Churn_customer_prediction.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score,classification_report\n",
        "from sklearn.svm import SVC\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
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        "data=pd.read_csv('Churn_Modelling (1).csv')"
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              "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
              "0          1    15634602  Hargrave          619    France  Female   42   \n",
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              "\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  \n",
              "0        101348.88       1  \n",
              "1        112542.58       0  \n",
              "2        113931.57       1  \n",
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              "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
              "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
              "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
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              "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-8b55ecdd-59fb-4ad4-8c5c-c48f0fbe5242\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b55ecdd-59fb-4ad4-8c5c-c48f0fbe5242')\"\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-8b55ecdd-59fb-4ad4-8c5c-c48f0fbe5242 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-938f6d08-09e6-46f6-bd38-ccc68cde5c46 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-938f6d08-09e6-46f6-bd38-ccc68cde5c46');\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": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyS-DUbfeKnc",
        "outputId": "89edbfaa-bb72-4e8a-f4ef-df017c1029f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlECGCcacXLE",
        "outputId": "e79ee734-b83c-4427-d9ad-f94ac0051aa1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "CJxfH__hcZvF",
        "outputId": "727c873b-f38b-47f5-a9fa-c74ea9d1adfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         RowNumber    CustomerId   CreditScore           Age        Tenure  \\\n",
              "count  10000.00000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
              "mean    5000.50000  1.569094e+07    650.528800     38.921800      5.012800   \n",
              "std     2886.89568  7.193619e+04     96.653299     10.487806      2.892174   \n",
              "min        1.00000  1.556570e+07    350.000000     18.000000      0.000000   \n",
              "25%     2500.75000  1.562853e+07    584.000000     32.000000      3.000000   \n",
              "50%     5000.50000  1.569074e+07    652.000000     37.000000      5.000000   \n",
              "75%     7500.25000  1.575323e+07    718.000000     44.000000      7.000000   \n",
              "max    10000.00000  1.581569e+07    850.000000     92.000000     10.000000   \n",
              "\n",
              "             Balance  NumOfProducts    HasCrCard  IsActiveMember  \\\n",
              "count   10000.000000   10000.000000  10000.00000    10000.000000   \n",
              "mean    76485.889288       1.530200      0.70550        0.515100   \n",
              "std     62397.405202       0.581654      0.45584        0.499797   \n",
              "min         0.000000       1.000000      0.00000        0.000000   \n",
              "25%         0.000000       1.000000      0.00000        0.000000   \n",
              "50%     97198.540000       1.000000      1.00000        1.000000   \n",
              "75%    127644.240000       2.000000      1.00000        1.000000   \n",
              "max    250898.090000       4.000000      1.00000        1.000000   \n",
              "\n",
              "       EstimatedSalary        Exited  \n",
              "count     10000.000000  10000.000000  \n",
              "mean     100090.239881      0.203700  \n",
              "std       57510.492818      0.402769  \n",
              "min          11.580000      0.000000  \n",
              "25%       51002.110000      0.000000  \n",
              "50%      100193.915000      0.000000  \n",
              "75%      149388.247500      0.000000  \n",
              "max      199992.480000      1.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-4e2b9de9-6e33-4994-8e1e-6f23777d7182\">\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",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>RowNumber</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.00000</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5000.50000</td>\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>0.203700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2886.89568</td>\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>0.402769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2500.75000</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5000.50000</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7500.25000</td>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10000.00000</td>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e2b9de9-6e33-4994-8e1e-6f23777d7182')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b827c817-fff9-45cc-a33b-88e0a26a3d87\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b827c817-fff9-45cc-a33b-88e0a26a3d87')\"\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",
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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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "  }\n",
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              "\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",
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              "    domScope.querySelector('#df-b827c817-fff9-45cc-a33b-88e0a26a3d87 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-4e2b9de9-6e33-4994-8e1e-6f23777d7182 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-4e2b9de9-6e33-4994-8e1e-6f23777d7182');\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": [
        "plot = plt.figure(figsize=(5,5))\n",
        "sns.barplot(x='Exited', y = 'IsActiveMember', data = data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "2IJbhw3mcm2o",
        "outputId": "e376dbc2-22cc-4c53-bf8f-1af8abdc012a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Exited', ylabel='IsActiveMember'>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Exited'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ufxUcm-c1Bk",
        "outputId": "1b844245-295a-4bb7-ee98-22d3df22b311"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    7963\n",
              "1    2037\n",
              "Name: Exited, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['IsActiveMember'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKINHriydZ5G",
        "outputId": "657cd6e6-0cca-4085-fecd-779e78970698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    5151\n",
              "0    4849\n",
              "Name: IsActiveMember, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(x='Exited', data = data, kind = 'count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "deSHuimRdimz",
        "outputId": "daef6485-1e51-4032-eeb0-cfe0e690e7d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7b21a2e1ccd0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(x='IsActiveMember', data = data, kind = 'count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "BwL9f2egdvoG",
        "outputId": "ef4272a7-fdb8-4e74-d82d-12feb74cdfd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7b21a2e1cd30>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WN45tsbjd51T",
        "outputId": "1937678c-c2c3-4814-9017-dda5aef366c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber          0\n",
              "CustomerId         0\n",
              "Surname            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",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = data.corr()\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(correlation, cbar=True, square=True, fmt = '.1f', annot = True, annot_kws={'size':8}, cmap = 'Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "rBz9axibhhYL",
        "outputId": "880fddab-c5ed-4f73-f2c9-965282c6cd1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = LabelEncoder()"
      ],
      "metadata": {
        "id": "3UmIKCvHh8l_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Geography'] = encoder.fit_transform(data['Geography'])\n",
        "\n",
        "data['Gender'] = encoder.fit_transform(data['Gender'])\n",
        "data['Surname'] = encoder.fit_transform(data['Surname'])\n"
      ],
      "metadata": {
        "id": "EGUibKuYiZ9Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "cElf2PpejLLe",
        "outputId": "2816d3ef-4378-4701-df08-5704b0352ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      RowNumber  CustomerId  Surname  CreditScore  Geography  Gender  Age  \\\n",
              "0             1    15634602     1115          619          0       0   42   \n",
              "1             2    15647311     1177          608          2       0   41   \n",
              "2             3    15619304     2040          502          0       0   42   \n",
              "3             4    15701354      289          699          0       0   39   \n",
              "4             5    15737888     1822          850          2       0   43   \n",
              "...         ...         ...      ...          ...        ...     ...  ...   \n",
              "9995       9996    15606229     1999          771          0       1   39   \n",
              "9996       9997    15569892     1336          516          0       1   35   \n",
              "9997       9998    15584532     1570          709          0       0   36   \n",
              "9998       9999    15682355     2345          772          1       1   42   \n",
              "9999      10000    15628319     2751          792          0       0   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  \n",
              "0           101348.88       1  \n",
              "1           112542.58       0  \n",
              "2           113931.57       1  \n",
              "3            93826.63       0  \n",
              "4            79084.10       0  \n",
              "...               ...     ...  \n",
              "9995         96270.64       0  \n",
              "9996        101699.77       0  \n",
              "9997         42085.58       1  \n",
              "9998         92888.52       1  \n",
              "9999         38190.78       0  \n",
              "\n",
              "[10000 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b8ef949e-aeb2-444b-b85d-69c3e85127ea\">\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>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>15634602</td>\n",
              "      <td>1115</td>\n",
              "      <td>619</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101348.88</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>15647311</td>\n",
              "      <td>1177</td>\n",
              "      <td>608</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>112542.58</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>15619304</td>\n",
              "      <td>2040</td>\n",
              "      <td>502</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113931.57</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>15701354</td>\n",
              "      <td>289</td>\n",
              "      <td>699</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>93826.63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>1822</td>\n",
              "      <td>850</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>9996</td>\n",
              "      <td>15606229</td>\n",
              "      <td>1999</td>\n",
              "      <td>771</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>96270.64</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>9997</td>\n",
              "      <td>15569892</td>\n",
              "      <td>1336</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>57369.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101699.77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>9998</td>\n",
              "      <td>15584532</td>\n",
              "      <td>1570</td>\n",
              "      <td>709</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>42085.58</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>9999</td>\n",
              "      <td>15682355</td>\n",
              "      <td>2345</td>\n",
              "      <td>772</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>3</td>\n",
              "      <td>75075.31</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>92888.52</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>10000</td>\n",
              "      <td>15628319</td>\n",
              "      <td>2751</td>\n",
              "      <td>792</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>130142.79</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38190.78</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8ef949e-aeb2-444b-b85d-69c3e85127ea')\"\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",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b1bf4fef-b593-4ccc-ba4c-b7f8206f1378\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b1bf4fef-b593-4ccc-ba4c-b7f8206f1378')\"\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",
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              "    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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    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",
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              "</style>\n",
              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\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-b8ef949e-aeb2-444b-b85d-69c3e85127ea 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-b8ef949e-aeb2-444b-b85d-69c3e85127ea');\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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(columns='Exited', axis=1)\n",
        "Y = data['Exited']"
      ],
      "metadata": {
        "id": "eBUPazi_gM5T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "dH2wymmpgSpB",
        "outputId": "a24bdcff-8ccc-4699-e234-a4303965abe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      RowNumber  CustomerId  Surname  CreditScore  Geography  Gender  Age  \\\n",
              "0             1    15634602     1115          619          0       0   42   \n",
              "1             2    15647311     1177          608          2       0   41   \n",
              "2             3    15619304     2040          502          0       0   42   \n",
              "3             4    15701354      289          699          0       0   39   \n",
              "4             5    15737888     1822          850          2       0   43   \n",
              "...         ...         ...      ...          ...        ...     ...  ...   \n",
              "9995       9996    15606229     1999          771          0       1   39   \n",
              "9996       9997    15569892     1336          516          0       1   35   \n",
              "9997       9998    15584532     1570          709          0       0   36   \n",
              "9998       9999    15682355     2345          772          1       1   42   \n",
              "9999      10000    15628319     2751          792          0       0   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  \n",
              "0           101348.88  \n",
              "1           112542.58  \n",
              "2           113931.57  \n",
              "3            93826.63  \n",
              "4            79084.10  \n",
              "...               ...  \n",
              "9995         96270.64  \n",
              "9996        101699.77  \n",
              "9997         42085.58  \n",
              "9998         92888.52  \n",
              "9999         38190.78  \n",
              "\n",
              "[10000 rows x 13 columns]"
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>96270.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>9997</td>\n",
              "      <td>15569892</td>\n",
              "      <td>1336</td>\n",
              "      <td>516</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>10</td>\n",
              "      <td>57369.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>101699.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>9998</td>\n",
              "      <td>15584532</td>\n",
              "      <td>1570</td>\n",
              "      <td>709</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>42085.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>9999</td>\n",
              "      <td>15682355</td>\n",
              "      <td>2345</td>\n",
              "      <td>772</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>3</td>\n",
              "      <td>75075.31</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>92888.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>10000</td>\n",
              "      <td>15628319</td>\n",
              "      <td>2751</td>\n",
              "      <td>792</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>130142.79</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38190.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>10000 rows × 13 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78eff29a-d71d-4ff3-8ca8-a5a5a2816192')\"\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
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              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\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",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "  quickchartButtonEl.style.display =\n",
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              "      height: 32px;\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-78eff29a-d71d-4ff3-8ca8-a5a5a2816192');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnnkGEWmhSgd",
        "outputId": "b38ae42f-8d0b-4f20-ff56-f3296326be8a"
      },
      "execution_count": null,
      "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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "metadata": {
        "id": "sL8MPpb6hTRh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "SBsz2Mi-jfVX",
        "outputId": "7733cd43-b6d1-44f1-ff10-5a4870f4356e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      RowNumber  CustomerId  Surname  CreditScore  Geography  Gender  Age  \\\n",
              "5291       5292    15575510     1803          659          0       0   32   \n",
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              "...         ...         ...      ...          ...        ...     ...  ...   \n",
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              "\n",
              "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
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              "\n",
              "      EstimatedSalary  \n",
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              "8771         48226.76  \n",
              "35           27822.99  \n",
              "1255         62825.03  \n",
              "3266         96555.42  \n",
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              "9859        184125.10  \n",
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              "<p>8000 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9647d2fb-4174-48e9-8512-9bbbbcbde90d')\"\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-cf6aedaf-95c4-4a62-9ad1-d455d8637fbe\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cf6aedaf-95c4-4a62-9ad1-d455d8637fbe')\"\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-cf6aedaf-95c4-4a62-9ad1-d455d8637fbe 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-9647d2fb-4174-48e9-8512-9bbbbcbde90d 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-9647d2fb-4174-48e9-8512-9bbbbcbde90d');\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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zg4JWyE5jg-H",
        "outputId": "fe4083ef-b748-44b9-eafd-38c2aae09774"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5291    0\n",
              "8771    0\n",
              "35      1\n",
              "1255    0\n",
              "3266    0\n",
              "       ..\n",
              "6400    0\n",
              "9160    0\n",
              "9859    1\n",
              "1688    0\n",
              "5994    0\n",
              "Name: Exited, Length: 8000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape,X_train.shape,X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFDIuww6jiwl",
        "outputId": "8d9b729b-90ad-4538-d556-53a9f8c7570c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10000, 13) (8000, 13) (2000, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "print(lr.fit(X_train,Y_train))\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "print(rf.fit(X_train,Y_train))\n",
        "\n",
        "\n",
        "xg = XGBClassifier()\n",
        "print(xg.fit(X_train,Y_train))\n",
        "\n",
        "sm = SVC()\n",
        "print(sm.fit(X_train,Y_train))"
      ],
      "metadata": {
        "id": "BOm_3iqYjs6U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63d54193-234f-4360-e1c5-ba5256769150"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogisticRegression()\n",
            "RandomForestClassifier()\n",
            "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
            "              colsample_bylevel=None, colsample_bynode=None,\n",
            "              colsample_bytree=None, early_stopping_rounds=None,\n",
            "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
            "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
            "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
            "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
            "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
            "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
            "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
            "              predictor=None, random_state=None, ...)\n",
            "SVC()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_prediction=lr.predict(X_train)\n",
        "train_data_accuracy=accuracy_score(X_train_prediction,Y_train)"
      ],
      "metadata": {
        "id": "nmrNx6xyX6xi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy:',train_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvDtVraHtTO0",
        "outputId": "38edb8b1-a41d-4f48-c7c7-2f2b560c62ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.79475\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_prediction=lr.predict(X_test)\n",
        "test_data_accuracy=accuracy_score(X_test_prediction,Y_test)"
      ],
      "metadata": {
        "id": "PsvDaaiotW08"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy:',test_data_accuracy)"
      ],
      "metadata": {
        "id": "qyGy5lCdthFE",
        "outputId": "0eb71800-026e-4340-b74f-ff50032eefc8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8025\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pred=rf.predict(X_train)\n",
        "train_data_accuracy=accuracy_score(X_train_pred,Y_train)"
      ],
      "metadata": {
        "id": "DYzDlkbHgBd7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy:',train_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2kSIAz2gGay",
        "outputId": "4571009a-7ae4-41ac-9267-df8b3112479e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.999875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_pred=rf.predict(X_test)\n",
        "test_data_accuracy=accuracy_score(X_test_pred,Y_test)"
      ],
      "metadata": {
        "id": "rzbLwsVA9ajV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy:',test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNgzXz2FAblH",
        "outputId": "4dad0b4a-5c08-49ed-99d5-1174d8b0f1cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.861\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pred1=xg.predict(X_train)\n",
        "train_data_accuracy=accuracy_score(X_train_pred1,Y_train)"
      ],
      "metadata": {
        "id": "eR8a2OdXBHN7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy:',train_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6omd8CGdgNQC",
        "outputId": "c5708ceb-e048-4863-aa76-08f7caad2ecb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.97475\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_pred1=xg.predict(X_test)\n",
        "test_data_accuracy=accuracy_score(X_test_pred1,Y_test)"
      ],
      "metadata": {
        "id": "i_-KFS3egXPC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy:',test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ygYL3gOgadq",
        "outputId": "3467fd18-3c5b-4f2f-8caf-db69cbffd5f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_pred2=sm.predict(X_train)\n",
        "train_data_accuracy=accuracy_score(X_train_pred2,Y_train)"
      ],
      "metadata": {
        "id": "0h-EPH6UgbNJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('acccuracy:',train_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FKmmTJ9qgdPp",
        "outputId": "26e7cb22-2a34-4cbf-f692-223a5de7b601"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "acccuracy: 0.79475\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_pred2=sm.predict(X_test)\n",
        "test_data_accuracy=accuracy_score(X_test_pred2,Y_test)"
      ],
      "metadata": {
        "id": "9MN5fcrSgfyh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('accuracy:',test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BU_mmp8sgh-h",
        "outputId": "0aa997af-ffe0-4a6f-b1d3-a69de1b2a265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy: 0.8025\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_data=pd.DataFrame({'Models':['lr','rf','xg','sm'],\n",
        "              'ACC':[accuracy_score(Y_test,X_test_prediction)*100,\n",
        "                     accuracy_score(Y_test,X_test_pred)*100,\n",
        "                     accuracy_score(Y_test,X_test_pred1)*100,\n",
        "                     accuracy_score(Y_test,X_test_pred2)*100,]})\n"
      ],
      "metadata": {
        "id": "WihgQ-FTgjqS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "t-J1vRfqhs9S",
        "outputId": "327a05f9-a114-4081-8390-b403815dda9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Models    ACC\n",
              "0     lr  80.25\n",
              "1     rf  86.10\n",
              "2     xg  84.80\n",
              "3     sm  80.25"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9d8b21be-62cc-4d81-be53-dfe2369c44ee\">\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>Models</th>\n",
              "      <th>ACC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>lr</td>\n",
              "      <td>80.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>rf</td>\n",
              "      <td>86.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>xg</td>\n",
              "      <td>84.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sm</td>\n",
              "      <td>80.25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d8b21be-62cc-4d81-be53-dfe2369c44ee')\"\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-9e80b22a-740e-434a-98e4-2c4341be0fec\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9e80b22a-740e-434a-98e4-2c4341be0fec')\"\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-9e80b22a-740e-434a-98e4-2c4341be0fec 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-9d8b21be-62cc-4d81-be53-dfe2369c44ee 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-9d8b21be-62cc-4d81-be53-dfe2369c44ee');\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": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test,X_test_prediction))\n",
        "print(classification_report(Y_test,X_test_pred))\n",
        "print(classification_report(Y_test,X_test_pred1))\n",
        "print(classification_report(Y_test,X_test_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ODguNIS_j1Xp",
        "outputId": "44ec3e2a-90e8-4d6c-ace1-0b508486b739"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      1.00      0.89      1605\n",
            "           1       0.00      0.00      0.00       395\n",
            "\n",
            "    accuracy                           0.80      2000\n",
            "   macro avg       0.40      0.50      0.45      2000\n",
            "weighted avg       0.64      0.80      0.71      2000\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.97      0.92      1605\n",
            "           1       0.76      0.43      0.55       395\n",
            "\n",
            "    accuracy                           0.86      2000\n",
            "   macro avg       0.82      0.70      0.73      2000\n",
            "weighted avg       0.85      0.86      0.85      2000\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.94      0.91      1605\n",
            "           1       0.67      0.46      0.54       395\n",
            "\n",
            "    accuracy                           0.85      2000\n",
            "   macro avg       0.77      0.70      0.73      2000\n",
            "weighted avg       0.83      0.85      0.84      2000\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      1.00      0.89      1605\n",
            "           1       0.00      0.00      0.00       395\n",
            "\n",
            "    accuracy                           0.80      2000\n",
            "   macro avg       0.40      0.50      0.45      2000\n",
            "weighted avg       0.64      0.80      0.71      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "MZq3oowqBhsZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = confusion_matrix(Y_test, X_test_prediction)\n",
        "\n",
        "print(cf_matrix)"
      ],
      "metadata": {
        "id": "rmE-l2Yn7J8l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23b6d96c-43a2-4ca9-fe5b-16cb1eb5df5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1605    0]\n",
            " [ 395    0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(cf_matrix, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "c-teOXPZj2U1",
        "outputId": "45e17783-c887-4f89-f495-c0e9c0ef5ef4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "model = ExtraTreesClassifier()\n",
        "model.fit(X,Y)\n",
        "print(model.feature_importances_)\n",
        "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
        "feat_importances.plot(kind='barh')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "7a3gi5GZLMEp",
        "outputId": "5c1867f3-7dcb-4aba-9916-7cc2c2f2ce21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.08147637 0.08135209 0.08008793 0.08199654 0.04080024 0.01787295\n",
            " 0.18191384 0.07268875 0.08854027 0.13534799 0.02283144 0.0344865\n",
            " 0.08060507]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}