Bank_Customer_Churn_Prediction.ipynb

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      "source": [
        "# Problem Statement\n",
        "\n",
        "Customer churn, or the loss of clients, is a critical concern for banks, as retaining customers is often more cost-effective than acquiring new ones. The objective of this analysis is to develop a predictive model to identify bank customers likely to leave based on their profile and behavioral data. By proactively identifying these customers, banks can implement targeted retention strategies to reduce churn and improve customer satisfaction."
      ],
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        "!pip install pandas"
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      "execution_count": 9,
      "outputs": [
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          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
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      "source": [
        "# Import all the necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
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        "id": "KZU9tAp5b_Pf"
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      "execution_count": 10,
      "outputs": []
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      "source": [
        "# Upload the dataset file and check the first five rows of the data to understand the dataset\n",
        "data = pd.read_csv(\"/content/Bank_Customer_Churn_Prediction.csv\")\n",
        "data.head()"
      ],
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          "height": 206
        },
        "id": "NHBW59odb_L5",
        "outputId": "8b7a23f6-ce2a-4465-99cb-ba1b53c0bc58"
      },
      "execution_count": 11,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   customer_id  credit_score country  gender  age  tenure    balance  \\\n",
              "0     15634602           619  France  Female   42       2       0.00   \n",
              "1     15647311           608   Spain  Female   41       1   83807.86   \n",
              "2     15619304           502  France  Female   42       8  159660.80   \n",
              "3     15701354           699  France  Female   39       1       0.00   \n",
              "4     15737888           850   Spain  Female   43       2  125510.82   \n",
              "\n",
              "   products_number  credit_card  active_member  estimated_salary  churn  \n",
              "0                1            1              1         101348.88      1  \n",
              "1                1            0              1         112542.58      0  \n",
              "2                3            1              0         113931.57      1  \n",
              "3                2            0              0          93826.63      0  \n",
              "4                1            1              1          79084.10      0  "
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"customer_id\",\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\": \"credit_score\",\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\": \"country\",\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\": 62397.40520238623,\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\": \"products_number\",\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\": \"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\": \"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\": 57510.49281769822,\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": 11
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        "# Check for the dataset information, if there are any NULL values\n",
        "data.info()"
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        "outputId": "72f18c49-b3b6-4542-e1ec-ad6e5cd9abed"
      },
      "execution_count": 12,
      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 12 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customer_id       10000 non-null  int64  \n",
            " 1   credit_score      10000 non-null  int64  \n",
            " 2   country           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   products_number   10000 non-null  int64  \n",
            " 8   credit_card       10000 non-null  int64  \n",
            " 9   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(8), object(2)\n",
            "memory usage: 937.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the shape of the creditcard dataset\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Rf_Hryab_GE",
        "outputId": "4fa7707a-917f-4ec8-ae34-43565818320a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "missing_values = data.isnull().sum()\n",
        "\n",
        "missing_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "bkWPpl60dw9Y",
        "outputId": "53a5bae7-926a-40ae-cb1d-163ed5033014"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "customer_id         0\n",
              "credit_score        0\n",
              "country             0\n",
              "gender              0\n",
              "age                 0\n",
              "tenure              0\n",
              "balance             0\n",
              "products_number     0\n",
              "credit_card         0\n",
              "active_member       0\n",
              "estimated_salary    0\n",
              "churn               0\n",
              "dtype: int64"
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 14
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      "source": [
        "# Check if there are any duplicated values, if yes, remove them\n",
        "data.duplicated()"
      ],
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      "execution_count": 15,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       False\n",
              "1       False\n",
              "2       False\n",
              "3       False\n",
              "4       False\n",
              "        ...  \n",
              "9995    False\n",
              "9996    False\n",
              "9997    False\n",
              "9998    False\n",
              "9999    False\n",
              "Length: 10000, dtype: bool"
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              "<p>10000 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> bool</label>"
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# Check the summary of the statistical properties of the numerical columns in the dataset\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "iZM1UCIKd_15",
        "outputId": "b3be4aeb-7d63-4f84-eea6-5392725495a5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        customer_id  credit_score           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",
              "       products_number  credit_card  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",
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              "    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",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      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-5171bf73-0feb-4e16-bcc3-56d97a0356d1 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\": \"customer_id\",\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\": \"credit_score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3346.722396870626,\n        \"min\": 96.65329873613061,\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\": 10.487806451704591,\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\": 3533.873203213367,\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\": \"products_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3534.972835009293,\n        \"min\": 0.5816543579989936,\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\": \"credit_card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3535.323751385342,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7055,\n          1.0,\n          0.45584046447514237\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"active_member\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3535.33114750997,\n        \"min\": 0.0,\n        \"max\": 10000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5151,\n          1.0,\n          0.4997969284589202\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          0.40276858399484955\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode categorical variables : We have two categorical features: country and gender.\n",
        "data_encoded = pd.get_dummies(data, columns=['country'], drop_first=True)\n",
        "\n",
        "# Label encode 'gender' (Female = 0, Male = 1)\n",
        "data_encoded['gender'] = data_encoded['gender'].map({'Female': 0, 'Male': 1})\n",
        "\n",
        "# Display the encoded DataFrame\n",
        "print(\"Encoded DataFrame:\")  # Print a descriptive label\n",
        "print(data_encoded)  # Print the DataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1pTZqBa1iyll",
        "outputId": "08726825-89bd-4d3b-c4eb-cddfd3200f82"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoded DataFrame:\n",
            "      customer_id  credit_score  gender  age  tenure    balance  \\\n",
            "0        15634602           619       0   42       2       0.00   \n",
            "1        15647311           608       0   41       1   83807.86   \n",
            "2        15619304           502       0   42       8  159660.80   \n",
            "3        15701354           699       0   39       1       0.00   \n",
            "4        15737888           850       0   43       2  125510.82   \n",
            "...           ...           ...     ...  ...     ...        ...   \n",
            "9995     15606229           771       1   39       5       0.00   \n",
            "9996     15569892           516       1   35      10   57369.61   \n",
            "9997     15584532           709       0   36       7       0.00   \n",
            "9998     15682355           772       1   42       3   75075.31   \n",
            "9999     15628319           792       0   28       4  130142.79   \n",
            "\n",
            "      products_number  credit_card  active_member  estimated_salary  churn  \\\n",
            "0                   1            1              1         101348.88      1   \n",
            "1                   1            0              1         112542.58      0   \n",
            "2                   3            1              0         113931.57      1   \n",
            "3                   2            0              0          93826.63      0   \n",
            "4                   1            1              1          79084.10      0   \n",
            "...               ...          ...            ...               ...    ...   \n",
            "9995                2            1              0          96270.64      0   \n",
            "9996                1            1              1         101699.77      0   \n",
            "9997                1            0              1          42085.58      1   \n",
            "9998                2            1              0          92888.52      1   \n",
            "9999                1            1              0          38190.78      0   \n",
            "\n",
            "      country_Germany  country_Spain  \n",
            "0               False          False  \n",
            "1               False           True  \n",
            "2               False          False  \n",
            "3               False          False  \n",
            "4               False           True  \n",
            "...               ...            ...  \n",
            "9995            False          False  \n",
            "9996            False          False  \n",
            "9997            False          False  \n",
            "9998             True          False  \n",
            "9999            False          False  \n",
            "\n",
            "[10000 rows x 13 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the display function from IPython.display\n",
        "from IPython.display import display\n",
        "\n",
        "display(data_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "6NSPbs7Kj_DE",
        "outputId": "de055c55-4ad9-4216-ebbd-13985d61dd54"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      customer_id  credit_score  gender  age  tenure    balance  \\\n",
              "0        15634602           619       0   42       2       0.00   \n",
              "1        15647311           608       0   41       1   83807.86   \n",
              "2        15619304           502       0   42       8  159660.80   \n",
              "3        15701354           699       0   39       1       0.00   \n",
              "4        15737888           850       0   43       2  125510.82   \n",
              "...           ...           ...     ...  ...     ...        ...   \n",
              "9995     15606229           771       1   39       5       0.00   \n",
              "9996     15569892           516       1   35      10   57369.61   \n",
              "9997     15584532           709       0   36       7       0.00   \n",
              "9998     15682355           772       1   42       3   75075.31   \n",
              "9999     15628319           792       0   28       4  130142.79   \n",
              "\n",
              "      products_number  credit_card  active_member  estimated_salary  churn  \\\n",
              "0                   1            1              1         101348.88      1   \n",
              "1                   1            0              1         112542.58      0   \n",
              "2                   3            1              0         113931.57      1   \n",
              "3                   2            0              0          93826.63      0   \n",
              "4                   1            1              1          79084.10      0   \n",
              "...               ...          ...            ...               ...    ...   \n",
              "9995                2            1              0          96270.64      0   \n",
              "9996                1            1              1         101699.77      0   \n",
              "9997                1            0              1          42085.58      1   \n",
              "9998                2            1              0          92888.52      1   \n",
              "9999                1            1              0          38190.78      0   \n",
              "\n",
              "      country_Germany  country_Spain  \n",
              "0               False          False  \n",
              "1               False           True  \n",
              "2               False          False  \n",
              "3               False          False  \n",
              "4               False           True  \n",
              "...               ...            ...  \n",
              "9995            False          False  \n",
              "9996            False          False  \n",
              "9997            False          False  \n",
              "9998             True          False  \n",
              "9999            False          False  \n",
              "\n",
              "[10000 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8ddf367-8f26-41e3-a901-af5e937662dd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "</style>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>credit_score</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>balance</th>\n",
              "      <th>products_number</th>\n",
              "      <th>credit_card</th>\n",
              "      <th>active_member</th>\n",
              "      <th>estimated_salary</th>\n",
              "      <th>churn</th>\n",
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              "      <th>country_Spain</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8ddf367-8f26-41e3-a901-af5e937662dd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_encoded')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_encoded",
              "summary": "{\n  \"name\": \"data_encoded\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"customer_id\",\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\": \"credit_score\",\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\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\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\": 62397.40520238623,\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\": \"products_number\",\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\": \"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\": \"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\": 57510.49281769822,\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      \"column\": \"country_Germany\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country_Spain\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Select numerical columns for scaling\n",
        "numerical_cols = ['credit_score', 'age', 'tenure', 'balance', 'products_number', 'credit_card',\n",
        "                  'active_member', 'estimated_salary']\n",
        "\n",
        "# Initialize StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Scale the selected numerical columns\n",
        "data_encoded[numerical_cols] = scaler.fit_transform(data_encoded[numerical_cols])\n",
        "\n",
        "# Use the display function from IPython.display\n",
        "from IPython.display import display\n",
        "\n",
        "display(data_encoded)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "0b5iOR3dj3nY",
        "outputId": "5ccf84c6-1525-4a2f-df96-d342a7dd9bf7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      customer_id  credit_score  gender       age    tenure   balance  \\\n",
              "0        15634602     -0.326221       0  0.293517 -1.041760 -1.225848   \n",
              "1        15647311     -0.440036       0  0.198164 -1.387538  0.117350   \n",
              "2        15619304     -1.536794       0  0.293517  1.032908  1.333053   \n",
              "3        15701354      0.501521       0  0.007457 -1.387538 -1.225848   \n",
              "4        15737888      2.063884       0  0.388871 -1.041760  0.785728   \n",
              "...           ...           ...     ...       ...       ...       ...   \n",
              "9995     15606229      1.246488       1  0.007457 -0.004426 -1.225848   \n",
              "9996     15569892     -1.391939       1 -0.373958  1.724464 -0.306379   \n",
              "9997     15584532      0.604988       0 -0.278604  0.687130 -1.225848   \n",
              "9998     15682355      1.256835       1  0.293517 -0.695982 -0.022608   \n",
              "9999     15628319      1.463771       0 -1.041433 -0.350204  0.859965   \n",
              "\n",
              "      products_number  credit_card  active_member  estimated_salary  churn  \\\n",
              "0           -0.911583     0.646092       0.970243          0.021886      1   \n",
              "1           -0.911583    -1.547768       0.970243          0.216534      0   \n",
              "2            2.527057     0.646092      -1.030670          0.240687      1   \n",
              "3            0.807737    -1.547768      -1.030670         -0.108918      0   \n",
              "4           -0.911583     0.646092       0.970243         -0.365276      0   \n",
              "...               ...          ...            ...               ...    ...   \n",
              "9995         0.807737     0.646092      -1.030670         -0.066419      0   \n",
              "9996        -0.911583     0.646092       0.970243          0.027988      0   \n",
              "9997        -0.911583    -1.547768       0.970243         -1.008643      1   \n",
              "9998         0.807737     0.646092      -1.030670         -0.125231      1   \n",
              "9999        -0.911583     0.646092      -1.030670         -1.076370      0   \n",
              "\n",
              "      country_Germany  country_Spain  \n",
              "0               False          False  \n",
              "1               False           True  \n",
              "2               False          False  \n",
              "3               False          False  \n",
              "4               False           True  \n",
              "...               ...            ...  \n",
              "9995            False          False  \n",
              "9996            False          False  \n",
              "9997            False          False  \n",
              "9998             True          False  \n",
              "9999            False          False  \n",
              "\n",
              "[10000 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-490147ff-0c89-4f4c-86c8-efafabc81833\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>credit_score</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>tenure</th>\n",
              "      <th>balance</th>\n",
              "      <th>products_number</th>\n",
              "      <th>credit_card</th>\n",
              "      <th>active_member</th>\n",
              "      <th>estimated_salary</th>\n",
              "      <th>churn</th>\n",
              "      <th>country_Germany</th>\n",
              "      <th>country_Spain</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15634602</td>\n",
              "      <td>-0.326221</td>\n",
              "      <td>0</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>-1.041760</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>0.021886</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15647311</td>\n",
              "      <td>-0.440036</td>\n",
              "      <td>0</td>\n",
              "      <td>0.198164</td>\n",
              "      <td>-1.387538</td>\n",
              "      <td>0.117350</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>0.216534</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15619304</td>\n",
              "      <td>-1.536794</td>\n",
              "      <td>0</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>1.032908</td>\n",
              "      <td>1.333053</td>\n",
              "      <td>2.527057</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>0.240687</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15701354</td>\n",
              "      <td>0.501521</td>\n",
              "      <td>0</td>\n",
              "      <td>0.007457</td>\n",
              "      <td>-1.387538</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>0.807737</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-0.108918</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15737888</td>\n",
              "      <td>2.063884</td>\n",
              "      <td>0</td>\n",
              "      <td>0.388871</td>\n",
              "      <td>-1.041760</td>\n",
              "      <td>0.785728</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>-0.365276</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>15606229</td>\n",
              "      <td>1.246488</td>\n",
              "      <td>1</td>\n",
              "      <td>0.007457</td>\n",
              "      <td>-0.004426</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>0.807737</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-0.066419</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>15569892</td>\n",
              "      <td>-1.391939</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.373958</td>\n",
              "      <td>1.724464</td>\n",
              "      <td>-0.306379</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>0.027988</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>15584532</td>\n",
              "      <td>0.604988</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.278604</td>\n",
              "      <td>0.687130</td>\n",
              "      <td>-1.225848</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>-1.547768</td>\n",
              "      <td>0.970243</td>\n",
              "      <td>-1.008643</td>\n",
              "      <td>1</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>15682355</td>\n",
              "      <td>1.256835</td>\n",
              "      <td>1</td>\n",
              "      <td>0.293517</td>\n",
              "      <td>-0.695982</td>\n",
              "      <td>-0.022608</td>\n",
              "      <td>0.807737</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-0.125231</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>15628319</td>\n",
              "      <td>1.463771</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.041433</td>\n",
              "      <td>-0.350204</td>\n",
              "      <td>0.859965</td>\n",
              "      <td>-0.911583</td>\n",
              "      <td>0.646092</td>\n",
              "      <td>-1.030670</td>\n",
              "      <td>-1.076370</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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   \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000050003750302,\n        \"min\": -1.9949687539344934,\n        \"max\": 5.0611969579618314,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          2.105235646221479,\n          0.2935174228967471\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037503126,\n        \"min\": -1.7333154938995745,\n        \"max\": 1.7244635794717436,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.3413519501232164,\n          -1.0417596792253108\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"balance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037502886,\n        \"min\": -1.2258476714090278,\n        \"max\": 2.7953233217054723,\n        \"num_unique_values\": 6382,\n        \"samples\": [\n          0.6606581122631684,\n          0.9065747044239153\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"products_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000050003750326,\n        \"min\": -0.9115834940401767,\n        \"max\": 4.246376675934418,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.52705661927622,\n          4.246376675934418\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"credit_card\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037503361,\n        \"min\": -1.5477679860172513,\n        \"max\": 0.646091668153197,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -1.5477679860172513,\n          0.646091668153197\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"active_member\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000500037503148,\n        \"min\": -1.0306701134001208,\n        \"max\": 0.9702425509371355,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -1.0306701134001208,\n          0.9702425509371355\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estimated_salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.00005000375031,\n        \"min\": -1.7402678934881386,\n        \"max\": 1.7372001301113063,\n        \"num_unique_values\": 9999,\n        \"samples\": [\n          0.012515735371750046,\n          -0.08375429401772011\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      \"column\": \"country_Germany\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country_Spain\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Define features (X) and target (y)\n",
        "X = data_encoded.drop(columns=['customer_id', 'churn'])  # Drop 'customer_id' and target 'churn'\n",
        "y = data_encoded['churn']\n",
        "\n",
        "# Split the data into training and testing sets (80% train, 20% test)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Display the shapes of the resulting datasets\n",
        "(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TTF4z26kZj_",
        "outputId": "08e1cda2-2d12-4b19-b541-bcf0b0e7b329"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8000, 11), (2000, 11), (8000,), (2000,))"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Initialize the logistic regression model\n",
        "model = LogisticRegression(random_state=42)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "4sMMFJ-Zkh3z",
        "outputId": "f72199ea-ea57-42ea-9be2-8cbfca56f109"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "# Print the accuracy of the LR model\n",
        "print(f\"Logistic Regression Accuracy: {accuracy * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M1G4XBABkrYB",
        "outputId": "538a0286-7dde-41df-ffa9-a060438e331e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 81.15%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get classification report\n",
        "class_report = classification_report(y_test, y_pred)\n",
        "\n",
        "# Print the classification report\n",
        "print(\"Classification Report:\")\n",
        "print(class_report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3dqdC5qk5C1",
        "outputId": "cd36215c-be5e-46f5-97ab-bfb3d97d10dc"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.96      0.89      1607\n",
            "           1       0.56      0.20      0.30       393\n",
            "\n",
            "    accuracy                           0.81      2000\n",
            "   macro avg       0.69      0.58      0.59      2000\n",
            "weighted avg       0.78      0.81      0.77      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Print the confusion matrix\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8UrUpBxlEFz",
        "outputId": "ae3b1309-2539-4ef4-81b6-744b221635e0"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[1544   63]\n",
            " [ 314   79]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['No Churn', 'Churn'], yticklabels=['No Churn', 'Churn'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "IpHr6ljQlO9g",
        "outputId": "21f50625-b37e-4d12-f924-80cdbb7190b6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion Matrix: To see the true positives, false positives, false negatives and false positives."
      ],
      "metadata": {
        "id": "EtdPSpgQltC1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot ROC Curve\n",
        "fpr, tpr, _ = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "nZ-N9awillfp",
        "outputId": "b69b1174-f5ea-4ef1-e3e6-6a8111c2e02a"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC Curve: To evaluate the model's ability to distinguish between the classes. The Receiver Operating Characteristic curve illustrates the model's performance at various threshold levels. The area under the curve (AUC) value indicates how well the model distinguishes between the two classes, with a value closer to 1 being ideal."
      ],
      "metadata": {
        "id": "Z5YYOG5Ol9Do"
      }
    }
  ]
}