",
        "16. **Card Type** : Type of card hold by the customer (used as input).\n",
        "17. **Point Earned** : The points earned by the customer for using credit card (used as input).\n",
        "18. **Exited** : used as the target. 1 if the client has left the bank during some period or 0 if he/she has not."
      ],
      "metadata": {
        "id": "XSOJsxBR5qge"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "#1. Importing required libraries"
      ],
      "metadata": {
        "id": "zMJ0FJfsStQG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4iWhdDoTJRUS"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split,GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.metrics import accuracy_score,classification_report,ConfusionMatrixDisplay\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.feature_selection import SelectKBest,chi2"
      ],
      "metadata": {
        "id": "QFDOPLjPKu9U"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "#2. Loading Dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "hKdxQvtRLKA3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/Customer-Churn-Records.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "uYVPfdBwJdaV",
        "outputId": "8fae0b3a-27d5-487a-fa47-b2966a7f6668"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
              "0             1    15634602   Hargrave          619    France  Female   42   \n",
              "1             2    15647311       Hill          608     Spain  Female   41   \n",
              "2             3    15619304       Onio          502    France  Female   42   \n",
              "3             4    15701354       Boni          699    France  Female   39   \n",
              "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
              "...         ...         ...        ...          ...       ...     ...  ...   \n",
              "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
              "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
              "9997       9998    15584532        Liu          709    France  Female   36   \n",
              "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
              "9999      10000    15628319     Walker          792    France  Female   28   \n",
              "\n",
              "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0          2       0.00              1          1               1   \n",
              "1          1   83807.86              1          0               1   \n",
              "2          8  159660.80              3          1               0   \n",
              "3          1       0.00              2          0               0   \n",
              "4          2  125510.82              1          1               1   \n",
              "...      ...        ...            ...        ...             ...   \n",
              "9995       5       0.00              2          1               0   \n",
              "9996      10   57369.61              1          1               1   \n",
              "9997       7       0.00              1          0               1   \n",
              "9998       3   75075.31              2          1               0   \n",
              "9999       4  130142.79              1          1               0   \n",
              "\n",
              "      EstimatedSalary  Exited  Complain  Satisfaction Score Card Type  \\\n",
              "0           101348.88       1         1                   2   DIAMOND   \n",
              "1           112542.58       0         1                   3   DIAMOND   \n",
              "2           113931.57       1         1                   3   DIAMOND   \n",
              "3            93826.63       0         0                   5      GOLD   \n",
              "4            79084.10       0         0                   5      GOLD   \n",
              "...               ...     ...       ...                 ...       ...   \n",
              "9995         96270.64       0         0                   1   DIAMOND   \n",
              "9996        101699.77       0         0                   5  PLATINUM   \n",
              "9997         42085.58       1         1                   3    SILVER   \n",
              "9998         92888.52       1         1                   2      GOLD   \n",
              "9999         38190.78       0         0                   3   DIAMOND   \n",
              "\n",
              "      Point Earned  \n",
              "0              464  \n",
              "1              456  \n",
              "2              377  \n",
              "3              350  \n",
              "4              425  \n",
              "...            ...  \n",
              "9995           300  \n",
              "9996           771  \n",
              "9997           564  \n",
              "9998           339  \n",
              "9999           911  \n",
              "\n",
              "[10000 rows x 18 columns]"
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9747a122-afa4-481d-82c4-b1ac6834e9aa button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-db6b5c9c-3fa2-40e5-8264-f49d253744f0 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-db6b5c9c-3fa2-40e5-8264-f49d253744f0');\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": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "TYLmPxY9LP_2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "sf3x1BDdLQmp",
        "outputId": "ce1076c8-cf6a-4dcf-de85-679fb6eec7a1"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
              "1          2    15647311      Hill          608     Spain  Female   41   \n",
              "2          3    15619304      Onio          502    France  Female   42   \n",
              "3          4    15701354      Boni          699    France  Female   39   \n",
              "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
              "\n",
              "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0       2       0.00              1          1               1   \n",
              "1       1   83807.86              1          0               1   \n",
              "2       8  159660.80              3          1               0   \n",
              "3       1       0.00              2          0               0   \n",
              "4       2  125510.82              1          1               1   \n",
              "\n",
              "   EstimatedSalary  Exited  Complain  Satisfaction Score Card Type  \\\n",
              "0        101348.88       1         1                   2   DIAMOND   \n",
              "1        112542.58       0         1                   3   DIAMOND   \n",
              "2        113931.57       1         1                   3   DIAMOND   \n",
              "3         93826.63       0         0                   5      GOLD   \n",
              "4         79084.10       0         0                   5      GOLD   \n",
              "\n",
              "   Point Earned  \n",
              "0           464  \n",
              "1           456  \n",
              "2           377  \n",
              "3           350  \n",
              "4           425  "
            ],
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              "      <td>15619304</td>\n",
              "      <td>Onio</td>\n",
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              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
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              "      <td>3</td>\n",
              "      <td>DIAMOND</td>\n",
              "      <td>377</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>15701354</td>\n",
              "      <td>Boni</td>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "      <td>93826.63</td>\n",
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              "      <td>350</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
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              "      <td>0</td>\n",
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              "      <td>GOLD</td>\n",
              "      <td>425</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6358b431-6863-42d8-a26c-c9aa3bd20c6d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-c14f439d-56dd-4b7b-a25d-bf7ab29f9fe8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c14f439d-56dd-4b7b-a25d-bf7ab29f9fe8')\"\n",
              "              title=\"Suggest charts.\"\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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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c14f439d-56dd-4b7b-a25d-bf7ab29f9fe8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-6358b431-6863-42d8-a26c-c9aa3bd20c6d 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-6358b431-6863-42d8-a26c-c9aa3bd20c6d');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['RowNumber','Surname'],axis=1,inplace=True)\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "pYGr1p9sLYmy",
        "outputId": "20297129-a780-48eb-db21-ae13c817cfcf"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CustomerId  CreditScore Geography  Gender  Age  Tenure    Balance  \\\n",
              "0       15634602          619    France  Female   42       2       0.00   \n",
              "1       15647311          608     Spain  Female   41       1   83807.86   \n",
              "2       15619304          502    France  Female   42       8  159660.80   \n",
              "3       15701354          699    France  Female   39       1       0.00   \n",
              "4       15737888          850     Spain  Female   43       2  125510.82   \n",
              "...          ...          ...       ...     ...  ...     ...        ...   \n",
              "9995    15606229          771    France    Male   39       5       0.00   \n",
              "9996    15569892          516    France    Male   35      10   57369.61   \n",
              "9997    15584532          709    France  Female   36       7       0.00   \n",
              "9998    15682355          772   Germany    Male   42       3   75075.31   \n",
              "9999    15628319          792    France  Female   28       4  130142.79   \n",
              "\n",
              "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
              "0                 1          1               1        101348.88       1   \n",
              "1                 1          0               1        112542.58       0   \n",
              "2                 3          1               0        113931.57       1   \n",
              "3                 2          0               0         93826.63       0   \n",
              "4                 1          1               1         79084.10       0   \n",
              "...             ...        ...             ...              ...     ...   \n",
              "9995              2          1               0         96270.64       0   \n",
              "9996              1          1               1        101699.77       0   \n",
              "9997              1          0               1         42085.58       1   \n",
              "9998              2          1               0         92888.52       1   \n",
              "9999              1          1               0         38190.78       0   \n",
              "\n",
              "      Complain  Satisfaction Score Card Type  Point Earned  \n",
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          "metadata": {},
          "execution_count": 5
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    {
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      "source": [
        "df.info()"
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        },
        "id": "I6uM8wvXVaYz",
        "outputId": "3491489d-92d9-4cff-a8cf-c353bf9b302b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 16 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   CustomerId          10000 non-null  int64  \n",
            " 1   CreditScore         10000 non-null  int64  \n",
            " 2   Geography           10000 non-null  object \n",
            " 3   Gender              10000 non-null  object \n",
            " 4   Age                 10000 non-null  int64  \n",
            " 5   Tenure              10000 non-null  int64  \n",
            " 6   Balance             10000 non-null  float64\n",
            " 7   NumOfProducts       10000 non-null  int64  \n",
            " 8   HasCrCard           10000 non-null  int64  \n",
            " 9   IsActiveMember      10000 non-null  int64  \n",
            " 10  EstimatedSalary     10000 non-null  float64\n",
            " 11  Exited              10000 non-null  int64  \n",
            " 12  Complain            10000 non-null  int64  \n",
            " 13  Satisfaction Score  10000 non-null  int64  \n",
            " 14  Card Type           10000 non-null  object \n",
            " 15  Point Earned        10000 non-null  int64  \n",
            "dtypes: float64(2), int64(11), object(3)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "bW018Gw9Pmbo",
        "outputId": "12dd2f69-a42b-4073-842e-f255ebd3e07b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         CustomerId   CreditScore           Age        Tenure        Balance  \\\n",
              "count  1.000000e+04  10000.000000  10000.000000  10000.000000   10000.000000   \n",
              "mean   1.569094e+07    650.528800     38.921800      5.012800   76485.889288   \n",
              "std    7.193619e+04     96.653299     10.487806      2.892174   62397.405202   \n",
              "min    1.556570e+07    350.000000     18.000000      0.000000       0.000000   \n",
              "25%    1.562853e+07    584.000000     32.000000      3.000000       0.000000   \n",
              "50%    1.569074e+07    652.000000     37.000000      5.000000   97198.540000   \n",
              "75%    1.575323e+07    718.000000     44.000000      7.000000  127644.240000   \n",
              "max    1.581569e+07    850.000000     92.000000     10.000000  250898.090000   \n",
              "\n",
              "       NumOfProducts    HasCrCard  IsActiveMember  EstimatedSalary  \\\n",
              "count   10000.000000  10000.00000    10000.000000     10000.000000   \n",
              "mean        1.530200      0.70550        0.515100    100090.239881   \n",
              "std         0.581654      0.45584        0.499797     57510.492818   \n",
              "min         1.000000      0.00000        0.000000        11.580000   \n",
              "25%         1.000000      0.00000        0.000000     51002.110000   \n",
              "50%         1.000000      1.00000        1.000000    100193.915000   \n",
              "75%         2.000000      1.00000        1.000000    149388.247500   \n",
              "max         4.000000      1.00000        1.000000    199992.480000   \n",
              "\n",
              "             Exited      Complain  Satisfaction Score  Point Earned  \n",
              "count  10000.000000  10000.000000        10000.000000  10000.000000  \n",
              "mean       0.203800      0.204400            3.013800    606.515100  \n",
              "std        0.402842      0.403283            1.405919    225.924839  \n",
              "min        0.000000      0.000000            1.000000    119.000000  \n",
              "25%        0.000000      0.000000            2.000000    410.000000  \n",
              "50%        0.000000      0.000000            3.000000    605.000000  \n",
              "75%        0.000000      0.000000            4.000000    801.000000  \n",
              "max        1.000000      1.000000            5.000000   1000.000000  "
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              "\n",
              "\n",
              "  <div id=\"df-e83dbe8f-fd23-4dde-ada7-657cf24e91c4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "      <th>Complain</th>\n",
              "      <th>Satisfaction Score</th>\n",
              "      <th>Point Earned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.569094e+07</td>\n",
              "      <td>650.528800</td>\n",
              "      <td>38.921800</td>\n",
              "      <td>5.012800</td>\n",
              "      <td>76485.889288</td>\n",
              "      <td>1.530200</td>\n",
              "      <td>0.70550</td>\n",
              "      <td>0.515100</td>\n",
              "      <td>100090.239881</td>\n",
              "      <td>0.203800</td>\n",
              "      <td>0.204400</td>\n",
              "      <td>3.013800</td>\n",
              "      <td>606.515100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.193619e+04</td>\n",
              "      <td>96.653299</td>\n",
              "      <td>10.487806</td>\n",
              "      <td>2.892174</td>\n",
              "      <td>62397.405202</td>\n",
              "      <td>0.581654</td>\n",
              "      <td>0.45584</td>\n",
              "      <td>0.499797</td>\n",
              "      <td>57510.492818</td>\n",
              "      <td>0.402842</td>\n",
              "      <td>0.403283</td>\n",
              "      <td>1.405919</td>\n",
              "      <td>225.924839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.556570e+07</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.580000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>119.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.562853e+07</td>\n",
              "      <td>584.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51002.110000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>410.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.569074e+07</td>\n",
              "      <td>652.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>97198.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100193.915000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>605.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.575323e+07</td>\n",
              "      <td>718.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>127644.240000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>149388.247500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>801.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.581569e+07</td>\n",
              "      <td>850.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>250898.090000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>199992.480000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e83dbe8f-fd23-4dde-ada7-657cf24e91c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-e5008467-39c0-4fba-a433-4b24940451ed\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e5008467-39c0-4fba-a433-4b24940451ed')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e5008467-39c0-4fba-a433-4b24940451ed button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-e83dbe8f-fd23-4dde-ada7-657cf24e91c4 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-e83dbe8f-fd23-4dde-ada7-657cf24e91c4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YCxEyVU_P8I4",
        "outputId": "eaa31bfb-474e-40bc-e24c-d946623bb613"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerId            0\n",
              "CreditScore           0\n",
              "Geography             0\n",
              "Gender                0\n",
              "Age                   0\n",
              "Tenure                0\n",
              "Balance               0\n",
              "NumOfProducts         0\n",
              "HasCrCard             0\n",
              "IsActiveMember        0\n",
              "EstimatedSalary       0\n",
              "Exited                0\n",
              "Complain              0\n",
              "Satisfaction Score    0\n",
              "Card Type             0\n",
              "Point Earned          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AxG3YaZQCaR",
        "outputId": "43416944-ba80-49d9-aa25-3d6321ac1742"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerId              int64\n",
              "CreditScore             int64\n",
              "Geography              object\n",
              "Gender                 object\n",
              "Age                     int64\n",
              "Tenure                  int64\n",
              "Balance               float64\n",
              "NumOfProducts           int64\n",
              "HasCrCard               int64\n",
              "IsActiveMember          int64\n",
              "EstimatedSalary       float64\n",
              "Exited                  int64\n",
              "Complain                int64\n",
              "Satisfaction Score      int64\n",
              "Card Type              object\n",
              "Point Earned            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of rows:\", df.shape[0])\n",
        "print(\"Number of columns:\", df.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsRkftXNQN6q",
        "outputId": "f5c49f2a-5200-4073-839f-87e4507ed4b2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows: 10000\n",
            "Number of columns: 16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDWilvJGSGrv",
        "outputId": "96400924-e068-480f-9e25-e76a32ef9b00"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CustomerId', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',\n",
              "       'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember',\n",
              "       'EstimatedSalary', 'Exited', 'Complain', 'Satisfaction Score',\n",
              "       'Card Type', 'Point Earned'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scUz9266SIn7",
        "outputId": "71e7b381-b83f-4554-88c8-1404c26acebd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerId            10000\n",
              "CreditScore           10000\n",
              "Geography             10000\n",
              "Gender                10000\n",
              "Age                   10000\n",
              "Tenure                10000\n",
              "Balance               10000\n",
              "NumOfProducts         10000\n",
              "HasCrCard             10000\n",
              "IsActiveMember        10000\n",
              "EstimatedSalary       10000\n",
              "Exited                10000\n",
              "Complain              10000\n",
              "Satisfaction Score    10000\n",
              "Card Type             10000\n",
              "Point Earned          10000\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['CustomerId'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "lJXOzQbjsi3I"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Exited'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7LnIsFrWR-3",
        "outputId": "106c912c-776c-400a-a486-116612452e39"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7962\n",
              "1    2038\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_outliers_iqr_feature(data,feature):\n",
        "  feature_data=data[feature]\n",
        "  Q1=np.percentile(feature_data,25)\n",
        "  Q3=np.percentile(feature_data,75)\n",
        "  iqr=Q3-Q1\n",
        "  lower_bound=Q1-1.5*iqr\n",
        "  upperbound=Q3+1.5*iqr\n",
        "\n",
        "  outliers=np.where((feature_data<lower_bound)|(feature_data>upperbound))[0]"
      ],
      "metadata": {
        "id": "mMSVq2B6VR9V"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_name=['CreditScore','Age','Tenure','Balance','NumOfProducts','HasCrCard','IsActiveMember','EstimatedSalary','Complain','Satisfaction Score','Point Earned']\n",
        "for i in feature_name:\n",
        "  outliers=detect_outliers_iqr_feature(df,i)\n",
        "  print(\"Outliers are present in\",i,\":\",outliers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Shcy2UQuizi6",
        "outputId": "10493cf8-eede-4f45-a9ff-5960e26f56bb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers are present in CreditScore : None\n",
            "Outliers are present in Age : None\n",
            "Outliers are present in Tenure : None\n",
            "Outliers are present in Balance : None\n",
            "Outliers are present in NumOfProducts : None\n",
            "Outliers are present in HasCrCard : None\n",
            "Outliers are present in IsActiveMember : None\n",
            "Outliers are present in EstimatedSalary : None\n",
            "Outliers are present in Complain : None\n",
            "Outliers are present in Satisfaction Score : None\n",
            "Outliers are present in Point Earned : None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Data Visualization"
      ],
      "metadata": {
        "id": "PoS6bqTzSqZj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn distribution\n",
        "\n",
        "plt.figure(figsize=(6, 6))\n",
        "a=df['Exited'].value_counts()\n",
        "labels=['Retained','Churned']\n",
        "plt.pie(a,labels=labels, autopct='%1.1f%%')\n",
        "plt.title('Churn Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "-uNVI1dwSq9T",
        "outputId": "4aa42b8f-33a9-4e88-c5d7-7b6c513f8840"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn by gender\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='Gender', hue='Exited', data=df)\n",
        "plt.title('Churn by Gender')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "wp-YIO0pyAtC",
        "outputId": "15ae762f-73e1-4562-b253-15d35112d1bc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "churn_rate_Gender = df.groupby('Gender')['Exited'].mean()\n",
        "print(churn_rate_Gender)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lk98YuZ54bo4",
        "outputId": "81b43f54-2fc2-43d4-c035-1f526b1ebbab"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gender\n",
            "Female    0.250715\n",
            "Male      0.164743\n",
            "Name: Exited, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The churn rate is higher for female customers compared to male customers. This indicates that gender may play a role in customer churn."
      ],
      "metadata": {
        "id": "6g8N_k7H1gkV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn by geography\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='Geography', hue='Exited', data=df)\n",
        "plt.title('Churn by Geography')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "uQXMjhE7yX8y",
        "outputId": "2c417d4d-f49d-4f27-843f-e832d45b23a7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "churn_rate_Geography = df.groupby('Geography')['Exited'].mean()\n",
        "print(churn_rate_Geography)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPjlQd4r4n23",
        "outputId": "cc950ede-acf6-48d3-eb0c-60c036d3e80b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Geography\n",
            "France     0.161747\n",
            "Germany    0.324432\n",
            "Spain      0.166734\n",
            "Name: Exited, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The churn rate varies across different countries. Germany has the highest churn rate, followed by Spain and France. This suggests that geographic location may impact customer churn."
      ],
      "metadata": {
        "id": "Xpwux6ul180b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn by age group\n",
        "\n",
        "df['AgeGroup'] = pd.cut(df['Age'], bins=[0, 30, 40, 50, 60, np.inf], labels=['<30', '30-40', '40-50', '50-60', '60+'])\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='AgeGroup', hue='Exited', data=df)\n",
        "plt.title('Churn by Age Group')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "kg5h3PjuzWjH",
        "outputId": "f439aea0-e7ae-4671-ac87-f187671ff2b3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "churn_rate_AgeGroup = df.groupby('AgeGroup')['Exited'].mean()\n",
        "print(churn_rate_AgeGroup)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-VIcUSb431X",
        "outputId": "229e2398-374a-4cb0-c375-272c9cb1ebcb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AgeGroup\n",
            "<30      0.075203\n",
            "30-40    0.121096\n",
            "40-50    0.339655\n",
            "50-60    0.562108\n",
            "60+      0.247845\n",
            "Name: Exited, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Age group strongly influences churn rate. The highest churn rate is observed in the 50-60 age group, followed by the 40-50 age group. The lowest churn rate is observed in the <30 age group. This implies that older customers are more likely to churn, while younger customers are more likely to stay with the bank."
      ],
      "metadata": {
        "id": "WsDVE5_Rzgt_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn by Number of Products:\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='NumOfProducts', hue='Exited', data=df)\n",
        "plt.title('Churn by Number of Products')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OxoIk-gN0FFP",
        "outputId": "7a410b52-7e7c-4d88-ba35-ec414b8f9f70"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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X3n333erRo4f+9re/6cSJEwoNDdX69etLhVBJGjZsmF566SVFRkZq+PDhysrK0rJly9SuXTunX898xx13aPDgwVqwYIG++eYb9evXT8XFxfroo490xx13OL602aVLF23btk0vvfSSgoOD1bx5c7Vu3VqNGzfWgw8+qI4dO6pOnTratm2bDhw4oLlz51bNmwfcSFy4AwWAG8DXX39tjBw50mjWrJlhtVqNunXrGj169DAWLlxoXLp0ydFPUplbMDVt2rTUlk0ffvih0b59e8NqtRqtW7c23nnnnd/c6quse5Zsg3bgwAGn9pKtxXbu3HnFMUVHRxu1a9c2jh8/bvTt29fw8fExAgICjKlTpzptyfVLTzzxhCHJWLVq1RXv/Uu/3Abtl7755hvD3d291DZohnF97016erohyZgzZ45Te1lbrvXq1cto166d8emnnxp2u93w8vIymjZtaixatKhUvQUFBcbs2bONdu3aGZ6enkb9+vWNLl26GNOnTzdycnKuWNPvWbRokdGmTRvDw8PDCAgIMMaMGWOcP3/eqU95tkH7vb4l4y9LZmam8dhjjxmNGjUyrFarERYW5rStWYkffvjBGDx4sGGz2QxfX19j8ODBxueff15qGzTDMIx33nnHaNGihWG1Wo1OnToZW7duLXPbu8LCQmPOnDlGmzZtDKvVavj5+Rn9+/c3UlJSHH2OHj1q9OzZ0/D29jYkGdHR0UZ+fr4xYcIEo2PHjkbdunWN2rVrGx07djSWLFnyu+8ZYEYWw6jAb4sAQA01fvx4vf7668rIyODX5QLADY41wADwOy5duqR33nlHAwYMIPwCQA3AGmAA+A1ZWVnatm2b1q1bpx9++MHxa3ABADc2AjAA/IYvv/xSgwYNkr+/vxYsWKBOnTq5uiQAQAVgDTAAAABMhTXAAAAAMBUCMAAAAEyFNcBXobi4WKdPn1bdunWv+CsoAQAA4BqGYejHH39UcHCw3NyuPMdLAL4Kp0+fVkhIiKvLAAAAwO84deqUGjdufMU+BOCrUPKrL0+dOiWbzebiagAAAPBrubm5CgkJceS2KyEAX4WSZQ82m40ADAAAUI1dzXJVvgQHAAAAUyEAAwAAwFQIwAAAADAV1gADAADUEEVFRbp8+bKry6g0Hh4ecnd3v+77EIABAABqgAsXLuj777+XYRiuLqXSWCwWNW7cWHXq1Lmu+xCAAQAAbnBFRUX6/vvv5ePjIz8/vxr5i7sMw9DZs2f1/fff66abbrqumWACMAAAwA3u8uXLMgxDfn5+8vb2dnU5lcbPz08nTpzQ5cuXrysA8yU4AACAGqImzvz+UkWNjwAMAAAAUyEAAwAAwFQIwAAAALgq4eHhGjduXKXcu1mzZpo3b16l3PvXCMAAAAAmMXToUFksllJHv379rur69evX69lnn3W8rsrQWpHYBQIAAMBE+vXrpzfffNOpzdPT86qubdCgQWWUVOWYAQYAADART09PBQYGOh3169fXrl27ZLVa9dFHHzn6xsfHy9/fX5mZmZKcl0CEh4fru+++0/jx4x0zySX27Nmj22+/Xd7e3goJCdFTTz2lvLw8x/msrCzdfffd8vb2VvPmzbVy5cqqGfz/RwAGAACAI9wOHjxYOTk5+vzzz/U///M/eu211xQQEFCq//r169W4cWPNmDFDZ86c0ZkzZyRJx48fV79+/TRgwAAdPHhQq1ev1p49ezR27FjHtUOHDtWpU6e0c+dOrVu3TkuWLFFWVlaVjZUlEAAAACayadOmUr9K+JlnntEzzzyj5557TomJiRo1apQOHz6s6Oho3XPPPWXep0GDBnJ3d1fdunUVGBjoaJ85c6YGDRrkmCm+6aabtGDBAvXq1UtLly7VyZMntXnzZu3fv1+33nqrJOn1119X27ZtK2fAZSAAAwAAmMgdd9yhpUuXOrWVrO21Wq1auXKlOnTooKZNm+rll1++5vt/8cUXOnjwoNOyBsMwVFxcrPT0dH399deqVauWunTp4jjfpk0b1atXr3wDKgcCMAAAgInUrl1brVq1+s3zH3/8sSTp3LlzOnfunGrXrn1N979w4YIef/xxPfXUU6XONWnSRF9//fW1FVwJWAMMAAAAST+v3x0/frxeffVVdevWTdHR0SouLv7N/larVUVFRU5tnTt31pdffqlWrVqVOqxWq9q0aaPCwkKlpKQ4rklLS1N2dnZlDasUZoCBGqbLhLddXYJLpMwZ4uoSAOCGkJ+fr4yMDKe2WrVqqX79+nr00UcVGRmpxx57TP369VNYWJjmzp2rCRMmlHmvZs2aaffu3Ro4cKA8PT3VqFEjTZo0Sd27d9fYsWM1YsQI1a5dW19++aUSExO1aNEitW7dWv369dPjjz+upUuXqlatWho3bpy8vb2rYviSmAEGAAAwlS1btigoKMjp+OMf/6jnn39e3333nZYvXy5JCgoK0iuvvKLJkyfriy++KPNeM2bM0IkTJ9SyZUv5+flJkjp06KCkpCR9/fXXuv3223XLLbdoypQpCg4Odlz35ptvKjg4WL169dIDDzygUaNGyd/fv/IH//9ZDMMwquxpN6jc3Fz5+voqJydHNpvN1eUAV8QMMACYz6VLl5Senq7mzZvLy8vL1eVUmiuN81ryGjPAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMJVari4AAAAAlaOqfzvojfJbOZkBBgAAgEstXrxYzZo1k5eXl7p166b9+/dX6vMIwAAAAHCZ1atXKzY2VlOnTtVnn32mjh07KjIyUllZWZX2TAIwAAAAXOall17SyJEj9dhjjyk0NFTLli2Tj4+P3njjjUp7JgEYAAAALlFQUKCUlBRFREQ42tzc3BQREaHk5ORKey4BGAAAAC7xn//8R0VFRQoICHBqDwgIUEZGRqU9lwAMAAAAUyEAAwAAwCUaNWokd3d3ZWZmOrVnZmYqMDCw0p5LAAYAAIBLWK1WdenSRdu3b3e0FRcXa/v27bLb7ZX23GoTgGfNmiWLxaJx48Y52i5duqSYmBg1bNhQderU0YABA0r9DeHkyZOKioqSj4+P/P39NWHCBBUWFjr12bVrlzp37ixPT0+1atVKK1asqIIRAQAA4PfExsbq1Vdf1VtvvaWvvvpKY8aMUV5enh577LFKe2a1+E1wBw4c0PLly9WhQwen9vHjxyshIUFr166Vr6+vxo4dqwceeEB79+6VJBUVFSkqKkqBgYH6+OOPdebMGQ0ZMkQeHh564YUXJEnp6emKiorS6NGjtXLlSm3fvl0jRoxQUFCQIiMjq3ysAAAAVeVG+M1sDz30kM6ePaspU6YoIyNDnTp10pYtW0p9Ma4iWQzDMCrt7lfhwoUL6ty5s5YsWaLnnntOnTp10rx585STkyM/Pz+tWrVKDz74oCTp6NGjatu2rZKTk9W9e3dt3rxZd911l06fPu14k5YtW6ZJkybp7NmzslqtmjRpkhISEnT48GHHMwcOHKjs7Gxt2bLlqmrMzc2Vr6+vcnJyZLPZKv5NACpQVf/ay+riRviXPABUlkuXLik9PV3NmzeXl5eXq8upNFca57XkNZcvgYiJiVFUVJTT/m+SlJKSosuXLzu1t2nTRk2aNHHsC5ecnKywsDCnvyFERkYqNzdXR44ccfT59b0jIyOvuLdcfn6+cnNznQ4AAADUDC5dAvHuu+/qs88+04EDB0qdy8jIkNVqVb169Zzaf7kvXEZGRpn7xpWcu1Kf3NxcXbx4Ud7e3qWePXPmTE2fPr3c4wIAAED15bIZ4FOnTumvf/2rVq5cWe2m6uPi4pSTk+M4Tp065eqSAAAAUEFcFoBTUlKUlZWlzp07q1atWqpVq5aSkpK0YMEC1apVSwEBASooKFB2drbTdb/cFy4wMLDMfeNKzl2pj81mK3P2V5I8PT1ls9mcDgAAANQMLgvAffr00aFDh5Samuo4unbtqkGDBjn+7OHh4bQvXFpamk6ePOnYF85ut+vQoUPKyspy9ElMTJTNZlNoaKijzy/vUdKnMveWAwAAQPXlsjXAdevWVfv27Z3aateurYYNGzrahw8frtjYWDVo0EA2m01PPvmk7Ha7unfvLknq27evQkNDNXjwYMXHxysjI0OTJ09WTEyMPD09JUmjR4/WokWLNHHiRA0bNkw7duzQmjVrlJCQULUDBgAAQLVQLfYB/i0vv/yy3NzcNGDAAOXn5ysyMlJLlixxnHd3d9emTZs0ZswY2e121a5dW9HR0ZoxY4ajT/PmzZWQkKDx48dr/vz5aty4sV577TX2AAYAADApl+8DfCNgH2DcSNgHGADMh32Ab7B9gAEAAICqVK2XQAAAAKD8Ts4Iq9LnNZlyqEqfV17MAAMAAMBldu/erbvvvlvBwcGyWCzauHFjpT+TAAwAAACXycvLU8eOHbV48eIqeyZLIAAAAOAy/fv3V//+/av0mcwAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFTYBQIAAAAuc+HCBR07dszxOj09XampqWrQoIGaNGlSKc8kAAMAANRQN8JvZvv00091xx13OF7HxsZKkqKjo7VixYpKeSYBGAAAAC4THh4uwzCq9JmsAQYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAKghqvrLZFWtosZHAAYAALjBubu7S5IKCgpcXEnlKhlfyXjLi23QAAAAbnC1atWSj4+Pzp49Kw8PD7m51bw5zuLiYp09e1Y+Pj6qVev6IiwBGAAA4AZnsVgUFBSk9PR0fffdd64up9K4ubmpSZMmslgs13UfAjAAAEANYLVaddNNN9XoZRBWq7VCZrcJwAAAADWEm5ubvLy8XF1GtVfzFogAAAAAV0AABgAAgKkQgAEAAGAqrAGuIl0mvO3qElwiZc4QV5cAAADghBlgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKbi0gC8dOlSdejQQTabTTabTXa7XZs3b3acDw8Pl8VicTpGjx7tdI+TJ08qKipKPj4+8vf314QJE1RYWOjUZ9euXercubM8PT3VqlUrrVixoiqGBwAAgGqolisf3rhxY82aNUs33XSTDMPQW2+9pXvvvVeff/652rVrJ0kaOXKkZsyY4bjGx8fH8eeioiJFRUUpMDBQH3/8sc6cOaMhQ4bIw8NDL7zwgiQpPT1dUVFRGj16tFauXKnt27drxIgRCgoKUmRkZNUOGAAAAC7n0gB89913O71+/vnntXTpUu3bt88RgH18fBQYGFjm9R9++KG+/PJLbdu2TQEBAerUqZOeffZZTZo0SdOmTZPVatWyZcvUvHlzzZ07V5LUtm1b7dmzRy+//DIBGAAAwISqzRrgoqIivfvuu8rLy5Pdbne0r1y5Uo0aNVL79u0VFxenn376yXEuOTlZYWFhCggIcLRFRkYqNzdXR44ccfSJiIhwelZkZKSSk5N/s5b8/Hzl5uY6HQAAAKgZXDoDLEmHDh2S3W7XpUuXVKdOHW3YsEGhoaGSpEceeURNmzZVcHCwDh48qEmTJiktLU3r16+XJGVkZDiFX0mO1xkZGVfsk5ubq4sXL8rb27tUTTNnztT06dMrfKwAAABwPZcH4NatWys1NVU5OTlat26doqOjlZSUpNDQUI0aNcrRLywsTEFBQerTp4+OHz+uli1bVlpNcXFxio2NdbzOzc1VSEhIpT0PAAAAVcflSyCsVqtatWqlLl26aObMmerYsaPmz59fZt9u3bpJko4dOyZJCgwMVGZmplOfktcl64Z/q4/NZitz9leSPD09HTtTlBwAAACoGVwegH+tuLhY+fn5ZZ5LTU2VJAUFBUmS7Ha7Dh06pKysLEefxMRE2Ww2xzIKu92u7du3O90nMTHRaZ0xAAAAzMOlSyDi4uLUv39/NWnSRD/++KNWrVqlXbt2aevWrTp+/LhWrVqlO++8Uw0bNtTBgwc1fvx49ezZUx06dJAk9e3bV6GhoRo8eLDi4+OVkZGhyZMnKyYmRp6enpKk0aNHa9GiRZo4caKGDRumHTt2aM2aNUpISHDl0AEAAOAiLg3AWVlZGjJkiM6cOSNfX1916NBBW7du1Z/+9CedOnVK27Zt07x585SXl6eQkBANGDBAkydPdlzv7u6uTZs2acyYMbLb7apdu7aio6Od9g1u3ry5EhISNH78eM2fP1+NGzfWa6+9xhZoAAAAJuXSAPz666//5rmQkBAlJSX97j2aNm2qDz744Ip9wsPD9fnnn19zfQAAAKh5qt0aYAAAAKAyEYABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpEIABAABgKgRgAAAAmAoBGAAAAKZCAAYAAICpuDQAL126VB06dJDNZpPNZpPdbtfmzZsd5y9duqSYmBg1bNhQderU0YABA5SZmel0j5MnTyoqKko+Pj7y9/fXhAkTVFhY6NRn165d6ty5szw9PdWqVSutWLGiKoYHAACAasilAbhx48aaNWuWUlJS9Omnn6p379669957deTIEUnS+PHj9f7772vt2rVKSkrS6dOn9cADDziuLyoqUlRUlAoKCvTxxx/rrbfe0ooVKzRlyhRHn/T0dEVFRemOO+5Qamqqxo0bpxEjRmjr1q1VPl4AAAC4nsUwDMPVRfxSgwYNNGfOHD344IPy8/PTqlWr9OCDD0qSjh49qrZt2yo5OVndu3fX5s2bddddd+n06dMKCAiQJC1btkyTJk3S2bNnZbVaNWnSJCUkJOjw4cOOZwwcOFDZ2dnasmVLmTXk5+crPz/f8To3N1chISHKycmRzWYr17i6THi7XNfd6FLmDHF1CabDZw0AYEa5ubny9fW9qrxWbdYAFxUV6d1331VeXp7sdrtSUlJ0+fJlRUREOPq0adNGTZo0UXJysiQpOTlZYWFhjvArSZGRkcrNzXXMIicnJzvdo6RPyT3KMnPmTPn6+jqOkJCQihwqAAAAXMjlAfjQoUOqU6eOPD09NXr0aG3YsEGhoaHKyMiQ1WpVvXr1nPoHBAQoIyNDkpSRkeEUfkvOl5y7Up/c3FxdvHixzJri4uKUk5PjOE6dOlURQwUAAEA1UMvVBbRu3VqpqanKycnRunXrFB0draSkJJfW5OnpKU9PT5fWAAAAgMrh8gBstVrVqlUrSVKXLl104MABzZ8/Xw899JAKCgqUnZ3tNAucmZmpwMBASVJgYKD279/vdL+SXSJ+2efXO0dkZmbKZrPJ29u7soYFAACAasrlSyB+rbi4WPn5+erSpYs8PDy0fft2x7m0tDSdPHlSdrtdkmS323Xo0CFlZWU5+iQmJspmsyk0NNTR55f3KOlTcg8AAACYi0tngOPi4tS/f381adJEP/74o1atWqVdu3Zp69at8vX11fDhwxUbG6sGDRrIZrPpySeflN1uV/fu3SVJffv2VWhoqAYPHqz4+HhlZGRo8uTJiomJcSxhGD16tBYtWqSJEydq2LBh2rFjh9asWaOEhARXDh0AAAAu4tIAnJWVpSFDhujMmTPy9fVVhw4dtHXrVv3pT3+SJL388styc3PTgAEDlJ+fr8jISC1ZssRxvbu7uzZt2qQxY8bIbrerdu3aio6O1owZMxx9mjdvroSEBI0fP17z589X48aN9dprrykyMrLKxwsAAADXq3b7AFdH17Kv3G9hb1ZUFT5rAAAzuiH3AQYAAACqAgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqLg3AM2fO1K233qq6devK399f9913n9LS0pz6hIeHy2KxOB2jR4926nPy5ElFRUXJx8dH/v7+mjBhggoLC5367Nq1S507d5anp6datWqlFStWVPbwAAAAUA25NAAnJSUpJiZG+/btU2Jioi5fvqy+ffsqLy/Pqd/IkSN15swZxxEfH+84V1RUpKioKBUUFOjjjz/WW2+9pRUrVmjKlCmOPunp6YqKitIdd9yh1NRUjRs3TiNGjNDWrVurbKwAAACoHmq58uFbtmxxer1ixQr5+/srJSVFPXv2dLT7+PgoMDCwzHt8+OGH+vLLL7Vt2zYFBASoU6dOevbZZzVp0iRNmzZNVqtVy5YtU/PmzTV37lxJUtu2bbVnzx69/PLLioyMrLwBAgAAoNqpVmuAc3JyJEkNGjRwal+5cqUaNWqk9u3bKy4uTj/99JPjXHJyssLCwhQQEOBoi4yMVG5uro4cOeLoExER4XTPyMhIJScnl1lHfn6+cnNznQ4AAADUDC6dAf6l4uJijRs3Tj169FD79u0d7Y888oiaNm2q4OBgHTx4UJMmTVJaWprWr18vScrIyHAKv5IcrzMyMq7YJzc3VxcvXpS3t7fTuZkzZ2r69OkVPkYAAAC4XrUJwDExMTp8+LD27Nnj1D5q1CjHn8PCwhQUFKQ+ffro+PHjatmyZaXUEhcXp9jYWMfr3NxchYSEVMqzAAAAULWqxRKIsWPHatOmTdq5c6caN258xb7dunWTJB07dkySFBgYqMzMTKc+Ja9L1g3/Vh+bzVZq9leSPD09ZbPZnA4AAADUDC4NwIZhaOzYsdqwYYN27Nih5s2b/+41qampkqSgoCBJkt1u16FDh5SVleXok5iYKJvNptDQUEef7du3O90nMTFRdru9gkYCAACAG4VLA3BMTIzeeecdrVq1SnXr1lVGRoYyMjJ08eJFSdLx48f17LPPKiUlRSdOnNB7772nIUOGqGfPnurQoYMkqW/fvgoNDdXgwYP1xRdfaOvWrZo8ebJiYmLk6ekpSRo9erS+/fZbTZw4UUePHtWSJUu0Zs0ajR8/3mVjBwAAgGu4NAAvXbpUOTk5Cg8PV1BQkONYvXq1JMlqtWrbtm3q27ev2rRpo6effloDBgzQ+++/77iHu7u7Nm3aJHd3d9ntdj366KMaMmSIZsyY4ejTvHlzJSQkKDExUR07dtTcuXP12muvsQUaAACACbn0S3CGYVzxfEhIiJKSkn73Pk2bNtUHH3xwxT7h4eH6/PPPr6k+AAAA1DzV4ktwAAAAQFUhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMpVwDu3bu3srOzS7Xn5uaqd+/e11sTAAAAUGnKFYB37dqlgoKCUu2XLl3SRx99dN1FAQAAAJXlmvYBPnjwoOPPX375pTIyMhyvi4qKtGXLFv3Xf/1XxVUHAAAAVLBrCsCdOnWSxWKRxWIpc6mDt7e3Fi5cWGHFAQAAABXtmgJwenq6DMNQixYttH//fvn5+TnOWa1W+fv7y93dvcKLBAAAACrKNQXgpk2bSpKKi4srpRgAAACgsl1TAP6lb775Rjt37lRWVlapQDxlypTrLgwAAACoDOUKwK+++qrGjBmjRo0aKTAwUBaLxXHOYrEQgAEAAFBtlSsAP/fcc3r++ec1adKkiq4HAAAAqFTl2gf4/Pnz+vOf/1zRtQAAAACVrlwB+M9//rM+/PDDiq4FAAAAqHTlWgLRqlUr/c///I/27dunsLAweXh4OJ1/6qmnKqQ4AAAAoKKVKwC/8sorqlOnjpKSkpSUlOR0zmKxEIABAABQbZUrAKenp1d0HQAAAECVKNcaYAAAAOBGVa4Z4GHDhl3x/BtvvFGuYgAAAIDKVq4AfP78eafXly9f1uHDh5Wdna3evXtXSGEAAABAZShXAN6wYUOptuLiYo0ZM0YtW7a87qIAAACAylJha4Dd3NwUGxurl19+uaJuCQAAAFS4Cv0S3PHjx1VYWFiRtwQAAAAqVLmWQMTGxjq9NgxDZ86cUUJCgqKjoyukMAAAAKAylCsAf/75506v3dzc5Ofnp7lz5/7uDhEAAACAK5UrAO/cubOi6wAAAACqRLkCcImzZ88qLS1NktS6dWv5+flVSFEAAABAZSnXl+Dy8vI0bNgwBQUFqWfPnurZs6eCg4M1fPhw/fTTTxVdIwAAAFBhyhWAY2NjlZSUpPfff1/Z2dnKzs7Wv//9byUlJenpp5+u6BoBAACAClOuJRD/+te/tG7dOoWHhzva7rzzTnl7e+svf/mLli5dWlH1AQAAABWqXDPAP/30kwICAkq1+/v7swQCAAAA1Vq5ArDdbtfUqVN16dIlR9vFixc1ffp02e32CisOAAAAqGjlWgIxb9489evXT40bN1bHjh0lSV988YU8PT314YcfVmiBAAAAQEUqVwAOCwvTN998o5UrV+ro0aOSpIcffliDBg2St7d3hRYIAAAAVKRyBeCZM2cqICBAI0eOdGp/4403dPbsWU2aNKlCigMAAAAqWrnWAC9fvlxt2rQp1d6uXTstW7bsuosCAAAAKku5AnBGRoaCgoJKtfv5+enMmTPXXRQAAABQWcoVgENCQrR3795S7Xv37lVwcPB1FwUAAABUlnKtAR45cqTGjRuny5cvq3fv3pKk7du3a+LEifwmOAAAAFRr5ZoBnjBhgoYPH64nnnhCLVq0UIsWLfTkk0/qqaeeUlxc3FXfZ+bMmbr11ltVt25d+fv767777lNaWppTn0uXLikmJkYNGzZUnTp1NGDAAGVmZjr1OXnypKKiouTj4yN/f39NmDBBhYWFTn127dqlzp07y9PTU61atdKKFSvKM3QAAADc4MoVgC0Wi2bPnq2zZ89q3759+uKLL3Tu3DlNmTLlmu6TlJSkmJgY7du3T4mJibp8+bL69u2rvLw8R5/x48fr/fff19q1a5WUlKTTp0/rgQcecJwvKipSVFSUCgoK9PHHH+utt97SihUrnGpJT09XVFSU7rjjDqWmpmrcuHEaMWKEtm7dWp7hAwAA4AZmMQzDcHURJc6ePSt/f38lJSWpZ8+eysnJkZ+fn1atWqUHH3xQknT06FG1bdtWycnJ6t69uzZv3qy77rpLp0+fdvx65mXLlmnSpEk6e/asrFarJk2apISEBB0+fNjxrIEDByo7O1tbtmz53bpyc3Pl6+urnJwc2Wy2co2ty4S3y3XdjS5lzhBXl2A6fNYAAGZ0LXmtXDPAlSUnJ0eS1KBBA0lSSkqKLl++rIiICEefNm3aqEmTJkpOTpYkJScnKywszBF+JSkyMlK5ubk6cuSIo88v71HSp+Qev5afn6/c3FynAwAAADVDtQnAxcXFGjdunHr06KH27dtL+nm7NavVqnr16jn1DQgIUEZGhqPPL8NvyfmSc1fqk5ubq4sXL5aqZebMmfL19XUcISEhFTJGAAAAuF61CcAxMTE6fPiw3n33XVeXori4OOXk5DiOU6dOubokAAAAVJBybYNW0caOHatNmzZp9+7daty4saM9MDBQBQUFys7OdpoFzszMVGBgoKPP/v37ne5XskvEL/v8eueIzMxM2Ww2eXt7l6rH09NTnp6eFTI2AAAAVC8unQE2DENjx47Vhg0btGPHDjVv3tzpfJcuXeTh4aHt27c72tLS0nTy5EnZ7XZJkt1u16FDh5SVleXok5iYKJvNptDQUEefX96jpE/JPQAAAGAeLp0BjomJ0apVq/Tvf/9bdevWdazZ9fX1lbe3t3x9fTV8+HDFxsaqQYMGstlsevLJJ2W329W9e3dJUt++fRUaGqrBgwcrPj5eGRkZmjx5smJiYhyzuKNHj9aiRYs0ceJEDRs2TDt27NCaNWuUkJDgsrEDAADANVw6A7x06VLl5OQoPDxcQUFBjmP16tWOPi+//LLuuusuDRgwQD179lRgYKDWr1/vOO/u7q5NmzbJ3d1ddrtdjz76qIYMGaIZM2Y4+jRv3lwJCQlKTExUx44dNXfuXL322muKjIys0vECAADA9Vw6A3w1WxB7eXlp8eLFWrx48W/2adq0qT744IMr3ic8PFyff/75NdcIAACAmqXa7AIBAAAAVAUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBUCMAAAAEyFAAwAAABTIQADAADAVAjAAAAAMBWXBuDdu3fr7rvvVnBwsCwWizZu3Oh0fujQobJYLE5Hv379nPqcO3dOgwYNks1mU7169TR8+HBduHDBqc/Bgwd1++23y8vLSyEhIYqPj6/soQEAAKCacmkAzsvLU8eOHbV48eLf7NOvXz+dOXPGcfzzn/90Oj9o0CAdOXJEiYmJ2rRpk3bv3q1Ro0Y5zufm5qpv375q2rSpUlJSNGfOHE2bNk2vvPJKpY0LAAAA1VctVz68f//+6t+//xX7eHp6KjAwsMxzX331lbZs2aIDBw6oa9eukqSFCxfqzjvv1Isvvqjg4GCtXLlSBQUFeuONN2S1WtWuXTulpqbqpZdecgrKAAAAMIdqvwZ4165d8vf3V+vWrTVmzBj98MMPjnPJycmqV6+eI/xKUkREhNzc3PTJJ584+vTs2VNWq9XRJzIyUmlpaTp//nyZz8zPz1dubq7TAQAAgJqhWgfgfv366e2339b27ds1e/ZsJSUlqX///ioqKpIkZWRkyN/f3+maWrVqqUGDBsrIyHD0CQgIcOpT8rqkz6/NnDlTvr6+jiMkJKSihwYAAAAXcekSiN8zcOBAx5/DwsLUoUMHtWzZUrt27VKfPn0q7blxcXGKjY11vM7NzSUEAwAA1BDVegb411q0aKFGjRrp2LFjkqTAwEBlZWU59SksLNS5c+cc64YDAwOVmZnp1Kfk9W+tLfb09JTNZnM6AAAAUDPcUAH4+++/1w8//KCgoCBJkt1uV3Z2tlJSUhx9duzYoeLiYnXr1s3RZ/fu3bp8+bKjT2Jiolq3bq369etX7QAAAADgci4NwBcuXFBqaqpSU1MlSenp6UpNTdXJkyd14cIFTZgwQfv27dOJEye0fft23XvvvWrVqpUiIyMlSW3btlW/fv00cuRI7d+/X3v37tXYsWM1cOBABQcHS5IeeeQRWa1WDR8+XEeOHNHq1as1f/58pyUOAAAAMA+XBuBPP/1Ut9xyi2655RZJUmxsrG655RZNmTJF7u7uOnjwoO655x7dfPPNGj58uLp06aKPPvpInp6ejnusXLlSbdq0UZ8+fXTnnXfqj3/8o9Mev76+vvrwww+Vnp6uLl266Omnn9aUKVPYAg0AAMCkXPoluPDwcBmG8Zvnt27d+rv3aNCggVatWnXFPh06dNBHH310zfUBAACg5rmh1gADAAAA14sADAAAAFMhAAMAAMBUqvUvwsCN7+SMMFeX4BJNphxydQkAAOA3MAMMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAU3FpAN69e7fuvvtuBQcHy2KxaOPGjU7nDcPQlClTFBQUJG9vb0VEROibb75x6nPu3DkNGjRINptN9erV0/Dhw3XhwgWnPgcPHtTtt98uLy8vhYSEKD4+vrKHBgAAgGrKpQE4Ly9PHTt21OLFi8s8Hx8frwULFmjZsmX65JNPVLt2bUVGRurSpUuOPoMGDdKRI0eUmJioTZs2affu3Ro1apTjfG5urvr27aumTZsqJSVFc+bM0bRp0/TKK69U+vgAAABQ/dRy5cP79++v/v37l3nOMAzNmzdPkydP1r333itJevvttxUQEKCNGzdq4MCB+uqrr7RlyxYdOHBAXbt2lSQtXLhQd955p1588UUFBwdr5cqVKigo0BtvvCGr1ap27dopNTVVL730klNQBgAAgDlU2zXA6enpysjIUEREhKPN19dX3bp1U3JysiQpOTlZ9erVc4RfSYqIiJCbm5s++eQTR5+ePXvKarU6+kRGRiotLU3nz58v89n5+fnKzc11OgAAAFAzVNsAnJGRIUkKCAhwag8ICHCcy8jIkL+/v9P5WrVqqUGDBk59yrrHL5/xazNnzpSvr6/jCAkJuf4BAQAAoFqotgHYleLi4pSTk+M4Tp065eqSAAAAUEGqbQAODAyUJGVmZjq1Z2ZmOs4FBgYqKyvL6XxhYaHOnTvn1Kese/zyGb/m6ekpm83mdAAAAKBmqLYBuHnz5goMDNT27dsdbbm5ufrkk09kt9slSXa7XdnZ2UpJSXH02bFjh4qLi9WtWzdHn927d+vy5cuOPomJiWrdurXq169fRaMBAABAdeHSAHzhwgWlpqYqNTVV0s9ffEtNTdXJkydlsVg0btw4Pffcc3rvvfd06NAhDRkyRMHBwbrvvvskSW3btlW/fv00cuRI7d+/X3v37tXYsWM1cOBABQcHS5IeeeQRWa1WDR8+XEeOHNHq1as1f/58xcbGumjUAAAAcCWXboP26aef6o477nC8Lgml0dHRWrFihSZOnKi8vDyNGjVK2dnZ+uMf/6gtW7bIy8vLcc3KlSs1duxY9enTR25ubhowYIAWLFjgOO/r66sPP/xQMTEx6tKlixo1aqQpU6awBRoAAIBJWQzDMFxdRHWXm5srX19f5eTklHs9cJcJb1dwVTeGDXXnuLoEl2gy5ZDLnm3Wz1rKnCGuLgEA4ELXkteq7RpgAAAAoDIQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKkQgAEAAGAqBGAAAACYCgEYAAAApkIABgAAgKlU6wA8bdo0WSwWp6NNmzaO85cuXVJMTIwaNmyoOnXqaMCAAcrMzHS6x8mTJxUVFSUfHx/5+/trwoQJKiwsrOqhAAAAoJqo5eoCfk+7du20bds2x+tatf6v5PHjxyshIUFr166Vr6+vxo4dqwceeEB79+6VJBUVFSkqKkqBgYH6+OOPdebMGQ0ZMkQeHh564YUXqnwsAAAAcL1qH4Br1aqlwMDAUu05OTl6/fXXtWrVKvXu3VuS9Oabb6pt27bat2+funfvrg8//FBffvmltm3bpoCAAHXq1EnPPvusJk2apGnTpslqtVb1cAAAAOBi1XoJhCR98803Cg4OVosWLTRo0CCdPHlSkpSSkqLLly8rIiLC0bdNmzZq0qSJkpOTJUnJyckKCwtTQECAo09kZKRyc3N15MiR33xmfn6+cnNznQ4AAADUDNU6AHfr1k0rVqzQli1btHTpUqWnp+v222/Xjz/+qIyMDFmtVtWrV8/pmoCAAGVkZEiSMjIynMJvyfmSc79l5syZ8vX1dRwhISEVOzAAAAC4TLVeAtG/f3/Hnzt06KBu3bqpadOmWrNmjby9vSvtuXFxcYqNjXW8zs3NJQQDAADUENV6BvjX6tWrp5tvvlnHjh1TYGCgCgoKlJ2d7dQnMzPTsWY4MDCw1K4QJa/LWldcwtPTUzabzekAAABAzXBDBeALFy7o+PHjCgoKUpcuXeTh4aHt27c7zqelpenkyZOy2+2SJLvdrkOHDikrK8vRJzExUTabTaGhoVVePwAAAFyvWi+B+O///m/dfffdatq0qU6fPq2pU6fK3d1dDz/8sHx9fTV8+HDFxsaqQYMGstlsevLJJ2W329W9e3dJUt++fRUaGqrBgwcrPj5eGRkZmjx5smJiYuTp6eni0QEAAMAVqnUA/v777/Xwww/rhx9+kJ+fn/74xz9q37598vPzkyS9/PLLcnNz04ABA5Sfn6/IyEgtWbLEcb27u7s2bdqkMWPGyG63q3bt2oqOjtaMGTNcNSQAAAC4WLUOwO++++4Vz3t5eWnx4sVavHjxb/Zp2rSpPvjgg4ouDQAAADeoG2oNMAAAAHC9CMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUqvVvggOAq3VyRpirS3CJJlMOuboEALjhMAMMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMpZarCwAA4EZyckaYq0twiSZTDrm6BKDCMAMMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADAVAjAAAABMhQAMAAAAUyEAAwAAwFQIwAAAADCVWq4uAAAAAKWdnBHm6hJcosmUQ5X+DGaAAQAAYCrMAAMAyqXLhLddXYJLbKjr6goAXC9TzQAvXrxYzZo1k5eXl7p166b9+/e7uiQAAABUMdME4NWrVys2NlZTp07VZ599po4dOyoyMlJZWVmuLg0AAABVyDQB+KWXXtLIkSP12GOPKTQ0VMuWLZOPj4/eeOMNV5cGAACAKmSKNcAFBQVKSUlRXFyco83NzU0RERFKTk4u1T8/P1/5+fmO1zk5OZKk3NzcctdQlH+x3NfeyH70KHJ1CS5xPZ+V68VnzVz4rFU9PmuoKj9e4rNWnusMw/jdvqYIwP/5z39UVFSkgIAAp/aAgAAdPXq0VP+ZM2dq+vTppdpDQkIqrcaaqr2rC3CVmb6ursB0+KyhqvBZAyrZdX7WfvzxR/n6XvkepgjA1youLk6xsbGO18XFxTp37pwaNmwoi8XiwspuLLm5uQoJCdGpU6dks9lcXQ5qMD5rqCp81lBV+KxdO8Mw9OOPPyo4OPh3+5oiADdq1Eju7u7KzMx0as/MzFRgYGCp/p6envL09HRqq1evXmWWWKPZbDb+4UWV4LOGqsJnDVWFz9q1+b2Z3xKm+BKc1WpVly5dtH37dkdbcXGxtm/fLrvd7sLKAAAAUNVMMQMsSbGxsYqOjlbXrl112223ad68ecrLy9Njjz3m6tIAAABQhUwTgB966CGdPXtWU6ZMUUZGhjp16qQtW7aU+mIcKo6np6emTp1aajkJUNH4rKGq8FlDVeGzVrksxtXsFQEAAADUEKZYAwwAAACUIAADAADAVAjAAAAAMBUCMAAAAEyFAIwKt3v3bt19990KDg6WxWLRxo0bXV0SaqiZM2fq1ltvVd26deXv76/77rtPaWlpri4LNdDSpUvVoUMHxy8lsNvt2rx5s6vLQg03a9YsWSwWjRs3ztWl1DgEYFS4vLw8dezYUYsXL3Z1KajhkpKSFBMTo3379ikxMVGXL19W3759lZeX5+rSUMM0btxYs2bNUkpKij799FP17t1b9957r44cOeLq0lBDHThwQMuXL1eHDh1cXUqNxDZoqFQWi0UbNmzQfffd5+pSYAJnz56Vv7+/kpKS1LNnT1eXgxquQYMGmjNnjoYPH+7qUlDDXLhwQZ07d9aSJUv03HPPqVOnTpo3b56ry6pRmAEGUGPk5ORI+jmYAJWlqKhI7777rvLy8mS3211dDmqgmJgYRUVFKSIiwtWl1Fim+U1wAGq24uJijRs3Tj169FD79u1dXQ5qoEOHDslut+vSpUuqU6eONmzYoNDQUFeXhRrm3Xff1WeffaYDBw64upQajQAMoEaIiYnR4cOHtWfPHleXghqqdevWSk1NVU5OjtatW6fo6GglJSURglFhTp06pb/+9a9KTEyUl5eXq8up0VgDjErFGmBUhbFjx+rf//63du/erebNm7u6HJhERESEWrZsqeXLl7u6FNQQGzdu1P333y93d3dHW1FRkSwWi9zc3JSfn+90DuXHDDCAG5ZhGHryySe1YcMG7dq1i/CLKlVcXKz8/HxXl4EapE+fPjp06JBT22OPPaY2bdpo0qRJhN8KRABGhbtw4YKOHTvmeJ2enq7U1FQ1aNBATZo0cWFlqGliYmK0atUq/fvf/1bdunWVkZEhSfL19ZW3t7eLq0NNEhcXp/79+6tJkyb68ccftWrVKu3atUtbt251dWmoQerWrVvqOwy1a9dWw4YN+W5DBSMAo8J9+umnuuOOOxyvY2NjJUnR0dFasWKFi6pCTbR06VJJUnh4uFP7m2++qaFDh1Z9QaixsrKyNGTIEJ05c0a+vr7q0KGDtm7dqj/96U+uLg1AObAGGAAAAKbCPsAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAAAAwFQIwAAAATIUADAAAAFMhAAMAAMBUCMAAUAPt3btXYWFh8vDw0H333Velzz5x4oQsFotSU1Or9LkAcLUIwABwlYYOHSqLxaJZs2Y5tW/cuFEWi6VKati0aZN69eqlunXrysfHR7feemuZv2I8NjZWnTp1Unp6ulasWOEIpSVHw4YN1bdvX33++edVUvf1atasmebNm+fqMgDUEARgALgGXl5emj17ts6fP1/lz164cKHuvfde9ejRQ5988okOHjyogQMHavTo0frv//5vp77Hjx9X79691bhxY9WrV8/Rvm3bNp05c0Zbt27VhQsX1L9/f2VnZ5f5vMuXL1fiaADAdQjAAHANIiIiFBgYqJkzZ5Z5ftq0aerUqZNT27x589SsWTPH66FDh+q+++7TCy+8oICAANWrV08zZsxQYWGhJkyYoAYNGqhx48Z68803HdecOnVKTz/9tMaNG6cXXnhBoaGhatWqlZ5++mnNmTNHc+fO1SeffOKY6f3hhx80bNgwWSwWpxnihg0bKjAwUF27dtWLL76ozMxMp+tWr16tXr16ycvLSytXrlRxcbFmzJihxo0by9PTU506ddKWLVucxrd//37dcsst8vLyUteuXUvNKq9YscIphEtlz5q///77uvXWW+Xl5aVGjRrp/vvvlySFh4fru+++0/jx4x0z2JL03Xff6e6771b9+vVVu3ZttWvXTh988MFv/uwAoAQBGACugbu7u1544QUtXLhQ33//fbnvs2PHDp0+fVq7d+/WSy+9pKlTp+quu+5S/fr19cknn2j06NF6/PHHHc9Yt26dLl++XGqmV5Ief/xx1alTR//85z8VEhKiM2fOyGazad68eTpz5oweeuihMmvw9vaWJBUUFDja/va3v+mvf/2rvvrqK0VGRmr+/PmaO3euXnzxRR08eFCRkZG655579M0330iSLly4oLvuukuhoaFKSUnRtGnTyqzx9yQkJOj+++/XnXfeqc8//1zbt2/XbbfdJklav369GjdurBkzZujMmTM6c+aMJCkmJkb5+fnavXu3Dh06pNmzZ6tOnTrX/GwA5lPL1QUAwI3m/vvvV6dOnTR16lS9/vrr5bpHgwYNtGDBArm5ual169aKj4/XTz/9pGeeeUaSFBcXp1mzZmnPnj0aOHCgvv76a/n6+iooKKjUvaxWq1q0aKGvv/5a7u7uCgwMlMVika+vrwIDA8t8fnZ2tp599lnVqVNHt912my5evChJGjdunB544AFHvxdffFGTJk3SwIEDJUmzZ8/Wzp07NW/ePC1evFirVq1ScXGxXn/9dXl5ealdu3b6/vvvNWbMmGt6P55//nkNHDhQ06dPd7R17NjR8V65u7urbt26TuM5efKkBgwYoLCwMElSixYtrumZAMyLGWAAKIfZs2frrbfe0ldffVWu69u1ayc3t//7V3BAQIAjyEk/zzQ3bNhQWVlZ113rL/3hD39QnTp1VL9+fX3xxRdavXq1AgICHOe7du3q+HNubq5Onz6tHj16ON2jR48ejnF/9dVX6tChg7y8vBzn7Xb7NdeVmpqqPn36XNM1Tz31lJ577jn16NFDU6dO1cGDB6/5uQDMiQAMAOXQs2dPRUZGKi4uzqndzc1NhmE4tZX1ZTIPDw+n1xaLpcy24uJiSdLNN9+snJwcnT59utS9CgoKdPz4cd18882/W/fq1av1xRdf6Pz58zp+/LjuvPNOp/O1a9f+3Xtcq6t5T0qWY1yLESNG6Ntvv9XgwYN16NAhde3aVQsXLryuWgGYAwEYAMpp1qxZev/995WcnOxo8/PzU0ZGhlPgq4j9cAcMGCAPDw/NnTu31Llly5YpLy9PDz/88O/eJyQkRC1btiz1pbSy2Gw2BQcHa+/evU7te/fuVWhoqCSpbdu2OnjwoC5duuQ4v2/fPqf+fn5++vHHH5WXl+do+/V70qFDB23fvv03a7FarSoqKipzPKNHj9b69ev19NNP69VXX/3dcQEAARgAyiksLEyDBg3SggULHG3h4eE6e/as4uPjdfz4cS1evFibN2++7mc1adJE8fHxmjdvnv7+97/r6NGjOn78uF566SVNnDhRTz/9tLp163bdz/m1CRMmaPbs2Vq9erXS0tL0t7/9TampqfrrX/8qSXrkkUdksVg0cuRIffnll/rggw/04osvOt2jW7du8vHx0TPPPKPjx49r1apVpfYunjp1qv75z39q6tSp+uqrrxxfaivRrFkz7d69W//7v/+r//znP5J+Xq+8detWpaen67PPPtPOnTvVtm3bCn8PANQ8BGAAuA4zZsxwLFOQfp4RXbJkiRYvXqyOHTtq//795doVoSzjxo3Thg0b9NFHH6lr165q3769Vq1apaVLl5YKnRXlqaeeUmxsrJ5++mmFhYVpy5Yteu+993TTTTdJkurUqaP3339fhw4d0i233KK///3vTsFV+vlLbO+8844++OADhYWF6Z///KemTZvm1Cc8PFxr167Ve++9p06dOql3797av3+/4/yMGTN04sQJtWzZUn5+fpKkoqIixcTEqG3bturXr59uvvlmLVmypFLeBwA1i8X49cIsAAAAoAZjBhgAAACmQgAGAACAqRCAAQAAYCoEYAAAAJgKARgAAACmQgAGAACAqRCAAQAAYCoEYAAAAJgKARgAAACmQgAGAACAqRCAAQAAYCr/DzeSnEJ+MUvvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "churn_rate_NumOfProducts = df.groupby('NumOfProducts')['Exited'].mean()\n",
        "print(churn_rate_NumOfProducts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VygldXvx5DYJ",
        "outputId": "bbd6a747-dc56-40a6-d33d-f338681d457e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NumOfProducts\n",
            "1    0.277144\n",
            "2    0.076035\n",
            "3    0.827068\n",
            "4    1.000000\n",
            "Name: Exited, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The number of products held by customers can impact churn. Customers with fewer products may be less likely to churn, while those with more products may have a higher churn rate."
      ],
      "metadata": {
        "id": "pAugyqmJ1CSk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn by Credit Card\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='HasCrCard', hue='Exited', data=df)\n",
        "plt.title('Churn by Credit Card')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zKNL7fjR0mmI",
        "outputId": "6e89324e-bbf6-4116-bb79-27593c3aa159"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "churn_rate_HasCrCard = df.groupby('HasCrCard')['Exited'].mean()\n",
        "print(churn_rate_HasCrCard)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHwAQ5TU4Ijm",
        "outputId": "1afe3e88-2f8e-46aa-d9f3-12a1ec51a02b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HasCrCard\n",
            "0    0.208149\n",
            "1    0.201984\n",
            "Name: Exited, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Having a credit card does not seem to have a significant impact on customer churn. The churn rate appears similar for customers with and without a credit card."
      ],
      "metadata": {
        "id": "DNl2-Zmz3DOj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn by Activity Status\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='IsActiveMember', hue='Exited', data=df)\n",
        "plt.title('Churn by Activity Status')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "M1KDvnPe0rPn",
        "outputId": "6ac5107c-863f-4038-85f7-ba31a3e4aca9"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "churn_rate_IsActiveMember = df.groupby('IsActiveMember')['Exited'].mean()\n",
        "print(churn_rate_IsActiveMember)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PsiPvbn35Qra",
        "outputId": "549fe7b5-c765-43c7-deda-d701920b3aca"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IsActiveMember\n",
            "0    0.268715\n",
            "1    0.142691\n",
            "Name: Exited, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Customer activity status is another important factor. Active members appear to have a lower churn rate compared to inactive members."
      ],
      "metadata": {
        "id": "SQDv4uDi2i3K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Churn by Balance:\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Exited', y='Balance', data=df)\n",
        "plt.title('Churn by Balance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "i6VBe09g02m7",
        "outputId": "40558987-7126-4ffa-c66d-4d56e283f720"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "nBpckFyzZjET",
        "outputId": "a6292fda-c929-4f36-d0d9-92cf4f6ddbbb"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    CreditScore       Age    Tenure   Balance  NumOfProducts  \\\n",
              "CreditScore            1.000000 -0.003965  0.000842  0.006268       0.012238   \n",
              "Age                   -0.003965  1.000000 -0.009997  0.028308      -0.030680   \n",
              "Tenure                 0.000842 -0.009997  1.000000 -0.012254       0.013444   \n",
              "Balance                0.006268  0.028308 -0.012254  1.000000      -0.304180   \n",
              "NumOfProducts          0.012238 -0.030680  0.013444 -0.304180       1.000000   \n",
              "HasCrCard             -0.005458 -0.011721  0.022583 -0.014858       0.003183   \n",
              "IsActiveMember         0.025651  0.085472 -0.028362 -0.010084       0.009612   \n",
              "EstimatedSalary       -0.001384 -0.007201  0.007784  0.012797       0.014204   \n",
              "Exited                -0.026771  0.285296 -0.013656  0.118577      -0.047611   \n",
              "Complain              -0.027048  0.283530 -0.013047  0.119124      -0.046357   \n",
              "Satisfaction Score    -0.012599 -0.000876  0.008663 -0.002588      -0.011394   \n",
              "Point Earned           0.000077  0.002222 -0.010196  0.014608      -0.015330   \n",
              "\n",
              "                    HasCrCard  IsActiveMember  EstimatedSalary    Exited  \\\n",
              "CreditScore         -0.005458        0.025651        -0.001384 -0.026771   \n",
              "Age                 -0.011721        0.085472        -0.007201  0.285296   \n",
              "Tenure               0.022583       -0.028362         0.007784 -0.013656   \n",
              "Balance             -0.014858       -0.010084         0.012797  0.118577   \n",
              "NumOfProducts        0.003183        0.009612         0.014204 -0.047611   \n",
              "HasCrCard            1.000000       -0.011866        -0.009933 -0.006976   \n",
              "IsActiveMember      -0.011866        1.000000        -0.011421 -0.156356   \n",
              "EstimatedSalary     -0.009933       -0.011421         1.000000  0.012490   \n",
              "Exited              -0.006976       -0.156356         0.012490  1.000000   \n",
              "Complain            -0.007095       -0.154741         0.012494  0.995693   \n",
              "Satisfaction Score  -0.000212        0.010236        -0.013747 -0.005849   \n",
              "Point Earned        -0.011041       -0.004983        -0.001515 -0.004628   \n",
              "\n",
              "                    Complain  Satisfaction Score  Point Earned  \n",
              "CreditScore        -0.027048           -0.012599      0.000077  \n",
              "Age                 0.283530           -0.000876      0.002222  \n",
              "Tenure             -0.013047            0.008663     -0.010196  \n",
              "Balance             0.119124           -0.002588      0.014608  \n",
              "NumOfProducts      -0.046357           -0.011394     -0.015330  \n",
              "HasCrCard          -0.007095           -0.000212     -0.011041  \n",
              "IsActiveMember     -0.154741            0.010236     -0.004983  \n",
              "EstimatedSalary     0.012494           -0.013747     -0.001515  \n",
              "Exited              0.995693           -0.005849     -0.004628  \n",
              "Complain            1.000000           -0.004799     -0.002893  \n",
              "Satisfaction Score -0.004799            1.000000     -0.014400  \n",
              "Point Earned       -0.002893           -0.014400      1.000000  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CreditScore</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003965</td>\n",
              "      <td>0.000842</td>\n",
              "      <td>0.006268</td>\n",
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              "      <td>0.000077</td>\n",
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              "      <td>0.002222</td>\n",
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              "      <th>Tenure</th>\n",
              "      <td>0.000842</td>\n",
              "      <td>-0.009997</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.012254</td>\n",
              "      <td>0.013444</td>\n",
              "      <td>0.022583</td>\n",
              "      <td>-0.028362</td>\n",
              "      <td>0.007784</td>\n",
              "      <td>-0.013656</td>\n",
              "      <td>-0.013047</td>\n",
              "      <td>0.008663</td>\n",
              "      <td>-0.010196</td>\n",
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              "    <tr>\n",
              "      <th>Balance</th>\n",
              "      <td>0.006268</td>\n",
              "      <td>0.028308</td>\n",
              "      <td>-0.012254</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.304180</td>\n",
              "      <td>-0.014858</td>\n",
              "      <td>-0.010084</td>\n",
              "      <td>0.012797</td>\n",
              "      <td>0.118577</td>\n",
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              "      <td>-0.002588</td>\n",
              "      <td>0.014608</td>\n",
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              "      <th>NumOfProducts</th>\n",
              "      <td>0.012238</td>\n",
              "      <td>-0.030680</td>\n",
              "      <td>0.013444</td>\n",
              "      <td>-0.304180</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003183</td>\n",
              "      <td>0.009612</td>\n",
              "      <td>0.014204</td>\n",
              "      <td>-0.047611</td>\n",
              "      <td>-0.046357</td>\n",
              "      <td>-0.011394</td>\n",
              "      <td>-0.015330</td>\n",
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              "      <th>HasCrCard</th>\n",
              "      <td>-0.005458</td>\n",
              "      <td>-0.011721</td>\n",
              "      <td>0.022583</td>\n",
              "      <td>-0.014858</td>\n",
              "      <td>0.003183</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.011866</td>\n",
              "      <td>-0.009933</td>\n",
              "      <td>-0.006976</td>\n",
              "      <td>-0.007095</td>\n",
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              "      <td>-0.010084</td>\n",
              "      <td>0.009612</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>-0.011421</td>\n",
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              "      <td>0.010236</td>\n",
              "      <td>-0.004983</td>\n",
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              "      <th>EstimatedSalary</th>\n",
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              "      <td>-0.007201</td>\n",
              "      <td>0.007784</td>\n",
              "      <td>0.012797</td>\n",
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              "      <td>-0.009933</td>\n",
              "      <td>-0.011421</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.006976</td>\n",
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              "      <td>0.119124</td>\n",
              "      <td>-0.046357</td>\n",
              "      <td>-0.007095</td>\n",
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              "      <td>-0.004799</td>\n",
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              "    }\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-e968b0f6-1949-44f9-ae94-6dfbc5641095 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-e968b0f6-1949-44f9-ae94-6dfbc5641095');\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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(figsize=(13,10))\n",
        "sns.heatmap(df.corr(),annot=True,linewidths=0.5,ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "0stQnqDRJPFP",
        "outputId": "cdec20ea-088f-4c0d-e94e-83af00b1b7bd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Scaling Data"
      ],
      "metadata": {
        "id": "N2krBCk555cN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "geo=LabelEncoder()\n",
        "gen=LabelEncoder()\n",
        "ca=LabelEncoder()\n",
        "\n",
        "df['Geography']=geo.fit_transform(df['Geography'])\n",
        "df['Gender']=gen.fit_transform(df['Gender'])\n",
        "df['Card Type']=ca.fit_transform(df['Card Type'])"
      ],
      "metadata": {
        "id": "HYlFhEDUAIhw"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.drop(['Exited','AgeGroup'],axis=1)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "OcIRKMB15m_D",
        "outputId": "8a6e72bb-2926-4e1f-bb03-dcac75b8975e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      CreditScore  Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
              "0             619          0       0   42       2       0.00              1   \n",
              "1             608          2       0   41       1   83807.86              1   \n",
              "2             502          0       0   42       8  159660.80              3   \n",
              "3             699          0       0   39       1       0.00              2   \n",
              "4             850          2       0   43       2  125510.82              1   \n",
              "...           ...        ...     ...  ...     ...        ...            ...   \n",
              "9995          771          0       1   39       5       0.00              2   \n",
              "9996          516          0       1   35      10   57369.61              1   \n",
              "9997          709          0       0   36       7       0.00              1   \n",
              "9998          772          1       1   42       3   75075.31              2   \n",
              "9999          792          0       0   28       4  130142.79              1   \n",
              "\n",
              "      HasCrCard  IsActiveMember  EstimatedSalary  Complain  \\\n",
              "0             1               1        101348.88         1   \n",
              "1             0               1        112542.58         1   \n",
              "2             1               0        113931.57         1   \n",
              "3             0               0         93826.63         0   \n",
              "4             1               1         79084.10         0   \n",
              "...         ...             ...              ...       ...   \n",
              "9995          1               0         96270.64         0   \n",
              "9996          1               1        101699.77         0   \n",
              "9997          0               1         42085.58         1   \n",
              "9998          1               0         92888.52         1   \n",
              "9999          1               0         38190.78         0   \n",
              "\n",
              "      Satisfaction Score  Card Type  Point Earned  \n",
              "0                      2          0           464  \n",
              "1                      3          0           456  \n",
              "2                      3          0           377  \n",
              "3                      5          1           350  \n",
              "4                      5          1           425  \n",
              "...                  ...        ...           ...  \n",
              "9995                   1          0           300  \n",
              "9996                   5          2           771  \n",
              "9997                   3          3           564  \n",
              "9998                   2          1           339  \n",
              "9999                   3          0           911  \n",
              "\n",
              "[10000 rows x 14 columns]"
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              "      <th>CreditScore</th>\n",
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              "      <th>0</th>\n",
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              "      <td>159660.80</td>\n",
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              "      <th>4</th>\n",
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              "      <td>79084.10</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>9996</th>\n",
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              "      <td>35</td>\n",
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              "      <td>5</td>\n",
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              "      <td>771</td>\n",
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              "</table>\n",
              "<p>10000 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70301a69-9c2d-4c74-a463-295bcb62b94f')\"\n",
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              "              style=\"display:none;\">\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-718f3926-4519-43c9-bf0a-50db67051539 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\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-70301a69-9c2d-4c74-a463-295bcb62b94f 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-70301a69-9c2d-4c74-a463-295bcb62b94f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['Exited']\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-HDTOOh6qxt",
        "outputId": "3d303ef3-6e6a-4cbe-d9d4-b19b58bf4158"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       1\n",
              "1       0\n",
              "2       1\n",
              "3       0\n",
              "4       0\n",
              "       ..\n",
              "9995    0\n",
              "9996    0\n",
              "9997    1\n",
              "9998    1\n",
              "9999    0\n",
              "Name: Exited, Length: 10000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mm=MinMaxScaler()\n",
        "X_sc=mm.fit_transform(X)\n",
        "X_sc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSKjvWOb8Gac",
        "outputId": "7d1254b5-8f14-4f15-9de1-b545342239f6"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.538     , 0.        , 0.        , ..., 0.25      , 0.        ,\n",
              "        0.39160045],\n",
              "       [0.516     , 1.        , 0.        , ..., 0.5       , 0.        ,\n",
              "        0.38251986],\n",
              "       [0.304     , 0.        , 0.        , ..., 0.5       , 0.        ,\n",
              "        0.29284904],\n",
              "       ...,\n",
              "       [0.718     , 0.        , 0.        , ..., 0.5       , 1.        ,\n",
              "        0.50510783],\n",
              "       [0.844     , 0.5       , 1.        , ..., 0.25      , 0.33333333,\n",
              "        0.24971623],\n",
              "       [0.884     , 0.        , 0.        , ..., 0.5       , 0.        ,\n",
              "        0.89897843]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#6. Spliting Data into Training and Test Set"
      ],
      "metadata": {
        "id": "GfIQBVi-BZGi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X_sc,y,test_size=0.3,random_state=1)"
      ],
      "metadata": {
        "id": "wiqlrxeH8ZCr"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#7. Model Training and Check Accuracy Score, Print Classification Report and ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "UXDWO_7bBfie"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "(i)KNeighbors Classifier"
      ],
      "metadata": {
        "id": "pDl659MmBjzn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn=KNeighborsClassifier(n_neighbors=11)\n",
        "knn.fit(X_train,y_train)\n",
        "y_pred_knn=knn.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_knn)*100)\n",
        "print(classification_report(y_test,y_pred_knn,digits=4))\n",
        "print(ConfusionMatrixDisplay.from_predictions(y_test,y_pred_knn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "sTl3tmJQBgW8",
        "outputId": "9101e9ae-c552-4702-cfc6-102bae168a17"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.83333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9992    0.9987    0.9989      2373\n",
            "           1     0.9952    0.9968    0.9960       627\n",
            "\n",
            "    accuracy                         0.9983      3000\n",
            "   macro avg     0.9972    0.9978    0.9975      3000\n",
            "weighted avg     0.9983    0.9983    0.9983      3000\n",
            "\n",
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x78ebff646a10>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(ii)Support Vector Classifier(SVC)"
      ],
      "metadata": {
        "id": "DqMa_NQKB2qn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sv=SVC(kernel='poly')\n",
        "sv.fit(X_train,y_train)\n",
        "y_pred_sv=sv.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_sv)*100)\n",
        "print(classification_report(y_test,y_pred_sv,digits=4))\n",
        "print(ConfusionMatrixDisplay.from_predictions(y_test,y_pred_sv))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "ljmrFGbeBx7b",
        "outputId": "480670a4-a9d8-4a08-fc7e-ef9561a7b0d1"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.83333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9992    0.9987    0.9989      2373\n",
            "           1     0.9952    0.9968    0.9960       627\n",
            "\n",
            "    accuracy                         0.9983      3000\n",
            "   macro avg     0.9972    0.9978    0.9975      3000\n",
            "weighted avg     0.9983    0.9983    0.9983      3000\n",
            "\n",
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x78ebff647130>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(iii)GaussianNB Classification"
      ],
      "metadata": {
        "id": "PnxoOY84CCY1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nb=GaussianNB()\n",
        "nb.fit(X_train,y_train)\n",
        "y_pred_nb=nb.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_nb)*100)\n",
        "print(classification_report(y_test,y_pred_nb,digits=4))\n",
        "print(ConfusionMatrixDisplay.from_predictions(y_test,y_pred_nb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "gB-HAV3RB-wB",
        "outputId": "3f789da6-1424-4367-b8e4-88757e3505f1"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.83333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9992    0.9987    0.9989      2373\n",
            "           1     0.9952    0.9968    0.9960       627\n",
            "\n",
            "    accuracy                         0.9983      3000\n",
            "   macro avg     0.9972    0.9978    0.9975      3000\n",
            "weighted avg     0.9983    0.9983    0.9983      3000\n",
            "\n",
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x78ebffbf7940>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(iv)Decision Tree Classifier"
      ],
      "metadata": {
        "id": "z_K3Fo9WCOLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf=DecisionTreeClassifier(criterion='gini')\n",
        "clf.fit(X_train,y_train)\n",
        "y_pred_clf=clf.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_clf)*100)\n",
        "print(classification_report(y_test,y_pred_clf,digits=4))\n",
        "print(ConfusionMatrixDisplay.from_predictions(y_test,y_pred_clf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "TjsREd6dCJyc",
        "outputId": "a9adf862-28da-4d5c-a207-1ba8bf79c354"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.76666666666667\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9983    0.9987    0.9985      2373\n",
            "           1     0.9952    0.9936    0.9944       627\n",
            "\n",
            "    accuracy                         0.9977      3000\n",
            "   macro avg     0.9968    0.9962    0.9965      3000\n",
            "weighted avg     0.9977    0.9977    0.9977      3000\n",
            "\n",
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x78ebffcadc30>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(v)Random Forest Classifier"
      ],
      "metadata": {
        "id": "uCqpwi5ACWqM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf=RandomForestClassifier(n_estimators=50,random_state=1)\n",
        "rf.fit(X_train,y_train)\n",
        "y_pred_rf=rf.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_rf)*100)\n",
        "print(classification_report(y_test,y_pred_rf,digits=4))\n",
        "print(ConfusionMatrixDisplay.from_predictions(y_test,y_pred_rf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "aNN8r9IdCV_0",
        "outputId": "2ea6137f-2b39-4e7b-8714-b71b2c40ebdc"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.83333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9992    0.9987    0.9989      2373\n",
            "           1     0.9952    0.9968    0.9960       627\n",
            "\n",
            "    accuracy                         0.9983      3000\n",
            "   macro avg     0.9972    0.9978    0.9975      3000\n",
            "weighted avg     0.9983    0.9983    0.9983      3000\n",
            "\n",
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x78ebff647250>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(vi)AdaBoost Classifier"
      ],
      "metadata": {
        "id": "x1-Dc3fqCgNK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ab=AdaBoostClassifier(random_state=1)\n",
        "ab.fit(X_train,y_train)\n",
        "y_pred_ab=ab.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_ab)*100)\n",
        "print(classification_report(y_test,y_pred_ab,digits=4))\n",
        "print(ConfusionMatrixDisplay.from_predictions(y_test,y_pred_ab))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "iZAGgE4kCfNb",
        "outputId": "51d49d1e-4325-4076-9605-b3065b0714af"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.83333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9992    0.9987    0.9989      2373\n",
            "           1     0.9952    0.9968    0.9960       627\n",
            "\n",
            "    accuracy                         0.9983      3000\n",
            "   macro avg     0.9972    0.9978    0.9975      3000\n",
            "weighted avg     0.9983    0.9983    0.9983      3000\n",
            "\n",
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x78ebffa02590>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(vii)LGBMClassifier"
      ],
      "metadata": {
        "id": "CJ9jc2VtzhkU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lgbm=LGBMClassifier()\n",
        "lgbm.fit(X_train,y_train)\n",
        "y_pred_lgbm=lgbm.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_lgbm)*100)\n",
        "print(classification_report(y_test,y_pred_lgbm,digits=4))\n",
        "print(ConfusionMatrixDisplay.from_predictions(y_test,y_pred_lgbm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "i-QcwXuYzgy9",
        "outputId": "3558534c-6e85-4ddb-d98f-dcaba30865d9"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.83333333333333\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9992    0.9987    0.9989      2373\n",
            "           1     0.9952    0.9968    0.9960       627\n",
            "\n",
            "    accuracy                         0.9983      3000\n",
            "   macro avg     0.9972    0.9978    0.9975      3000\n",
            "weighted avg     0.9983    0.9983    0.9983      3000\n",
            "\n",
            "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x78ebffa01630>\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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nVVpaal9dAM/6YH/maY83j67SY/9Xs9/en129+7THe/y5RC+m/VDT0ACvmfcg84/MgNUEznk9GfjrX/+qQ4cOadq0acrPz1fPnj21bt26kyYVAgDgDtoEznk9GZCkcePG0RYAAMBL6kUyAABAbePZBM6RDAAATIE2gXO+OxsCAADUCJUBAIApUBlwjmQAAGAKJAPO0SYAAMDkqAwAAEyByoBzJAMAAFMw5N7yQF9+bBXJAADAFKgMOMecAQAATI7KAADAFKgMOEcyAAAwBZIB52gTAABgclQGAACmQGXAOZIBAIApGIZFhhtf6O6cW9/RJgAAwOSoDAAATMEmi1s3HXLn3PqOZAAAYArMGXCONgEAACZHZQAAYApMIHSOZAAAYAq0CZwjGQAAmAKVAeeYMwAAgMlRGQAAmILhZpvAlysDJAMAAFMwJBmGe+f7KtoEAACYHJUBAIAp2GSRhTsQnhLJAADAFFhN4BxtAgAATI7KAADAFGyGRRZuOnRKJAMAAFMwDDdXE/jwcgLaBAAAmBzJAADAFKonELqzuWLz5s0aMmSIYmJiZLFYtGrVKofjt912mywWi8M2cOBAhzGFhYUaOXKkwsLCFBERodGjR6ukpMRhzNdff60+ffooKChIsbGxmjt3rst/NyQDAABTqOtkoLS0VD169NCCBQucjhk4cKAOHDhg39544w2H4yNHjtTOnTuVlpamNWvWaPPmzbrzzjvtx4uLizVgwADFxcUpIyNDTz75pGbMmKGXXnrJpViZMwAAMIW6nkA4aNAgDRo06LRjAgMDFR0dfcpju3bt0rp16/TFF1+oV69ekqTnn39eV111lZ566inFxMRo6dKlqqio0KuvvqqAgACdf/75yszM1DPPPOOQNJwJlQEAAFxQXFzssJWXl5/1tTZu3KhWrVqpU6dOuvvuu3X48GH7sfT0dEVERNgTAUlKSkqSn5+ftm3bZh/Tt29fBQQE2MckJycrKytLR44cqXEcJAMAAFOoXk3gziZJsbGxCg8Pt29z5sw5q3gGDhyoJUuWaP369frnP/+pTZs2adCgQbJarZKk/Px8tWrVyuGcRo0aKTIyUvn5+fYxUVFRDmOqX1ePqQnaBAAAUzjxhe7OHQhP/DcvL09hYWH2/YGBgWd1vREjRtj/3K1bN3Xv3l3nnnuuNm7cqCuuuOKs4zwbVAYAAHBBWFiYw3a2ycAfdejQQS1atFB2drYkKTo6WgcPHnQYU1VVpcLCQvs8g+joaBUUFDiMqX7tbC7CqZAMAABMoa5XE7hq7969Onz4sFq3bi1JSkxM1NGjR5WRkWEfs2HDBtlsNvXu3ds+ZvPmzaqsrLSPSUtLU6dOndSsWbMavzfJAADAFAwPbK4oKSlRZmamMjMzJUk5OTnKzMxUbm6uSkpKNGnSJH322Wf66aeftH79eg0dOlTx8fFKTk6WJHXp0kUDBw7UmDFj9Pnnn+vTTz/VuHHjNGLECMXExEiSbrrpJgUEBGj06NHauXOnli9frueee04TJkxwKVaSAQAAasH27dt14YUX6sILL5QkTZgwQRdeeKGmTZsmf39/ff3117rmmmvUsWNHjR49WgkJCdqyZYtD22Hp0qXq3LmzrrjiCl111VW67LLLHO4hEB4erg8//FA5OTlKSEjQgw8+qGnTprm0rFBiAiEAwCTq+hHG/fr1k3GaBxp88MEHZ7xGZGSkli1bdtox3bt315YtW1yK7Y9IBgAA5nA2tf4/nu+jSAYAAObg7iRAH36EMXMGAAAwOSoDAABT+N+7CJ7t+b6KZAAAYAp1PYGwIaFNAACAyVEZAACYg2FxbxKgD1cGSAYAAKbAnAHnaBMAAGByVAYAAObATYecIhkAAJgCqwmcq1Ey8N5779X4gtdcc81ZBwMAAOpejZKBYcOG1ehiFotFVqvVnXgAAKg9Plzqd0eNkgGbzVbbcQAAUKtoEzjn1mqCsrIyT8UBAEDtMjyw+SiXkwGr1arZs2frnHPOUUhIiH788UdJ0tSpU/XKK694PEAAAFC7XE4GHn/8caWmpmru3LkKCAiw77/gggu0ePFijwYHAIDnWDyw+SaXk4ElS5bopZde0siRI+Xv72/f36NHD33//fceDQ4AAI+hTeCUy8nAvn37FB8ff9J+m82myspKjwQFAADqjsvJQNeuXbVly5aT9r/99tu68MILPRIUAAAeR2XAKZfvQDht2jSlpKRo3759stlsevfdd5WVlaUlS5ZozZo1tREjAADu46mFTrlcGRg6dKhWr16tjz76SE2bNtW0adO0a9curV69WldeeWVtxAgAAGrRWT2boE+fPkpLS/N0LAAA1BoeYezcWT+oaPv27dq1a5ekE/MIEhISPBYUAAAex1MLnXI5Gdi7d69uvPFGffrpp4qIiJAkHT16VH/+85/15ptvqk2bNp6OEQAA1CKX5wzccccdqqys1K5du1RYWKjCwkLt2rVLNptNd9xxR23ECACA+6onELqz+SiXKwObNm3S1q1b1alTJ/u+Tp066fnnn1efPn08GhwAAJ5iMU5s7pzvq1xOBmJjY095cyGr1aqYmBiPBAUAgMcxZ8Apl9sETz75pO69915t377dvm/79u26//779dRTT3k0OAAAUPtqVBlo1qyZLJbfeyWlpaXq3bu3GjU6cXpVVZUaNWqk22+/XcOGDauVQAEAcAs3HXKqRsnAs88+W8thAABQy2gTOFWjZCAlJaW24wAAAF5y1jcdkqSysjJVVFQ47AsLC3MrIAAAagWVAadcnkBYWlqqcePGqVWrVmratKmaNWvmsAEAUC/x1EKnXE4GHnroIW3YsEELFy5UYGCgFi9erJkzZyomJkZLliypjRgBAEAtcrlNsHr1ai1ZskT9+vXTqFGj1KdPH8XHxysuLk5Lly7VyJEjayNOAADcw2oCp1yuDBQWFqpDhw6STswPKCwslCRddtll2rx5s2ejAwDAQ6rvQOjO5qtcTgY6dOignJwcSVLnzp21YsUKSScqBtUPLgIAAA2Hy8nAqFGj9NVXX0mSpkyZogULFigoKEjjx4/XpEmTPB4gAAAewQRCp1yeMzB+/Hj7n5OSkvT9998rIyND8fHx6t69u0eDAwAAtc+t+wxIUlxcnOLi4jwRCwAAtcYiN59a6LFI6p8aJQPz58+v8QXvu+++sw4GAADUvRolA/PmzavRxSwWi1eSgWs7dlMjS+M6f1+gLpRe39vbIQC1pqqyTFr1n7p5M5YWOlWjZKB69QAAAA0WtyN2yuXVBAAAwLe4PYEQAIAGgcqAUyQDAABTcPcugtyBEAAA+CwqAwAAc6BN4NRZVQa2bNmim2++WYmJidq3b58k6d///rc++eQTjwYHAIDHcDtip1xOBt555x0lJycrODhYX375pcrLyyVJRUVFeuKJJzweIAAAqF0uJwOPPfaYFi1apJdfflmNG/9+o59LL71UO3bs8GhwAAB4Co8wds7lOQNZWVnq27fvSfvDw8N19OhRT8QEAIDncQdCp1yuDERHRys7O/uk/Z988ok6dOjgkaAAAPA45gw45XIyMGbMGN1///3atm2bLBaL9u/fr6VLl2rixIm6++67ayNGAABQi1xuE0yZMkU2m01XXHGFjh8/rr59+yowMFATJ07UvffeWxsxAgDgNm465JzLyYDFYtEjjzyiSZMmKTs7WyUlJeratatCQkJqIz4AADyD+ww4ddY3HQoICFDXrl09GQsAAPACl5OB/v37y2JxPqNyw4YNbgUEAECtcHd5IJWB3/Xs2dPhdWVlpTIzM/Xtt98qJSXFU3EBAOBZtAmccjkZmDdv3in3z5gxQyUlJW4HBAAA6pbHnlp4880369VXX/XU5QAA8CzuM+CUx55amJ6erqCgIE9dDgAAj2JpoXMuJwPDhw93eG0Yhg4cOKDt27dr6tSpHgsMAADUDZeTgfDwcIfXfn5+6tSpk2bNmqUBAwZ4LDAAAFA3XEoGrFarRo0apW7duqlZs2a1FRMAAJ7HagKnXJpA6O/vrwEDBvB0QgBAg8MjjJ1zeTXBBRdcoB9//LE2YgEAwGds3rxZQ4YMUUxMjCwWi1atWuVw3DAMTZs2Ta1bt1ZwcLCSkpK0e/duhzGFhYUaOXKkwsLCFBERodGjR5+0jP/rr79Wnz59FBQUpNjYWM2dO9flWF1OBh577DFNnDhRa9as0YEDB1RcXOywAQBQb9XhssLS0lL16NFDCxYsOOXxuXPnav78+Vq0aJG2bdumpk2bKjk5WWVlZfYxI0eO1M6dO5WWlqY1a9Zo8+bNuvPOO+3Hi4uLNWDAAMXFxSkjI0NPPvmkZsyYoZdeesmlWGs8Z2DWrFl68MEHddVVV0mSrrnmGofbEhuGIYvFIqvV6lIAAADUiTqeMzBo0CANGjTo1JcyDD377LN69NFHNXToUEnSkiVLFBUVpVWrVmnEiBHatWuX1q1bpy+++EK9evWSJD3//PO66qqr9NRTTykmJkZLly5VRUWFXn31VQUEBOj8889XZmamnnnmGYek4UxqnAzMnDlTd911lz7++OMaXxwAAF/zxyp4YGCgAgMDXbpGTk6O8vPzlZSUZN8XHh6u3r17Kz09XSNGjFB6eroiIiLsiYAkJSUlyc/PT9u2bdO1116r9PR09e3bVwEBAfYxycnJ+uc//6kjR47UeLJ/jZMBwziREl1++eU1PQUAgHrDUzcdio2Nddg/ffp0zZgxw6Vr5efnS5KioqIc9kdFRdmP5efnq1WrVg7HGzVqpMjISIcx7du3P+ka1cc8ngxIOu3TCgEAqNc81CbIy8tTWFiYfberVYH6yKVkoGPHjmdMCAoLC90KCACA+iwsLMwhGTgb0dHRkqSCggK1bt3avr+goMD+dODo6GgdPHjQ4byqqioVFhbaz4+OjlZBQYHDmOrX1WNqwqVkYObMmSfdgRAAgIagPj2boH379oqOjtb69evtX/7FxcXatm2b7r77bklSYmKijh49qoyMDCUkJEiSNmzYIJvNpt69e9vHPPLII6qsrFTjxo0lSWlpaerUqZNLNwd0KRkYMWLESf0LAAAahDpeTVBSUqLs7Gz765ycHGVmZioyMlJt27bVAw88oMcee0znnXee2rdvr6lTpyomJkbDhg2TJHXp0kUDBw7UmDFjtGjRIlVWVmrcuHEaMWKEYmJiJEk33XSTZs6cqdGjR2vy5Mn69ttv9dxzz2nevHkuxVrjZID5AgAA1Nz27dvVv39/++sJEyZIklJSUpSamqqHHnpIpaWluvPOO3X06FFddtllWrduncMTgJcuXapx48bpiiuukJ+fn6677jrNnz/ffjw8PFwffvihxo4dq4SEBLVo0ULTpk1zaVmhdBarCQAAaJDquDLQr1+/0353WiwWzZo1S7NmzXI6JjIyUsuWLTvt+3Tv3l1btmxxLbg/qHEyYLPZ3HojAAC8qT7NGahvXH6EMQAADRJPLXTK5WcTAAAA30JlAABgDlQGnCIZAACYAnMGnKNNAACAyVEZAACYA20Cp0gGAACmQJvAOdoEAACYHJUBAIA50CZwimQAAGAOJANO0SYAAMDkqAwAAEzB8tvmzvm+imQAAGAOtAmcIhkAAJgCSwudY84AAAAmR2UAAGAOtAmcIhkAAJiHD3+hu4M2AQAAJkdlAABgCkwgdI5kAABgDswZcIo2AQAAJkdlAABgCrQJnCMZAACYA20Cp2gTAABgclQGAACmQJvAOZIBAIA50CZwimQAAGAOJANOMWcAAACTozIAADAF5gw4RzIAADAH2gRO0SYAAMDkqAwAAEzBYhiyGGf/670759Z3JAMAAHOgTeAUbQIAAEyOygAAwBRYTeAcyQAAwBxoEzhFmwAAAJOjMgAAMAXaBM6RDAAAzIE2gVMkAwAAU6Ay4BxzBgAAMDkqAwAAc6BN4BTJAADANHy51O8O2gQAAJgclQEAgDkYxonNnfN9FMkAAMAUWE3gHG0CAABMjsoAAMAcWE3gFMkAAMAULLYTmzvn+yraBAAAmByVAZzRX8cV6NKrihQbX66KMj99t72JXnm8tfbuCfJ2aECNtAgv1d3XbNMlXfMU1LhKe38J0xNL+ykrr6X8/Wy68+ovdEnXXMU0P6bSsgBtzzpHC9+7WIeLm9qv8db0ZWrdvMThuoveu1j/91HPOv40OGu0CZwiGcAZdU8s1erUFvohs4n8Gxm6bcoBPfHGjxpzeSeV/+rv7fCA0woNLtfCB/6jHbtjNHHhIB0tCVKbVsU69mugJCkooEod2/yi1z+4SLv3NVdYk3LdP3yr/nnnB7rjqeEO13p5bS+t3trZ/vp4eeM6/SxwD6sJnPNqMrB582Y9+eSTysjI0IEDB7Ry5UoNGzbMmyHhFB4Z2cHh9dMPtNWKb3fqvO6/6tttIV6KCqiZkUmZOng0RHOW9bPvO1AYZv9zaVmAxr842OGcZ96+VIsnrlJUsxIVHPn9Z/x4eWMVHmtS6zGjlnCfAae8mgyUlpaqR48euv322zV8+PAzn4B6oWmYVZJ07ChVAdR/l3b7WZ/vaqPZo9LUM/6ADhU11cotXbU6vYvTc0KCKmSzScd+DXDYf3NSpm5L3qGCIyFK2x6vFRu7yWpj6hUaPq8mA4MGDdKgQYNqPL68vFzl5eX218XFxbURFk7DYjF018x9+vbzJvo5K9jb4QBnFNP8mIZdtkvLP+6mJWkXqkvbQ3rguq2qtPpr3ecdTxof0KhKdw/9XB/tiNfxst+Tgbc3X6Af8lqo+HigLmhfoLuGfK7m4cf1wsrEuvw4cANtAuca1JyBOXPmaObMmd4Ow9TGPbFPcZ3L9OCweG+HAtSIn8XQ93kt9dKaiyVJu/e2UPvWhRp26XcnJQP+fjbNGvWRJENPrbjM4djyj7vb/7xnf3NVWf006a9b9K/VF6uyiipZg8AEQqcaVH3r4YcfVlFRkX3Ly8vzdkimMvbxvep9ZbEeuv5c/XIg4MwnAPXA4eIm+ik/wmHfzwXNFNXMcWWAv59Ns0d9pOjIEo1fMNihKnAq3/3USo38DUVHHvN0yECda1CVgcDAQAUGBno7DBMyNPbxffrzwCJNuj5eBXn8P0DD8c2PUWrbqshhX2zLo8o/Emp/XZ0ItGlZpPteuFrFx8+8bDa+zWFZbRYdPUa7rKGgTeBcg6oMwDvGPbFPfxl+RP8YG6dfS/zUrGWlmrWsVECQD9+OCz5j+cZuOr9dgW658kud06JIVyZk65o/f693t3SVdCIReGx0mjq1PaRZS/4iP4uhyNDjigw9rkb+JybLnt+uQP+v3zeKjzmsmObFurLXbt13bbo+/CLevkQRDUD1agJ3Nh/VoCoD8I4htx2WJD317h6H/U89EKu0FZHeCAmose9zW+nviwfob0M+120Dd+jA4VDNfzdRadvPkyS1jChVn24/S5JSp7zjcO6986/Wl9kxqqzyV9JFe3T7wAwFNLJqf2Golm/s5jCPAGjIvJoMlJSUKDs72/46JydHmZmZioyMVNu2bb0YGf5XckwPb4cAuGXrzjht3Rl3ymP5haG67L47T3v+D3tb6G/PDKuFyFCXaBM459VkYPv27erfv7/99YQJEyRJKSkpSk1N9VJUAACfxGoCp7yaDPTr10+GD/dgAABoCJgzAAAwBdoEzpEMAADMwWac2Nw530eRDAAAzIE5A05xnwEAAGrBjBkzZLFYHLbOnX9/BHZZWZnGjh2r5s2bKyQkRNddd50KCgocrpGbm6vBgwerSZMmatWqlSZNmqSqqiqPx0plAABgCha5OWfgLM45//zz9dFHH9lfN2r0+9fu+PHjtXbtWr311lsKDw/XuHHjNHz4cH366aeSJKvVqsGDBys6Olpbt27VgQMHdOutt6px48Z64oknzv6DnALJAADAHNy9i+BZnNuoUSNFR0eftL+oqEivvPKKli1bpr/85S+SpNdee01dunTRZ599pksuuUQffvihvvvuO3300UeKiopSz549NXv2bE2ePFkzZsxQQIDnnhFDmwAAABcUFxc7bOXl5U7H7t69WzExMerQoYNGjhyp3NxcSVJGRoYqKyuVlJRkH9u5c2e1bdtW6enpkqT09HR169ZNUVFR9jHJyckqLi7Wzp07PfqZSAYAAKZQvbTQnU2SYmNjFR4ebt/mzJlzyvfr3bu3UlNTtW7dOi1cuFA5OTnq06ePjh07pvz8fAUEBCgiIsLhnKioKOXn50uS8vPzHRKB6uPVxzyJNgEAwBw8tJogLy9PYWFh9t3OnqY7aNAg+5+7d++u3r17Ky4uTitWrFBwcP162iWVAQAAXBAWFuawOUsG/igiIkIdO3ZUdna2oqOjVVFRoaNHjzqMKSgosM8xiI6OPml1QfXrU81DcAfJAADAFCyG4fbmjpKSEu3Zs0etW7dWQkKCGjdurPXr19uPZ2VlKTc3V4mJiZKkxMREffPNNzp48KB9TFpamsLCwtS1a1e3Yvkj2gQAAHOw/ba5c74LJk6cqCFDhiguLk779+/X9OnT5e/vrxtvvFHh4eEaPXq0JkyYoMjISIWFhenee+9VYmKiLrnkEknSgAED1LVrV91yyy2aO3eu8vPz9eijj2rs2LE1rkbUFMkAAAC1YO/evbrxxht1+PBhtWzZUpdddpk+++wztWzZUpI0b948+fn56brrrlN5ebmSk5P14osv2s/39/fXmjVrdPfddysxMVFNmzZVSkqKZs2a5fFYSQYAAKbgbqnf1XPffPPN0x4PCgrSggULtGDBAqdj4uLi9P7777v0vmeDZAAAYA48m8ApkgEAgDl44Q6EDQWrCQAAMDkqAwAAU/jfuwie7fm+imQAAGAOtAmcok0AAIDJURkAAJiCxXZic+d8X0UyAAAwB9oETtEmAADA5KgMAADMgZsOOUUyAAAwhbq+HXFDQpsAAACTozIAADAHJhA6RTIAADAHQ5I7ywN9NxcgGQAAmANzBpxjzgAAACZHZQAAYA6G3Jwz4LFI6h2SAQCAOTCB0CnaBAAAmByVAQCAOdgkWdw830eRDAAATIHVBM7RJgAAwOSoDAAAzIEJhE6RDAAAzIFkwCnaBAAAmByVAQCAOVAZcIpkAABgDiwtdIpkAABgCiwtdI45AwAAmByVAQCAOTBnwCmSAQCAOdgMyeLGF7rNd5MB2gQAAJgclQEAgDnQJnCKZAAAYBJuJgPy3WSANgEAACZHZQAAYA60CZwiGQAAmIPNkFulflYTAAAAX0VlAABgDobtxObO+T6KZAAAYA7MGXCKZAAAYA7MGXCKOQMAAJgclQEAgDnQJnCKZAAAYA6G3EwGPBZJvUObAAAAk6MyAAAwB9oETpEMAADMwWaT5Ma9Amy+e58B2gQAAJgclQEAgDnQJnCKZAAAYA4kA07RJgAAwOSoDAAAzIHbETtFMgAAMAXDsMlw48mD7pxb35EMAADMwTDc++2eOQMAAMBXURkAAJiD4eacAR+uDJAMAADMwWaTLG70/X14zgBtAgAATI7KAADAHGgTOEUyAAAwBcNmk+FGm8CXlxbSJgAAwOSoDAAAzIE2gVMkAwAAc7AZkoVk4FRoEwAAYHJUBgAA5mAYkty5z4DvVgZIBgAApmDYDBlutAkMkgEAABo4wyb3KgMsLQQAAD6KygAAwBRoEzhHMgAAMAfaBE416GSgOkurUqVb95EA6rOqyjJvhwDUGutvP9918Vu3u98VVar0XDD1jMVowHWPvXv3KjY21tthAADclJeXpzZt2tTKtcvKytS+fXvl5+e7fa3o6Gjl5OQoKCjIA5HVHw06GbDZbNq/f79CQ0NlsVi8HY4pFBcXKzY2Vnl5eQoLC/N2OIBH8fNd9wzD0LFjxxQTEyM/v9qb015WVqaKigq3rxMQEOBziYDUwNsEfn5+tZZJ4vTCwsL4xxI+i5/vuhUeHl7r7xEUFOSTX+KewtJCAABMjmQAAACTIxmASwIDAzV9+nQFBgZ6OxTA4/j5hlk16AmEAADAfVQGAAAwOZIBAABMjmQAAACTIxkAAMDkSAZQYwsWLFC7du0UFBSk3r176/PPP/d2SIBHbN68WUOGDFFMTIwsFotWrVrl7ZCAOkUygBpZvny5JkyYoOnTp2vHjh3q0aOHkpOTdfDgQW+HBrittLRUPXr00IIFC7wdCuAVLC1EjfTu3Vt/+tOf9MILL0g68VyI2NhY3XvvvZoyZYqXowM8x2KxaOXKlRo2bJi3QwHqDJUBnFFFRYUyMjKUlJRk3+fn56ekpCSlp6d7MTIAgCeQDOCMfvnlF1mtVkVFRTnsj4qK8sgjQQEA3kUyAACAyZEM4IxatGghf39/FRQUOOwvKChQdHS0l6ICAHgKyQDOKCAgQAkJCVq/fr19n81m0/r165WYmOjFyAAAntDI2wGgYZgwYYJSUlLUq1cvXXzxxXr22WdVWlqqUaNGeTs0wG0lJSXKzs62v87JyVFmZqYiIyPVtm1bL0YG1A2WFqLGXnjhBT355JPKz89Xz549NX/+fPXu3dvbYQFu27hxo/r373/S/pSUFKWmptZ9QEAdIxkAAMDkmDMAAIDJkQwAAGByJAMAAJgcyQAAACZHMgAAgMmRDAAAYHIkAwAAmBzJAAAAJkcyALjptttu07Bhw+yv+/XrpwceeKDO49i4caMsFouOHj3qdIzFYtGqVatqfM0ZM2aoZ8+ebsX1008/yWKxKDMz063rAKg9JAPwSbfddpssFossFosCAgIUHx+vWbNmqaqqqtbf+91339Xs2bNrNLYmX+AAUNt4UBF81sCBA/Xaa6+pvLxc77//vsaOHavGjRvr4YcfPmlsRUWFAgICPPK+kZGRHrkOANQVKgPwWYGBgYqOjlZcXJzuvvtuJSUl6b333pP0e2n/8ccfV0xMjDp16iRJysvL0w033KCIiAhFRkZq6NCh+umnn+zXtFqtmjBhgiIiItS8eXM99NBD+uPjPf7YJigvL9fkyZMVGxurwMBAxcfH65VXXtFPP/1kfzhOs2bNZLFYdNttt0k68YjoOXPmqH379goODlaPHj309ttvO7zP+++/r44dOyo4OFj9+/d3iLOmJk+erI4dO6pJkybq0KGDpk6dqsrKypPG/etf/1JsbKyaNGmiG264QUVFRQ7HFy9erC5duigoKEidO3fWiy++6HIsALyHZACmERwcrIqKCvvr9evXKysrS2lpaVqzZo0qKyuVnJys0NBQbdmyRZ9++qlCQkI0cOBA+3lPP/20UlNT9eqrr+qTTz5RYWGhVq5cedr3vfXWW/XGG29o/vz52rVrl/71r38pJCREsbGxeueddyRJWVlZOnDggJ577jlJ0pw5c7RkyRItWrRIO3fu1Pjx43XzzTdr06ZNkk4kLcOHD9eQIUOUmZmpO+64Q1OmTHH57yQ0NFSpqan67rvv9Nxzz+nll1/WvHnzHMZkZ2drxYoVWr16tdatW6cvv/xS99xzj/340qVLNW3aND3++OPatWuXnnjiCU2dOlWvv/66y/EA8BID8EEpKSnG0KFDDcMwDJvNZqSlpRmBgYHGxIkT7cejoqKM8vJy+zn//ve/jU6dOhk2m82+r7y83AgODjY++OADwzAMo3Xr1sbcuXPtxysrK402bdrY38swDOPyyy837r//fsMwDCMrK8uQZKSlpZ0yzo8//tiQZBw5csS+r6yszGjSpImxdetWh7GjR482brzxRsMwDOPhhx82unbt6nB88uTJJ13rjyQZK1eudHr8ySefNBISEuyvp0+fbvj7+xt79+617/vvf/9r+Pn5GQcOHDAMwzDOPfdcY9myZQ7XmT17tpGYmGgYhmHk5OQYkowvv/zS6fsC8C7mDMBnrVmzRiEhIaqsrJTNZtNNN92kGTNm2I9369bNYZ7AV199pezsbIWGhjpcp6ysTHv27FFRUZEOHDig3r172481atRIvXr1OqlVUC0zM1P+/v66/PLLaxx3dna2jh8/riuvvNJhf0VFhS688EJJ0q5duxzikKTExMQav0e15cuXa/78+dqzZ49KSkpUVVWlsLAwhzFt27bVOeec4/A+NptNWVlZCg0N1Z49ezR69GiNGTPGPqaqqkrh4eEuxwPAO0gG4LP69++vhQsXKiAgQDExMWrUyPHHvWnTpg6vS0pKlJCQoKVLl550rZYtW55VDMHBwS6fU1JSIklau3atw5ewdGIehKekp6dr5MiRmjlzppKTkxUeHq4333xTTz/9tMuxvvzyyyclJ/7+/h6LFUDtIhmAz2ratKni4+NrPP6iiy7S8uXL1apVq5N+O67WunVrbdu2TX379pV04jfgjIwMXXTRRacc361bN9lsNm3atElJSUknHa+uTFitVvu+rl27KjAwULm5uU4rCl26dLFPhqz22WefnflD/o+tW7cqLi5OjzzyiH3fzz//fNK43Nxc7d+/XzExMfb38fPzU6dOnRQVFaWYmBj9+OOPGjlypEvvD6D+YAIh8JuRI0eqRYsWGjp0qLZs2aKcnBxt3LhR9913n/bu3StJuv/++/WPf/xDq1at0vfff6977rnntPcIaNeunVJSUnT77bdr1apV9muuWLFCkhQXFyeLxaI1a9bo0KFDKikpUWhoqCZOnKjx48fr9ddf1549e7Rjxw49//zz9kl5d911l3bv3q1JkyYpKytLy5YtU2pqqkuf97zzzlNubq7efPNN7dmzR/Pnzz/lZMigoCClpKToq6++0pYtW3TffffphhtuUHR0tCRp5syZmjNnjubPn68ffvhB33zzjV577TU988wzLsUDwHtIBoDfNGnSRJs3b1bbtm01fPhwdenSRaNHj1ZZWZm9UvDggw/qlltuUUpKihITExUaGqprr732tNdduHChrr/+et1zzz3q3LmzxowZo9LSUknSOeeco5kzZ2rKlCmKiorSuHHjJEmzZ8/W1KlTNWfOHHXp0kUDBw7U2rVr1b59e0kn+vjvvPOOVq1apR49emjRokV64oknXPq811xzjcaPH69x48apZ8+e2rp1q6ZOnXrSuPj4eA0fPlxXXXWVBgwYoO7duzssHbzjjju0ePFivfbaa+rWrZsuv/xypaam2mMFUP9ZDGcznwAAgClQGQAAwORIBgAAMDmSAQAATI5kAAAAkyMZAADA5EgGAAAwOZIBAABMjmQAAACTIxkAAMDkSAYAADA5kgEAAEzu/wOcNhrY80/O6QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rAwHF6fC58h",
        "outputId": "913cfd04-9112-45e5-95ed-ffd1428ab492"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7962\n",
              "1    2038\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we can see that accuracy of the dataset is pretty good but this is an imbalanced data set. So the chance of false prediction in the class with lower value counts is high.\n",
        "\n",
        "Inorder to rectify that problem, we use the oversampling method."
      ],
      "metadata": {
        "id": "cGxNNi0M7E5g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#8. Balance Dataset - Over Sampling"
      ],
      "metadata": {
        "id": "qAri1K58DEcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "os=SMOTE(random_state=1)\n",
        "X_os,y_os=os.fit_resample(X,y)"
      ],
      "metadata": {
        "id": "7REx_B5NC-KB"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_os.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6IhyikJDwLu",
        "outputId": "578127f9-8653-49f3-a23d-e345dac44dec"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    7962\n",
              "0    7962\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mm=MinMaxScaler()\n",
        "X_sc1=mm.fit_transform(X_os)\n",
        "X_sc1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDoNTyYbDxgU",
        "outputId": "b8842e0f-ac1d-4f38-8b9f-734c06b6f2bd"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.538     , 0.        , 0.        , ..., 0.25      , 0.        ,\n",
              "        0.39160045],\n",
              "       [0.516     , 1.        , 0.        , ..., 0.5       , 0.        ,\n",
              "        0.38251986],\n",
              "       [0.304     , 0.        , 0.        , ..., 0.5       , 0.        ,\n",
              "        0.29284904],\n",
              "       ...,\n",
              "       [0.266     , 0.        , 0.        , ..., 0.75      , 0.33333333,\n",
              "        0.3984109 ],\n",
              "       [0.538     , 0.5       , 1.        , ..., 0.5       , 1.        ,\n",
              "        0.30079455],\n",
              "       [0.67      , 0.5       , 0.        , ..., 0.        , 1.        ,\n",
              "        0.54143019]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X_sc1,y_os,test_size=0.3,random_state=1)"
      ],
      "metadata": {
        "id": "uyK7ttxJD58a"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_os=KNeighborsClassifier(n_neighbors=11)\n",
        "sv_os=SVC(kernel='poly')\n",
        "nb_os=GaussianNB()\n",
        "clf_os=DecisionTreeClassifier(criterion='gini')\n",
        "rf_os=RandomForestClassifier(n_estimators=50,random_state=1)\n",
        "ab_os=AdaBoostClassifier(random_state=1)\n",
        "lgbm_os=LGBMClassifier()"
      ],
      "metadata": {
        "id": "D9KZMO7-D9Dm"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models=[knn_os,sv_os,nb_os,clf_os,rf_os,ab_os,lgbm_os]\n",
        "for model in models:\n",
        "  print('**********',model,'**********')\n",
        "  model.fit(X_train,y_train)\n",
        "  y_pred_model=model.predict(X_test)\n",
        "  print(accuracy_score(y_test,y_pred_model)*100)\n",
        "  print(classification_report(y_test,y_pred_model,digits=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajwZSSB2EA-H",
        "outputId": "17a04e0e-134b-4805-a997-4fc4d7fbd7ac"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** KNeighborsClassifier(n_neighbors=11) **********\n",
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n",
            "********** SVC(kernel='poly') **********\n",
            "99.79070740895773\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9975    0.9983    0.9979      2365\n",
            "           1     0.9983    0.9975    0.9979      2413\n",
            "\n",
            "    accuracy                         0.9979      4778\n",
            "   macro avg     0.9979    0.9979    0.9979      4778\n",
            "weighted avg     0.9979    0.9979    0.9979      4778\n",
            "\n",
            "********** GaussianNB() **********\n",
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n",
            "********** DecisionTreeClassifier() **********\n",
            "99.68606111343658\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9979    0.9958    0.9968      2365\n",
            "           1     0.9959    0.9979    0.9969      2413\n",
            "\n",
            "    accuracy                         0.9969      4778\n",
            "   macro avg     0.9969    0.9968    0.9969      4778\n",
            "weighted avg     0.9969    0.9969    0.9969      4778\n",
            "\n",
            "********** RandomForestClassifier(n_estimators=50, random_state=1) **********\n",
            "99.79070740895773\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9975    0.9983    0.9979      2365\n",
            "           1     0.9983    0.9975    0.9979      2413\n",
            "\n",
            "    accuracy                         0.9979      4778\n",
            "   macro avg     0.9979    0.9979    0.9979      4778\n",
            "weighted avg     0.9979    0.9979    0.9979      4778\n",
            "\n",
            "********** AdaBoostClassifier(random_state=1) **********\n",
            "99.79070740895773\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9975    0.9983    0.9979      2365\n",
            "           1     0.9983    0.9975    0.9979      2413\n",
            "\n",
            "    accuracy                         0.9979      4778\n",
            "   macro avg     0.9979    0.9979    0.9979      4778\n",
            "weighted avg     0.9979    0.9979    0.9979      4778\n",
            "\n",
            "********** LGBMClassifier() **********\n",
            "99.81163666806195\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9979    0.9983    0.9981      2365\n",
            "           1     0.9983    0.9979    0.9981      2413\n",
            "\n",
            "    accuracy                         0.9981      4778\n",
            "   macro avg     0.9981    0.9981    0.9981      4778\n",
            "weighted avg     0.9981    0.9981    0.9981      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#9. Hyper Parameter Tuning"
      ],
      "metadata": {
        "id": "yE0Q2zH_ENq4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#KNeighbors Classifier\n",
        "knn1=KNeighborsClassifier()\n",
        "param1={'n_neighbors':[3,5,7,9,11,13,15],'weights':['uniform', 'distance'],'algorithm':['auto','ball_tree','kd_tree','brute']}\n",
        "hp_knn1=GridSearchCV(knn1,param1,cv=5,scoring='accuracy')\n",
        "hp_knn1.fit(X_train,y_train)\n",
        "hp_knn1.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gK4DuEGSEE0D",
        "outputId": "ad2ca9ce-c5e4-42de-b526-735b6d747efa"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'auto', 'n_neighbors': 11, 'weights': 'distance'}"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SVC\n",
        "sv1=SVC()\n",
        "param2={'kernel':['linear','poly','rbf'],'gamma':['scale','auto']}\n",
        "hp_sv1=GridSearchCV(sv1,param2,cv=5,scoring='accuracy')\n",
        "hp_sv1.fit(X_train,y_train)\n",
        "hp_sv1.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTSf7UUeES4b",
        "outputId": "7da21c17-7545-48e2-e5fc-90723dcf637b"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'gamma': 'scale', 'kernel': 'poly'}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DecisionTree Classifier\n",
        "clf1=DecisionTreeClassifier()\n",
        "param4={'criterion':['gini','entropy'],'splitter':['best','random']}\n",
        "hp_clf1=GridSearchCV(clf1,param4,cv=5,scoring='accuracy')\n",
        "hp_clf1.fit(X_train,y_train)\n",
        "hp_clf1.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UofVmB1-aS2G",
        "outputId": "4a3b55ce-1c9b-4df6-d7d8-2261741f142b"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'entropy', 'splitter': 'best'}"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RandomForest Classifier\n",
        "rf1=RandomForestClassifier(random_state=1)\n",
        "params3={'n_estimators':[40,50,60,70,80,90,100],'criterion':['gini','entropy','log_loss'],'max_depth':[3,4,5,6]}\n",
        "hp_rf1=GridSearchCV(rf1,params3,cv=5,scoring='accuracy')\n",
        "hp_rf1.fit(X_train,y_train)\n",
        "hp_rf1.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6COeAw5Y2k6",
        "outputId": "e80c8086-0619-4447-f7a7-612ea4f5ca19"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini', 'max_depth': 6, 'n_estimators': 40}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn2=KNeighborsClassifier(algorithm='auto',n_neighbors=11,weights='distance')\n",
        "sv2=SVC(gamma='scale',kernel='poly')\n",
        "clf2=DecisionTreeClassifier(criterion='entropy',splitter='best')\n",
        "rf2=RandomForestClassifier(n_estimators=40,criterion='gini',max_depth=6,random_state=1)"
      ],
      "metadata": {
        "id": "mORRKTG3EdbQ"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models=[knn2,sv2,clf2,rf2]\n",
        "for model in models:\n",
        "  print('**********',model,'**********')\n",
        "  model.fit(X_train,y_train)\n",
        "  y_pred2=model.predict(X_test)\n",
        "  print(accuracy_score(y_test,y_pred2)*100)\n",
        "  print(classification_report(y_test,y_pred2,digits=4))"
      ],
      "metadata": {
        "id": "eRM_9BaYEimU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a511070a-41bb-4394-a104-26f420756248"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "********** KNeighborsClassifier(n_neighbors=11, weights='distance') **********\n",
            "99.79070740895773\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9975    0.9983    0.9979      2365\n",
            "           1     0.9983    0.9975    0.9979      2413\n",
            "\n",
            "    accuracy                         0.9979      4778\n",
            "   macro avg     0.9979    0.9979    0.9979      4778\n",
            "weighted avg     0.9979    0.9979    0.9979      4778\n",
            "\n",
            "********** SVC(kernel='poly') **********\n",
            "99.79070740895773\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9975    0.9983    0.9979      2365\n",
            "           1     0.9983    0.9975    0.9979      2413\n",
            "\n",
            "    accuracy                         0.9979      4778\n",
            "   macro avg     0.9979    0.9979    0.9979      4778\n",
            "weighted avg     0.9979    0.9979    0.9979      4778\n",
            "\n",
            "********** DecisionTreeClassifier(criterion='entropy') **********\n",
            "99.70699037254082\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9958    0.9983    0.9970      2365\n",
            "           1     0.9983    0.9959    0.9971      2413\n",
            "\n",
            "    accuracy                         0.9971      4778\n",
            "   macro avg     0.9971    0.9971    0.9971      4778\n",
            "weighted avg     0.9971    0.9971    0.9971      4778\n",
            "\n",
            "********** RandomForestClassifier(max_depth=6, n_estimators=40, random_state=1) **********\n",
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#10. Feature Extraction - PCA"
      ],
      "metadata": {
        "id": "hSP_XLXGf52f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca=PCA(n_components=11)\n",
        "X_train=pca.fit_transform(X_train)\n",
        "X_test=pca.transform(X_test)"
      ],
      "metadata": {
        "id": "UN9-pg0Ef5J6"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#KNeighbors Classifier\n",
        "knn_pca=KNeighborsClassifier(algorithm='auto',n_neighbors=11,weights='distance')\n",
        "knn_pca.fit(X_train,y_train)\n",
        "y_pred_knn_pca=knn_pca.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_knn_pca)*100)\n",
        "print(classification_report(y_test,y_pred_knn_pca,digits=4))"
      ],
      "metadata": {
        "id": "qg41H6Jggt9H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18cb7aab-7b5f-476e-e06f-c614f9d37918"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "id": "OFY4yUUFiDbM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8187f3d8-b010-4386-ea67-86a10da1731d"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.23851414, 0.13168744, 0.12107151, 0.08473012, 0.08248466,\n",
              "       0.07076656, 0.06295944, 0.04874617, 0.04274653, 0.03659719,\n",
              "       0.0328227 ])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SVC\n",
        "svc_pca=SVC(gamma='scale',kernel='poly')\n",
        "svc_pca.fit(X_train,y_train)\n",
        "y_pred_svc_pca=knn_pca.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_svc_pca)*100)\n",
        "print(classification_report(y_test,y_pred_svc_pca,digits=4))"
      ],
      "metadata": {
        "id": "GxHhtq1piH1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea9b5dd3-8b96-493f-ab08-663a474fc54b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "id": "H4JUDZVvig6W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b7155e1-6928-457d-efea-2160b25aafaa"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.23851414, 0.13168744, 0.12107151, 0.08473012, 0.08248466,\n",
              "       0.07076656, 0.06295944, 0.04874617, 0.04274653, 0.03659719,\n",
              "       0.0328227 ])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest\n",
        "rf_pca=RandomForestClassifier(n_estimators=40,random_state=1)\n",
        "rf_pca.fit(X_train,y_train)\n",
        "y_pred_rf_pca=knn_pca.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_rf_pca)*100)\n",
        "print(classification_report(y_test,y_pred_rf_pca,digits=4))"
      ],
      "metadata": {
        "id": "c_maHcfZkaQT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "444c367a-80b0-4a99-d7b0-bf42ab073d23"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca.explained_variance_ratio_"
      ],
      "metadata": {
        "id": "o6NIu7cFk2xD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9805a7ff-a83a-4ba8-f683-cf40b773cac8"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.23851414, 0.13168744, 0.12107151, 0.08473012, 0.08248466,\n",
              "       0.07076656, 0.06295944, 0.04874617, 0.04274653, 0.03659719,\n",
              "       0.0328227 ])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#11. Feature Selection"
      ],
      "metadata": {
        "id": "_jQ7xlOklCvK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "chi2_select=SelectKBest(chi2,k=10)\n",
        "X_kbest=chi2_select.fit_transform(X_sc1,y_os)\n",
        "X_kbest.shape"
      ],
      "metadata": {
        "id": "huzFJ0jnlHH7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98b99412-9bf0-45e2-d14c-7394ababed7e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15924, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selected_features=chi2_select.fit_transform(X_sc1,y_os)\n",
        "selected_feature_indices=chi2_select.get_support(indices=True)\n",
        "print('Selected_feature_names:',X_os.columns[selected_feature_indices].tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "61wygcTVbVew",
        "outputId": "1cfb0abe-fadf-4f2b-e255-16e9ba31e534"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected_feature_names: ['Geography', 'Gender', 'Age', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'Complain', 'Satisfaction Score', 'Card Type']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_os.drop(['CreditScore','Tenure','EstimatedSalary','Point Earned'],axis=1,inplace=True)\n"
      ],
      "metadata": {
        "id": "1o_MOTgzIyza"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_os"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "4UfWFnPYJH8e",
        "outputId": "0ee71794-3a74-4562-c80f-4e85f6336393"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Geography  Gender  Age        Balance  NumOfProducts  HasCrCard  \\\n",
              "0              0       0   42       0.000000              1          1   \n",
              "1              2       0   41   83807.860000              1          0   \n",
              "2              0       0   42  159660.800000              3          1   \n",
              "3              0       0   39       0.000000              2          0   \n",
              "4              2       0   43  125510.820000              1          1   \n",
              "...          ...     ...  ...            ...            ...        ...   \n",
              "15919          1       1   43   88232.099299              2          1   \n",
              "15920          0       0   34   70527.920705              1          0   \n",
              "15921          0       0   45  144949.077444              1          1   \n",
              "15922          1       1   47  101276.972553              1          0   \n",
              "15923          1       0   31  101077.953098              2          1   \n",
              "\n",
              "       IsActiveMember  Complain  Satisfaction Score  Card Type  \n",
              "0                   1         1                   2          0  \n",
              "1                   1         1                   3          0  \n",
              "2                   0         1                   3          0  \n",
              "3                   0         0                   5          1  \n",
              "4                   1         0                   5          1  \n",
              "...               ...       ...                 ...        ...  \n",
              "15919               0         1                   2          2  \n",
              "15920               0         1                   5          1  \n",
              "15921               0         1                   4          1  \n",
              "15922               0         1                   3          3  \n",
              "15923               0         1                   1          3  \n",
              "\n",
              "[15924 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-21c68fc0-18e4-40b8-ac76-085fbb3018c9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>Complain</th>\n",
              "      <th>Satisfaction Score</th>\n",
              "      <th>Card Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>83807.860000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>159660.800000</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>125510.820000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15919</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>88232.099299</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15920</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>70527.920705</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15921</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>144949.077444</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15922</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>101276.972553</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15923</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>101077.953098</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>15924 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21c68fc0-18e4-40b8-ac76-085fbb3018c9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-29284f41-d07b-405d-9e0c-546b4ba513bc\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-29284f41-d07b-405d-9e0c-546b4ba513bc')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-29284f41-d07b-405d-9e0c-546b4ba513bc button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-21c68fc0-18e4-40b8-ac76-085fbb3018c9 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-21c68fc0-18e4-40b8-ac76-085fbb3018c9');\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": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mm2=MinMaxScaler()\n",
        "X_sc2=mm2.fit_transform(X_os)\n",
        "X_sc2"
      ],
      "metadata": {
        "id": "fXeSYx8Sca_z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b60da3f0-8434-482c-8c1a-a4184900b27a"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.32432432, ..., 1.        , 0.25      ,\n",
              "        0.        ],\n",
              "       [1.        , 0.        , 0.31081081, ..., 1.        , 0.5       ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.32432432, ..., 1.        , 0.5       ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.36486486, ..., 1.        , 0.75      ,\n",
              "        0.33333333],\n",
              "       [0.5       , 1.        , 0.39189189, ..., 1.        , 0.5       ,\n",
              "        1.        ],\n",
              "       [0.5       , 0.        , 0.17567568, ..., 1.        , 0.        ,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X_sc2,y_os,test_size=0.3,random_state=1)"
      ],
      "metadata": {
        "id": "4v9y7PUTl9Dd"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#KNeighbors Classifier\n",
        "knn_chi=KNeighborsClassifier(algorithm='auto',n_neighbors=11,weights='distance')\n",
        "knn_chi.fit(X_train,y_train)\n",
        "y_pred_knn_chi=knn_chi.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_knn_chi)*100)\n",
        "print(classification_report(y_test,y_pred_knn_chi,digits=4))"
      ],
      "metadata": {
        "id": "JT4wqTG6mKi_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c424963-6f88-4915-b075-08dba268abe2"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SVC\n",
        "svc_chi=SVC(gamma='scale',kernel='poly')\n",
        "svc_chi.fit(X_train,y_train)\n",
        "y_pred_svc_chi=svc_chi.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_svc_chi)*100)\n",
        "print(classification_report(y_test,y_pred_svc_chi,digits=4))"
      ],
      "metadata": {
        "id": "a_d80AYLm5vX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8be75148-fa59-4972-960a-0350673700bf"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.7697781498535\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9970    0.9983    0.9977      2365\n",
            "           1     0.9983    0.9971    0.9977      2413\n",
            "\n",
            "    accuracy                         0.9977      4778\n",
            "   macro avg     0.9977    0.9977    0.9977      4778\n",
            "weighted avg     0.9977    0.9977    0.9977      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Classifier\n",
        "rf_chi=RandomForestClassifier(n_estimators=40,random_state=1)\n",
        "rf_chi.fit(X_train,y_train)\n",
        "y_pred_rf_chi=rf_chi.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred_rf_chi)*100)\n",
        "print(classification_report(y_test,y_pred_rf_chi,digits=4))"
      ],
      "metadata": {
        "id": "RjO0tQzenNpJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdbad4e4-3602-4b08-ac90-d072578d2b8b"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.79070740895773\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.9975    0.9983    0.9979      2365\n",
            "           1     0.9983    0.9975    0.9979      2413\n",
            "\n",
            "    accuracy                         0.9979      4778\n",
            "   macro avg     0.9979    0.9979    0.9979      4778\n",
            "weighted avg     0.9979    0.9979    0.9979      4778\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Conclusion** - By comparing the performance measures of different algorithms the most accurate model is using KNeighbors Classifier, SVC and Random Forest Classifier after Feature Selection."
      ],
      "metadata": {
        "id": "0zW2mAH1Eo2V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#New Prediction"
      ],
      "metadata": {
        "id": "3IF4w25O3pBL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data={'Geography':['France'],'Gender':['Female'],'Age':[42],'Balance':[0.00],'NumOfProducts':[1],'HasCrCard':[1],'IsActiveMember':[1],'Complain':[1],'Satisfaction Score':[2],'Card Type':['Diamond']}\n",
        "df_pred=pd.DataFrame(data)\n",
        "df_pred['Geography'] = geo.fit_transform(df_pred['Geography'])\n",
        "df_pred['Gender'] = gen.fit_transform(df_pred['Gender'])\n",
        "df_pred['Card Type'] = ca.fit_transform(df_pred['Card Type'])"
      ],
      "metadata": {
        "id": "rakfKcdI3uAf"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "(i) Prediction using KNeighbors Classifier"
      ],
      "metadata": {
        "id": "K2YBnPeqJJi0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_new=knn_chi.predict(mm2.transform(df_pred))\n",
        "if y_new==0:\n",
        "  print('The customer will Stay')\n",
        "else:\n",
        "  print('The Customer Exited')"
      ],
      "metadata": {
        "id": "tXV_q0vAJRq2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01363d10-88d8-4ea6-d66d-6b54eeada17b"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Customer Exited\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(ii) Prediction using SVC"
      ],
      "metadata": {
        "id": "lT3UNfISJdqC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_new=svc_chi.predict(mm2.transform(df_pred))\n",
        "if y_new==0:\n",
        "  print('The customer will Stay')\n",
        "else:\n",
        "  print('The Customer Exited')"
      ],
      "metadata": {
        "id": "kpvEeMBiJh47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2210bc52-aa14-424a-c617-ef73ebd4e2aa"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Customer Exited\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(iii) Prediction using Random Forest Classifier"
      ],
      "metadata": {
        "id": "VVtOqjKwfyVY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_new=rf_chi.predict(mm2.transform(df_pred))\n",
        "if y_new==0:\n",
        "  print('The customer will Stay')\n",
        "else:\n",
        "  print('The Customer Exited')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmdO3dPlf8-R",
        "outputId": "bcfc64a7-f790-49b5-c94c-ca96c1083b18"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Customer Exited\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Saving Model"
      ],
      "metadata": {
        "id": "aSjHLscOMHol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "JF7-jWNbMOht"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#saving the model\n",
        "pickle.dump(svc_chi,open('model.sav','wb'))\n",
        "#saving objects\n",
        "objects=[geo,gen,ca,mm2]\n",
        "pickle.dump(objects,open('objects.sav','wb'))"
      ],
      "metadata": {
        "id": "L49VNwKsM3CW"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#loading\n",
        "model=pickle.load(open('model.sav','rb'))\n",
        "objects1=pickle.load(open('objects.sav','rb'))\n",
        "objects1"
      ],
      "metadata": {
        "id": "-hurEzjZN_4O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4508534-d366-4400-c2b5-ccc59612b096"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[LabelEncoder(), LabelEncoder(), LabelEncoder(), MinMaxScaler()]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    }
  ]
}