Bank_Customer_Churn_Model.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://github.com/YBIFoundation/Dataset/raw/main/Bank%20Churn%20Modelling.csv')"
      ],
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        "df.head()"
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          "data": {
            "text/plain": [
              "             Surname  CreditScore  Geography  Gender  Age  Tenure    Balance  \\\n",
              "CustomerId                                                                     \n",
              "15634602    Hargrave          619          2       1   42       2       0.00   \n",
              "15647311        Hill          608          0       1   41       1   83807.86   \n",
              "15619304        Onio          502          2       1   42       8  159660.80   \n",
              "15701354        Boni          699          2       1   39       1       0.00   \n",
              "15737888    Mitchell          850          0       1   43       2  125510.82   \n",
              "\n",
              "            Num Of Products  Has Credit Card  Is Active Member  \\\n",
              "CustomerId                                                       \n",
              "15634602                  1                1                 1   \n",
              "15647311                  1                0                 1   \n",
              "15619304                  3                1                 0   \n",
              "15701354                  2                0                 0   \n",
              "15737888                  1                1                 1   \n",
              "\n",
              "            Estimated Salary  Churn  \n",
              "CustomerId                           \n",
              "15634602           101348.88      1  \n",
              "15647311           112542.58      0  \n",
              "15619304           113931.57      1  \n",
              "15701354            93826.63      0  \n",
              "15737888            79084.10      0  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"CustomerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71936,\n        \"min\": 15565701,\n        \"max\": 15815690,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          15687492,\n          15736963,\n          15721730\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Surname\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2932,\n        \"samples\": [\n          \"McGuirk\",\n          \"Torkelson\",\n          \"Rapuluchukwu\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CreditScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96,\n        \"min\": 350,\n        \"max\": 850,\n        \"num_unique_values\": 460,\n        \"samples\": [\n          754,\n          533,\n          744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Geography\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 18,\n        \"max\": 92,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          61,\n          42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62397.405202385955,\n        \"min\": 0.0,\n        \"max\": 250898.09,\n        \"num_unique_values\": 6382,\n        \"samples\": [\n          117707.18,\n          133050.97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num Of Products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Has Credit Card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Is Active Member\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57510.49281769816,\n        \"min\": 11.58,\n        \"max\": 199992.48,\n        \"num_unique_values\": 9999,\n        \"samples\": [\n          100809.99,\n          95273.73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 15
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        "df.info()"
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      "execution_count": null,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   CustomerId        10000 non-null  int64  \n",
            " 1   Surname           10000 non-null  object \n",
            " 2   CreditScore       10000 non-null  int64  \n",
            " 3   Geography         10000 non-null  object \n",
            " 4   Gender            10000 non-null  object \n",
            " 5   Age               10000 non-null  int64  \n",
            " 6   Tenure            10000 non-null  int64  \n",
            " 7   Balance           10000 non-null  float64\n",
            " 8   Num Of Products   10000 non-null  int64  \n",
            " 9   Has Credit Card   10000 non-null  int64  \n",
            " 10  Is Active Member  10000 non-null  int64  \n",
            " 11  Estimated Salary  10000 non-null  float64\n",
            " 12  Churn             10000 non-null  int64  \n",
            "dtypes: float64(2), int64(8), object(3)\n",
            "memory usage: 1015.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "XjZ5GfNR7JrL",
        "outputId": "5f2090de-d779-44c0-b959-7feabb96dfe2"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         CustomerId   CreditScore           Age        Tenure        Balance  \\\n",
              "count  1.000000e+04  10000.000000  10000.000000  10000.000000   10000.000000   \n",
              "mean   1.569094e+07    650.528800     38.921800      5.012800   76485.889288   \n",
              "std    7.193619e+04     96.653299     10.487806      2.892174   62397.405202   \n",
              "min    1.556570e+07    350.000000     18.000000      0.000000       0.000000   \n",
              "25%    1.562853e+07    584.000000     32.000000      3.000000       0.000000   \n",
              "50%    1.569074e+07    652.000000     37.000000      5.000000   97198.540000   \n",
              "75%    1.575323e+07    718.000000     44.000000      7.000000  127644.240000   \n",
              "max    1.581569e+07    850.000000     92.000000     10.000000  250898.090000   \n",
              "\n",
              "       Num Of Products  Has Credit Card  Is Active Member  Estimated Salary  \\\n",
              "count     10000.000000      10000.00000      10000.000000      10000.000000   \n",
              "mean          1.530200          0.70550          0.515100     100090.239881   \n",
              "std           0.581654          0.45584          0.499797      57510.492818   \n",
              "min           1.000000          0.00000          0.000000         11.580000   \n",
              "25%           1.000000          0.00000          0.000000      51002.110000   \n",
              "50%           1.000000          1.00000          1.000000     100193.915000   \n",
              "75%           2.000000          1.00000          1.000000     149388.247500   \n",
              "max           4.000000          1.00000          1.000000     199992.480000   \n",
              "\n",
              "              Churn  \n",
              "count  10000.000000  \n",
              "mean       0.203700  \n",
              "std        0.402769  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max        1.000000  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Num Of Products</th>\n",
              "      <th>Has Credit Card</th>\n",
              "      <th>Is Active Member</th>\n",
              "      <th>Estimated Salary</th>\n",
              "      <th>Churn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.569094e+07</td>\n",
              "      <td>650.528800</td>\n",
              "      <td>38.921800</td>\n",
              "      <td>5.012800</td>\n",
              "      <td>76485.889288</td>\n",
              "      <td>1.530200</td>\n",
              "      <td>0.70550</td>\n",
              "      <td>0.515100</td>\n",
              "      <td>100090.239881</td>\n",
              "      <td>0.203700</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.193619e+04</td>\n",
              "      <td>96.653299</td>\n",
              "      <td>10.487806</td>\n",
              "      <td>2.892174</td>\n",
              "      <td>62397.405202</td>\n",
              "      <td>0.581654</td>\n",
              "      <td>0.45584</td>\n",
              "      <td>0.499797</td>\n",
              "      <td>57510.492818</td>\n",
              "      <td>0.402769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.556570e+07</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.580000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.562853e+07</td>\n",
              "      <td>584.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51002.110000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.569074e+07</td>\n",
              "      <td>652.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>97198.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100193.915000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.575323e+07</td>\n",
              "      <td>718.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>127644.240000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>149388.247500</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.581569e+07</td>\n",
              "      <td>850.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>250898.090000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>199992.480000</td>\n",
              "      <td>1.000000</td>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"CustomerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7244870.561655487,\n        \"min\": 10000.0,\n        \"max\": 15815690.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15690940.5694,\n          15690738.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CreditScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3346.7223968706257,\n        \"min\": 96.65329873613035,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          650.5288,\n          652.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3521.859127755016,\n        \"min\": 10.487806451704609,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.9218,\n          37.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3533.8732032133667,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.0128,\n          5.0,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 84316.21503683121,\n        \"min\": 0.0,\n        \"max\": 250898.09,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          10000.0,\n          76485.889288,\n          127644.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num Of Products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3534.9728350092923,\n        \"min\": 0.5816543579989906,\n        \"max\": 10000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          10000.0,\n          1.5302,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Has Credit Card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3535.3237513853414,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7055,\n          1.0,\n          0.4558404644751333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Is Active Member\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3535.3311475099704,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5151,\n          1.0,\n          0.49979692845891893\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68211.65700170657,\n        \"min\": 11.58,\n        \"max\": 199992.48,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          100090.239881,\n          100193.915,\n          10000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3535.452783887065,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2037,\n          1.0,\n          0.4027685839948609\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated('CustomerId').sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJhKtm0u7g1z",
        "outputId": "14ad261f-8d82-46fb-fa20-851e85451357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.set_index('CustomerId')"
      ],
      "metadata": {
        "id": "W_5rwDYN7qyA"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UN8hWrqSXzkX",
        "outputId": "8faf8e3d-5b7d-4e6d-c5e0-1f7d8425af51"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 10000 entries, 15634602 to 15628319\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Surname           10000 non-null  object \n",
            " 1   CreditScore       10000 non-null  int64  \n",
            " 2   Geography         10000 non-null  object \n",
            " 3   Gender            10000 non-null  object \n",
            " 4   Age               10000 non-null  int64  \n",
            " 5   Tenure            10000 non-null  int64  \n",
            " 6   Balance           10000 non-null  float64\n",
            " 7   Num Of Products   10000 non-null  int64  \n",
            " 8   Has Credit Card   10000 non-null  int64  \n",
            " 9   Is Active Member  10000 non-null  int64  \n",
            " 10  Estimated Salary  10000 non-null  float64\n",
            " 11  Churn             10000 non-null  int64  \n",
            "dtypes: float64(2), int64(7), object(3)\n",
            "memory usage: 1015.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Geography'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pq3yaDBTYC9Z",
        "outputId": "6a460d71-147b-4633-dd01-5b4f7a1c467b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Geography\n",
              "France     5014\n",
              "Germany    2509\n",
              "Spain      2477\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace({'Geography' : {'France': 2, 'Germany' : 1 , 'Spain' : 0}}, inplace = True)"
      ],
      "metadata": {
        "id": "-2HU-0pYYOeH"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LoZWsggSZaeL",
        "outputId": "2f9317e4-031d-4c53-b8ef-8937c500b219"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "Male      5457\n",
              "Female    4543\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace({'Gender' : {'Male' : 0, 'Female' : 1}}, inplace = True)"
      ],
      "metadata": {
        "id": "uOKItb0cZgFi"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Num Of Products'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hA5G7qXMZtEy",
        "outputId": "c869ee9b-3f01-4ad2-c1ae-97f9a5edf87b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Num Of Products\n",
              "1    5084\n",
              "2    4590\n",
              "3     266\n",
              "4      60\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace({'Num Of Products' : {1 : 0, 2 : 1, 3 : 1, 4 : 1}}, inplace = True)"
      ],
      "metadata": {
        "id": "bpOhhnYmaFRO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Has Credit Card'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RM2DR8WmamjJ",
        "outputId": "f9a95e55-bad0-4013-d6fb-ebc01a2cdf41"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Has Credit Card\n",
              "1    7055\n",
              "0    2945\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Is Active Member'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVpwjpEhawvP",
        "outputId": "2ac12f59-2a3e-4010-de0b-87454b53b0f3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Is Active Member\n",
              "1    5151\n",
              "0    4849\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[(df['Balance']==0), 'Churn'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7PVMpMMa2Wx",
        "outputId": "f9159cda-1207-4ba3-cf6e-7bdb24799b46"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn\n",
              "0    3117\n",
              "1     500\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Zero Balance'] = np.where(df['Balance']>0, 1, 0)"
      ],
      "metadata": {
        "id": "iPq_H5iGbOTG"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Zero Balance'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "zmT9eXz9bj9k",
        "outputId": "4d7caab7-4563-4400-a64e-7244939fb0e1"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['Churn', 'Geography']).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "nQTlOJdCbycJ",
        "outputId": "96aa45ee-d09b-46b1-e828-f2d6a6a2a631"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Surname  CreditScore  Gender   Age  Tenure  Balance  \\\n",
              "Churn Geography                                                        \n",
              "0     0             2064         2064    2064  2064    2064     2064   \n",
              "      1             1695         1695    1695  1695    1695     1695   \n",
              "      2             4204         4204    4204  4204    4204     4204   \n",
              "1     0              413          413     413   413     413      413   \n",
              "      1              814          814     814   814     814      814   \n",
              "      2              810          810     810   810     810      810   \n",
              "\n",
              "                 Num Of Products  Has Credit Card  Is Active Member  \\\n",
              "Churn Geography                                                       \n",
              "0     0                     2064             2064              2064   \n",
              "      1                     1695             1695              1695   \n",
              "      2                     4204             4204              4204   \n",
              "1     0                      413              413               413   \n",
              "      1                      814              814               814   \n",
              "      2                      810              810               810   \n",
              "\n",
              "                 Estimated Salary  Zero Balance  \n",
              "Churn Geography                                  \n",
              "0     0                      2064          2064  \n",
              "      1                      1695          1695  \n",
              "      2                      4204          4204  \n",
              "1     0                       413           413  \n",
              "      1                       814           814  \n",
              "      2                       810           810  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9907e433-cde8-47ea-a6fe-67d25d4b74c6\" 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></th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>Num Of Products</th>\n",
              "      <th>Has Credit Card</th>\n",
              "      <th>Is Active Member</th>\n",
              "      <th>Estimated Salary</th>\n",
              "      <th>Zero Balance</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Churn</th>\n",
              "      <th>Geography</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">0</th>\n",
              "      <th>0</th>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "      <td>2064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "      <td>4204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
              "      <th>0</th>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "      <td>413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "      <td>814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "      <td>810</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9907e433-cde8-47ea-a6fe-67d25d4b74c6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9907e433-cde8-47ea-a6fe-67d25d4b74c6 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-9907e433-cde8-47ea-a6fe-67d25d4b74c6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c8d873b4-ddc4-4657-ba84-4569a9eb3534')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c8d873b4-ddc4-4657-ba84-4569a9eb3534 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Surname\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CreditScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Num Of Products\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Has Credit Card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Is Active Member\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Estimated Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zero Balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1387,\n        \"min\": 413,\n        \"max\": 4204,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2064,\n          1695,\n          810\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Define Labels and features"
      ],
      "metadata": {
        "id": "t7V4TNTocDAB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWeZXc9YcJ5L",
        "outputId": "e30c8c95-e070-42d5-cc95-3fdb858d47c0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Surname', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',\n",
              "       'Balance', 'Num Of Products', 'Has Credit Card', 'Is Active Member',\n",
              "       'Estimated Salary', 'Churn', 'Zero Balance'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['Surname', 'Churn'], axis = 1)"
      ],
      "metadata": {
        "id": "YmrPiRLMcNn-"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['Churn']"
      ],
      "metadata": {
        "id": "Q9Sb8wUWcSrP"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rNJt8zcNcZkk",
        "outputId": "1e0e8aea-742b-4a83-d593-b727b36ee94d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 11), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Churn'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r3zTAY7Kcl3H",
        "outputId": "796d2311-6086-4d6f-c8bb-1d19e5ee7918"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn\n",
              "0    7963\n",
              "1    2037\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = 'Churn', data = df);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "73FEXaKFiw1l",
        "outputId": "66b2635a-b121-4ff3-9d8b-bc670e4f05ce"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape , y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0G5Wikb5jeTD",
        "outputId": "525d3aa6-4440-4ef4-9e55-04642816b27f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 11), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Undersampling"
      ],
      "metadata": {
        "id": "ndg_oBj-juB0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler"
      ],
      "metadata": {
        "id": "oWSUcsWPjxss"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rus = RandomUnderSampler(random_state = 2529)"
      ],
      "metadata": {
        "id": "BTD384ZRk9Ix"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_rus , y_rus = rus.fit_resample(X, y)"
      ],
      "metadata": {
        "id": "I4ClcrnHlC6C"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_rus.shape, y_rus.shape, X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbWXp96UlLZi",
        "outputId": "1ae2c7ac-9e9a-4358-ed60-8fd87d0acc48"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4074, 11), (4074,), (10000, 11), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPUt5e3JmF4N",
        "outputId": "556a65b4-44bd-43c6-b769-2e37c26861ba"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn\n",
              "0    7963\n",
              "1    2037\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_rus.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGsCWQbDmLG6",
        "outputId": "10012983-e5b6-4b8a-e3b4-f6e8fd5e013d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn\n",
              "0    2037\n",
              "1    2037\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_rus.plot(kind = 'hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "UVeCNVkumRyM",
        "outputId": "f5fe9595-300c-4828-fd23-1432c5dfebf7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Over Sampling"
      ],
      "metadata": {
        "id": "TjwK0tNImcb_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler"
      ],
      "metadata": {
        "id": "jyAvLPramfa5"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ros = random_over_sampler = RandomOverSampler(random_state = 2529)"
      ],
      "metadata": {
        "id": "HQU0vwrkmrX9"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_ros,y_ros = ros.fit_resample(X,y)"
      ],
      "metadata": {
        "id": "oQkcONCZmxIK"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_ros.shape, y_ros.shape, X.shape, y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85N5fbLEm7vr",
        "outputId": "3c79e1a3-28d3-4fc0-c946-f6ea48b79e9b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((15926, 11), (15926,), (10000, 11), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5goKwwQlnA1H",
        "outputId": "1f7fa531-b523-483f-a43a-222fa146e78b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn\n",
              "0    7963\n",
              "1    2037\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_ros.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HyEoZMIYnIIC",
        "outputId": "d21fe4fc-fa48-4de1-c438-6d0faf5d01c2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Churn\n",
              "1    7963\n",
              "0    7963\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_ros.plot(kind = 'hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "Pme86EODnONc",
        "outputId": "f80077ad-d071-4ba5-e2f8-589c2c2965f2"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train test split"
      ],
      "metadata": {
        "id": "I05rDsDBnnxU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "6jxuZAxmnnM1"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train,y_test=train_test_split(X,y, test_size=0.3,random_state=222529)"
      ],
      "metadata": {
        "id": "ewZs4T-FpuVM"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_rus, X_test_rus, y_train_rus,y_test_rus=train_test_split(X_rus,y_rus, test_size=0.3,random_state=222529)"
      ],
      "metadata": {
        "id": "dRCLSVuBqBR8"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_ros, X_test_ros, y_train_ros,y_test_ros=train_test_split(X_rus,y_rus, test_size=0.3,random_state=222529)"
      ],
      "metadata": {
        "id": "jvA_5IG7qJMu"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Standardize Features"
      ],
      "metadata": {
        "id": "EehrAXPKqgnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc=StandardScaler()"
      ],
      "metadata": {
        "id": "29LJ_n-JqQOt"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[['CreditScore','Age','Tenure','Balance','Estimated Salary']]=sc.fit_transform(X_train[['CreditScore','Age','Tenure','Balance','Estimated Salary']])"
      ],
      "metadata": {
        "id": "cfxmNnTRqXW9"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test[['CreditScore','Age','Tenure','Balance','Estimated Salary']]=sc.fit_transform(X_test[['CreditScore','Age','Tenure','Balance','Estimated Salary']])"
      ],
      "metadata": {
        "id": "t7zPjL0RqnfP"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_rus[['CreditScore','Age','Tenure','Balance','Estimated Salary']]=sc.fit_transform(X_train_rus[['CreditScore','Age','Tenure','Balance','Estimated Salary']])"
      ],
      "metadata": {
        "id": "tRdANc1AqviF"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_rus[['CreditScore','Age','Tenure','Balance','Estimated Salary']]=sc.fit_transform(X_test_rus[['CreditScore','Age','Tenure','Balance','Estimated Salary']])"
      ],
      "metadata": {
        "id": "2A4qXh1VraOn"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_ros[['CreditScore','Age','Tenure','Balance','Estimated Salary']]=sc.fit_transform(X_train_ros[['CreditScore','Age','Tenure','Balance','Estimated Salary']])"
      ],
      "metadata": {
        "id": "R4NfsVCtrkz9"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_ros[['CreditScore','Age','Tenure','Balance','Estimated Salary']]=sc.fit_transform(X_test_ros[['CreditScore','Age','Tenure','Balance','Estimated Salary']])"
      ],
      "metadata": {
        "id": "CezaoN6vrs1f"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "support vector machine classifier"
      ],
      "metadata": {
        "id": "hRcJcpMJr-zO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC"
      ],
      "metadata": {
        "id": "iD8sr7pyr3mG"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc=SVC()"
      ],
      "metadata": {
        "id": "o3C8-3BpsBe1"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "DP-B6EbLsFZW",
        "outputId": "76b5c645-a091-436c-ba15-eaa38a90ef3e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=svc.predict(X_test)"
      ],
      "metadata": {
        "id": "MhGQNCzxsMlD"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Accuracy"
      ],
      "metadata": {
        "id": "u0VC24Q5sXg5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "pDGApIqisPIF"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNBCdQiRsiic",
        "outputId": "73216ae0-c42e-40e9-eb4b-0662014fa601"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2359,   50],\n",
              "       [ 388,  203]])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZIGEombsjyq",
        "outputId": "29a85b1d-9dfd-4af5-eb01-53b9b9e60f67"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.98      0.92      2409\n",
            "           1       0.80      0.34      0.48       591\n",
            "\n",
            "    accuracy                           0.85      3000\n",
            "   macro avg       0.83      0.66      0.70      3000\n",
            "weighted avg       0.85      0.85      0.83      3000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "tnbhBHKKs1FV"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {'C':[0.1,1,10], 'gamma':[1,0.1,0.01], 'kernel':['rbf'], 'class_weight': ['balanced']}"
      ],
      "metadata": {
        "id": "UnV1PndMyvFK"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 2, cv=2)\n",
        "grid.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "RuNmJwAezUSr",
        "outputId": "a4e7754d-6095-4080-9370-d7489cc3c445"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 9 candidates, totalling 18 fits\n",
            "[CV] END ..C=0.1, class_weight=balanced, gamma=1, kernel=rbf; total time=   1.6s\n",
            "[CV] END ..C=0.1, class_weight=balanced, gamma=1, kernel=rbf; total time=   1.6s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   1.1s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   1.1s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   1.2s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   1.3s\n",
            "[CV] END ....C=1, class_weight=balanced, gamma=1, kernel=rbf; total time=   1.4s\n",
            "[CV] END ....C=1, class_weight=balanced, gamma=1, kernel=rbf; total time=   2.2s\n",
            "[CV] END ..C=1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   1.7s\n",
            "[CV] END ..C=1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   1.0s\n",
            "[CV] END .C=1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   1.3s\n",
            "[CV] END .C=1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   1.8s\n",
            "[CV] END ...C=10, class_weight=balanced, gamma=1, kernel=rbf; total time=   2.1s\n",
            "[CV] END ...C=10, class_weight=balanced, gamma=1, kernel=rbf; total time=   2.2s\n",
            "[CV] END .C=10, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   2.7s\n",
            "[CV] END .C=10, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   4.3s\n",
            "[CV] END C=10, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   2.1s\n",
            "[CV] END C=10, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   2.1s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2, estimator=SVC(),\n",
              "             param_grid={'C': [0.1, 1, 10], 'class_weight': ['balanced'],\n",
              "                         'gamma': [1, 0.1, 0.01], 'kernel': ['rbf']},\n",
              "             verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;class_weight&#x27;: [&#x27;balanced&#x27;],\n",
              "                         &#x27;gamma&#x27;: [1, 0.1, 0.01], &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=2, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;class_weight&#x27;: [&#x27;balanced&#x27;],\n",
              "                         &#x27;gamma&#x27;: [1, 0.1, 0.01], &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid.best_estimator_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qg5oIj-xzbiN",
        "outputId": "6852ea7d-fecb-4ec7-cef9-19b6fab1a28e"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC(C=0.1, class_weight='balanced', gamma=1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_predictions = grid.predict(X_test)"
      ],
      "metadata": {
        "id": "OLDGOSpjzq2r"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,grid_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcb_1eggzwzd",
        "outputId": "1fdf9fac-4289-495e-9d33-c84d9368fb4d"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1952,  457],\n",
              "       [ 236,  355]])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print (classification_report(y_test,grid_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e1hAlU2z-e6",
        "outputId": "6138ad7b-2cc7-4bc8-abf4-d2f84c6c3d18"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.81      0.85      2409\n",
            "           1       0.44      0.60      0.51       591\n",
            "\n",
            "    accuracy                           0.77      3000\n",
            "   macro avg       0.66      0.71      0.68      3000\n",
            "weighted avg       0.80      0.77      0.78      3000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model with Random undersampling"
      ],
      "metadata": {
        "id": "iZ8v2FZ30RUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svc_rus = SVC()"
      ],
      "metadata": {
        "id": "ZiDyaR4-0Qjw"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svc_rus.fit(X_train_rus, y_train_rus)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "RmvX4Prb0d3d",
        "outputId": "be0d7e0c-6ffe-44eb-f79b-557c3621843c"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rus = svc_rus.predict(X_test_rus)"
      ],
      "metadata": {
        "id": "9ACzAWr-0jkj"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Accuracy"
      ],
      "metadata": {
        "id": "i6nhrRAG0mzD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test_rus, y_pred_rus)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5O6DE1yA0osK",
        "outputId": "4262c561-cb8a-481e-b5d7-643449a83434"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[495, 118],\n",
              "       [199, 411]])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_rus, y_pred_rus))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyYI72te0thH",
        "outputId": "5b37c94b-cc69-460a-a483-ee66ab49ac6b"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.81      0.76       613\n",
            "           1       0.78      0.67      0.72       610\n",
            "\n",
            "    accuracy                           0.74      1223\n",
            "   macro avg       0.75      0.74      0.74      1223\n",
            "weighted avg       0.75      0.74      0.74      1223\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hyperarameter tuning"
      ],
      "metadata": {
        "id": "FSvZb6Bl0y_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {'C':[0.1,1,10], 'gamma':[1,0.1,0.01], 'kernel':['rbf'], 'class_weight': ['balanced']}"
      ],
      "metadata": {
        "id": "3h9mkUAE02bb"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_rus = GridSearchCV(SVC(), param_grid, refit = True, verbose = 2, cv=2)\n",
        "grid_rus.fit(X_train_rus, y_train_rus)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "-ehwBVcU09Ui",
        "outputId": "052621f1-daf0-486f-fcc4-63b54dc6ced1"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 9 candidates, totalling 18 fits\n",
            "[CV] END ..C=0.1, class_weight=balanced, gamma=1, kernel=rbf; total time=   0.4s\n",
            "[CV] END ..C=0.1, class_weight=balanced, gamma=1, kernel=rbf; total time=   0.2s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   0.4s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   0.4s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   0.5s\n",
            "[CV] END C=0.1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   0.8s\n",
            "[CV] END ....C=1, class_weight=balanced, gamma=1, kernel=rbf; total time=   1.1s\n",
            "[CV] END ....C=1, class_weight=balanced, gamma=1, kernel=rbf; total time=   0.8s\n",
            "[CV] END ..C=1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   0.5s\n",
            "[CV] END ..C=1, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   0.7s\n",
            "[CV] END .C=1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   0.6s\n",
            "[CV] END .C=1, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   0.5s\n",
            "[CV] END ...C=10, class_weight=balanced, gamma=1, kernel=rbf; total time=   0.5s\n",
            "[CV] END ...C=10, class_weight=balanced, gamma=1, kernel=rbf; total time=   0.5s\n",
            "[CV] END .C=10, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   0.3s\n",
            "[CV] END .C=10, class_weight=balanced, gamma=0.1, kernel=rbf; total time=   0.4s\n",
            "[CV] END C=10, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   0.3s\n",
            "[CV] END C=10, class_weight=balanced, gamma=0.01, kernel=rbf; total time=   0.2s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2, estimator=SVC(),\n",
              "             param_grid={'C': [0.1, 1, 10], 'class_weight': ['balanced'],\n",
              "                         'gamma': [1, 0.1, 0.01], 'kernel': ['rbf']},\n",
              "             verbose=2)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;class_weight&#x27;: [&#x27;balanced&#x27;],\n",
              "                         &#x27;gamma&#x27;: [1, 0.1, 0.01], &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=2, estimator=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: [0.1, 1, 10], &#x27;class_weight&#x27;: [&#x27;balanced&#x27;],\n",
              "                         &#x27;gamma&#x27;: [1, 0.1, 0.01], &#x27;kernel&#x27;: [&#x27;rbf&#x27;]},\n",
              "             verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(grid_rus.best_estimator_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ki04E8WJ1SQL",
        "outputId": "116cea97-7284-4a93-f531-c9fff3d84899"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVC(C=1, class_weight='balanced', gamma=0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_predictions_rus = grid_rus.predict(X_test_rus)"
      ],
      "metadata": {
        "id": "Dam5sCjF1cI3"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test_rus,grid_predictions_rus)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rk6Fgmlh1ffL",
        "outputId": "4dfa5364-f8a6-43ea-f603-54abc79e8b1f"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[500, 113],\n",
              "       [197, 413]])"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test_rus,grid_predictions_rus))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WleBmHlE1i8h",
        "outputId": "178875e8-d2d4-4393-e50a-9c32530f17d1"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.82      0.76       613\n",
            "           1       0.79      0.68      0.73       610\n",
            "\n",
            "    accuracy                           0.75      1223\n",
            "   macro avg       0.75      0.75      0.75      1223\n",
            "weighted avg       0.75      0.75      0.75      1223\n",
            "\n"
          ]
        }
      ]
    }
  ]
}