CUSTOMER_CHURN_PREDICTION.ipynb

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        "<a href=\"https://colab.research.google.com/github/Aruvi-B/Machine-Learning/blob/main/CUSTOMER_CHURN_PREDICTION.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": null,
      "metadata": {
        "id": "HQuhb9JH0f85"
      },
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 423
        },
        "outputId": "cec0f5ae-ebce-41d5-aacb-f01152c135f0"
      },
      "outputs": [
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              "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
              "0             1    15634602   Hargrave          619    France  Female   42   \n",
              "1             2    15647311       Hill          608     Spain  Female   41   \n",
              "2             3    15619304       Onio          502    France  Female   42   \n",
              "3             4    15701354       Boni          699    France  Female   39   \n",
              "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
              "...         ...         ...        ...          ...       ...     ...  ...   \n",
              "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
              "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
              "9997       9998    15584532        Liu          709    France  Female   36   \n",
              "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
              "9999      10000    15628319     Walker          792    France  Female   28   \n",
              "\n",
              "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
              "0          2       0.00              1          1               1   \n",
              "1          1   83807.86              1          0               1   \n",
              "2          8  159660.80              3          1               0   \n",
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              "9997       7       0.00              1          0               1   \n",
              "9998       3   75075.31              2          1               0   \n",
              "9999       4  130142.79              1          1               0   \n",
              "\n",
              "      EstimatedSalary  Exited  \n",
              "0           101348.88       1  \n",
              "1           112542.58       0  \n",
              "2           113931.57       1  \n",
              "3            93826.63       0  \n",
              "4            79084.10       0  \n",
              "...               ...     ...  \n",
              "9995         96270.64       0  \n",
              "9996        101699.77       0  \n",
              "9997         42085.58       1  \n",
              "9998         92888.52       1  \n",
              "9999         38190.78       0  \n",
              "\n",
              "[10000 rows x 14 columns]"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"RowNumber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2886,\n        \"min\": 1,\n        \"max\": 10000,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          6253,\n          4685,\n          1732\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"France\",\n          \"Spain\",\n          \"Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\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\": \"NumOfProducts\",\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\": \"HasCrCard\",\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\": \"IsActiveMember\",\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\": \"EstimatedSalary\",\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\": \"Exited\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "data = pd.read_csv(\"/content/Churn_Modelling.csv\")\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xK-q8POv1DE5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "f8fb737a-d92d-4519-9907-6de1dd8e9a4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         RowNumber    CustomerId   CreditScore           Age        Tenure  \\\n",
              "count  10000.00000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
              "mean    5000.50000  1.569094e+07    650.528800     38.921800      5.012800   \n",
              "std     2886.89568  7.193619e+04     96.653299     10.487806      2.892174   \n",
              "min        1.00000  1.556570e+07    350.000000     18.000000      0.000000   \n",
              "25%     2500.75000  1.562853e+07    584.000000     32.000000      3.000000   \n",
              "50%     5000.50000  1.569074e+07    652.000000     37.000000      5.000000   \n",
              "75%     7500.25000  1.575323e+07    718.000000     44.000000      7.000000   \n",
              "max    10000.00000  1.581569e+07    850.000000     92.000000     10.000000   \n",
              "\n",
              "             Balance  NumOfProducts    HasCrCard  IsActiveMember  \\\n",
              "count   10000.000000   10000.000000  10000.00000    10000.000000   \n",
              "mean    76485.889288       1.530200      0.70550        0.515100   \n",
              "std     62397.405202       0.581654      0.45584        0.499797   \n",
              "min         0.000000       1.000000      0.00000        0.000000   \n",
              "25%         0.000000       1.000000      0.00000        0.000000   \n",
              "50%     97198.540000       1.000000      1.00000        1.000000   \n",
              "75%    127644.240000       2.000000      1.00000        1.000000   \n",
              "max    250898.090000       4.000000      1.00000        1.000000   \n",
              "\n",
              "       EstimatedSalary        Exited  \n",
              "count     10000.000000  10000.000000  \n",
              "mean     100090.239881      0.203700  \n",
              "std       57510.492818      0.402769  \n",
              "min          11.580000      0.000000  \n",
              "25%       51002.110000      0.000000  \n",
              "50%      100193.915000      0.000000  \n",
              "75%      149388.247500      0.000000  \n",
              "max      199992.480000      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9db11608-db62-45bf-91e9-82a7c4861b50\" class=\"colab-df-container\">\n",
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              "      <th>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>CreditScore</th>\n",
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              "      <th>Tenure</th>\n",
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              "      <th>Exited</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.00000</td>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5000.50000</td>\n",
              "      <td>1.569094e+07</td>\n",
              "      <td>650.528800</td>\n",
              "      <td>38.921800</td>\n",
              "      <td>5.012800</td>\n",
              "      <td>76485.889288</td>\n",
              "      <td>1.530200</td>\n",
              "      <td>0.70550</td>\n",
              "      <td>0.515100</td>\n",
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              "      <td>0.203700</td>\n",
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              "      <th>std</th>\n",
              "      <td>2886.89568</td>\n",
              "      <td>7.193619e+04</td>\n",
              "      <td>96.653299</td>\n",
              "      <td>10.487806</td>\n",
              "      <td>2.892174</td>\n",
              "      <td>62397.405202</td>\n",
              "      <td>0.581654</td>\n",
              "      <td>0.45584</td>\n",
              "      <td>0.499797</td>\n",
              "      <td>57510.492818</td>\n",
              "      <td>0.402769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.556570e+07</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.580000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2500.75000</td>\n",
              "      <td>1.562853e+07</td>\n",
              "      <td>584.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51002.110000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5000.50000</td>\n",
              "      <td>1.569074e+07</td>\n",
              "      <td>652.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>97198.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>100193.915000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7500.25000</td>\n",
              "      <td>1.575323e+07</td>\n",
              "      <td>718.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>127644.240000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>149388.247500</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10000.00000</td>\n",
              "      <td>1.581569e+07</td>\n",
              "      <td>850.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>250898.090000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>199992.480000</td>\n",
              "      <td>1.000000</td>\n",
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              "  .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",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\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-05167e4f-cc33-4fe6-a43f-2fc8a51c373c 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\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"RowNumber\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3603.743586536124,\n        \"min\": 1.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          10000.0,\n          5000.5,\n          7500.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"NumOfProducts\",\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\": \"HasCrCard\",\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\": \"IsActiveMember\",\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\": \"EstimatedSalary\",\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\": \"Exited\",\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": 43
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "20Sr0xKe1EvV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fe2487c-2b01-49ac-927d-81f136fc66ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   RowNumber        10000 non-null  int64  \n",
            " 1   CustomerId       10000 non-null  int64  \n",
            " 2   Surname          10000 non-null  object \n",
            " 3   CreditScore      10000 non-null  int64  \n",
            " 4   Geography        10000 non-null  object \n",
            " 5   Gender           10000 non-null  object \n",
            " 6   Age              10000 non-null  int64  \n",
            " 7   Tenure           10000 non-null  int64  \n",
            " 8   Balance          10000 non-null  float64\n",
            " 9   NumOfProducts    10000 non-null  int64  \n",
            " 10  HasCrCard        10000 non-null  int64  \n",
            " 11  IsActiveMember   10000 non-null  int64  \n",
            " 12  EstimatedSalary  10000 non-null  float64\n",
            " 13  Exited           10000 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(3)\n",
            "memory usage: 1.1+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwWvdksIkA0p",
        "outputId": "11c21a6c-8231-4b9f-9494-f2ec7858ec08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RowNumber          0\n",
              "CustomerId         0\n",
              "Surname            0\n",
              "CreditScore        0\n",
              "Geography          0\n",
              "Gender             0\n",
              "Age                0\n",
              "Tenure             0\n",
              "Balance            0\n",
              "NumOfProducts      0\n",
              "HasCrCard          0\n",
              "IsActiveMember     0\n",
              "EstimatedSalary    0\n",
              "Exited             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SKuC7U5U3N2c"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "data['Gender'] = le.fit_transform(data['Gender'])\n",
        "data['Geography'] = le.fit_transform(data['Geography'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dAnIEylf1Fsv"
      },
      "outputs": [],
      "source": [
        "data.isnull().sum()\n",
        "data = data.drop(\"Surname\", axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxvqGe3E1ImS",
        "outputId": "72198cf5-40e5-410d-c6e4-319217184594"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wEahab1D1Jb1"
      },
      "outputs": [],
      "source": [
        "X = data.drop(['Exited'], axis = 1)\n",
        "y = data['Exited']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hkM7_8KX2PeU"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Total\", len(X))\n",
        "print(\"Train size \", len(X_train))\n",
        "print(\"Test size \", len(X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZLGWmTxWy4Z",
        "outputId": "2312c06a-0ffd-43ee-c75d-7d25e16768f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total 10000\n",
            "Train size  8000\n",
            "Test size  2000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "model = SVC(kernel='rbf', C=1)\n",
        "model.fit(X_train, y_train)\n",
        "y_predicted = model.predict(X_test)\n",
        "print(\"Train data\", model.score(X_train,y_train))\n",
        "print(\"Test data\" , model.score(X_test,y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fyGG6HN_Wy05",
        "outputId": "651b5202-087f-4be9-cb25-0e7b09cabfec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data 0.7945\n",
            "Test data 0.8035\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_predicted)\n",
        "print(cm)\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "plt.figure(figsize=(4,3))\n",
        "sn.heatmap(cm, annot=True)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "IJ9jlklTWyxW",
        "outputId": "92d88251-9f35-4998-b702-cb53e715df7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1607    0]\n",
            " [ 393    0]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(20.72222222222222, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "tYMYQiFzhVVC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest\n",
        "rf_model = RandomForestClassifier(random_state=42)\n",
        "rf_model.fit(X_train_scaled, y_train.values.ravel())\n",
        "rf_predition = rf_model.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "uydfr5KhkKCc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Gradient Boosting\n",
        "gb_model = GradientBoostingClassifier(random_state=42)\n",
        "gb_model.fit(X_train_scaled, y_train.values.ravel())\n",
        "gb_predition = gb_model.predict(X_test_scaled)\n"
      ],
      "metadata": {
        "id": "oHxXCG5LkJ9S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "models = ['Random Forest', \"Gradient Boosting\"]\n",
        "predictions = [rf_predition, gb_predition]\n",
        "\n",
        "for model, pred in zip(models, predictions):\n",
        "    accuracy = accuracy_score(y_test, pred)\n",
        "    conf_matrix = confusion_matrix(y_test, pred)\n",
        "    classification_rep = classification_report(y_test, pred)\n",
        "\n",
        "    print(f\"Model: {model}\")\n",
        "    print(f\"Accuracy: {accuracy * 100}\")\n",
        "    print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "    print(\"Classification Report:\\n\", classification_rep)\n",
        "\n",
        "    print(\"__________________________________________________________________\")\n",
        "\n",
        "\n",
        "    # confusion matrix\n",
        "    plt.figure(figsize = (8, 6))\n",
        "    sns.heatmap(conf_matrix,annot = True, fmt = 'd', linewidth = 0.5, square = True)\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MzmJKBomhVRi",
        "outputId": "118ea866-ef34-45d6-afde-64d8ac6561b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: Random Forest\n",
            "Accuracy: 86.3\n",
            "Confusion Matrix:\n",
            " [[1552   55]\n",
            " [ 219  174]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.97      0.92      1607\n",
            "           1       0.76      0.44      0.56       393\n",
            "\n",
            "    accuracy                           0.86      2000\n",
            "   macro avg       0.82      0.70      0.74      2000\n",
            "weighted avg       0.85      0.86      0.85      2000\n",
            "\n",
            "__________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: Gradient Boosting\n",
            "Accuracy: 86.5\n",
            "Confusion Matrix:\n",
            " [[1552   55]\n",
            " [ 215  178]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.97      0.92      1607\n",
            "           1       0.76      0.45      0.57       393\n",
            "\n",
            "    accuracy                           0.86      2000\n",
            "   macro avg       0.82      0.71      0.74      2000\n",
            "weighted avg       0.86      0.86      0.85      2000\n",
            "\n",
            "__________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPpjs9CPoWdpGG3DbUxul16",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}