Churn_Prediction_with_ML.ipynb

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        "\n",
        "#**Telecom Customer Churn Prediction Using Machine Learning!**\n",
        "\n",
        "---\n"
      ],
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        "The **Telco Customer Churn Dataset** is commonly used for predicting customer retention in the telecommunications industry. Here’s a breakdown of the dataset and its significance:\n",
        "\n",
        "\n",
        "**Dataset Overview:**\n",
        "\n",
        "*Rows:* Each row represents a unique customer.\n",
        "\n",
        "*Columns:* Contain information about customer demographics, account details, services subscribed to, and whether the customer has churned (left the service).\n",
        "\n"
      ],
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        "**Key Features:**\n",
        "\n",
        "*Demographic Information:* gender, SeniorCitizen, Partner, Dependents.\n",
        "\n",
        "*Service Details:* PhoneService, MultipleLines, InternetService,\n",
        "OnlineSecurity, TechSupport, etc.\n",
        "\n",
        "*Account Information:* tenure (how long the customer has been with the company), Contract, MonthlyCharges, TotalCharges.\n",
        "\n",
        "*Target Variable:* Churn – whether the customer left the company or stayed (Yes/No)."
      ],
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        "**Objective:**\n",
        "\n",
        "Main goal is to predict customer churn, which refers to whether a customer will leave the company based on their historical data.\n",
        "\n",
        "---\n",
        "\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "## Loading libraries and data"
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        "# Importing necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import classification_report, roc_auc_score, confusion_matrix, RocCurveDisplay, accuracy_score\n",
        "from sklearn.metrics import precision_score, recall_score"
      ],
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      "execution_count": 45,
      "outputs": []
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      "source": [
        "df=pd.read_csv(\"/content/WA_Fn-UseC_-Telco-Customer-Churn.csv\")"
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            "text/plain": [
              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
              "\n",
              "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
              "0  No phone service             DSL             No  ...               No   \n",
              "1                No             DSL            Yes  ...              Yes   \n",
              "2                No             DSL            Yes  ...               No   \n",
              "3  No phone service             DSL            Yes  ...              Yes   \n",
              "4                No     Fiber optic             No  ...               No   \n",
              "\n",
              "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
              "0          No          No              No  Month-to-month              Yes   \n",
              "1          No          No              No        One year               No   \n",
              "2          No          No              No  Month-to-month              Yes   \n",
              "3         Yes          No              No        One year               No   \n",
              "4          No          No              No  Month-to-month              Yes   \n",
              "\n",
              "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
              "0           Electronic check          29.85         29.85    No  \n",
              "1               Mailed check          56.95        1889.5    No  \n",
              "2               Mailed check          53.85        108.15   Yes  \n",
              "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
              "4           Electronic check          70.70        151.65   Yes  \n",
              "\n",
              "[5 rows x 21 columns]"
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      "source": [
        "df['TotalRevenue'] = df['MonthlyCharges'] * df['tenure']"
      ],
      "metadata": {
        "id": "BBWwiXxnM7WH"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "_rC94cVuNTP-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSWpzNkLNSN1",
        "outputId": "4a806092-f5e3-45d7-eae5-95a242213e86"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 22 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   object \n",
            " 20  Churn             7043 non-null   object \n",
            " 21  TotalRevenue      7043 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(18)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBczt-n-aJD2",
        "outputId": "ac6864b5-a7e9-42de-80da-fa2a9cbefabd"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nChecking for missing values:\")\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x55uSsZcNVYP",
        "outputId": "0d03069f-dc75-4d42-f17f-7b29865f895f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Checking for missing values:\n",
            "customerID          0\n",
            "gender              0\n",
            "SeniorCitizen       0\n",
            "Partner             0\n",
            "Dependents          0\n",
            "tenure              0\n",
            "PhoneService        0\n",
            "MultipleLines       0\n",
            "InternetService     0\n",
            "OnlineSecurity      0\n",
            "OnlineBackup        0\n",
            "DeviceProtection    0\n",
            "TechSupport         0\n",
            "StreamingTV         0\n",
            "StreamingMovies     0\n",
            "Contract            0\n",
            "PaperlessBilling    0\n",
            "PaymentMethod       0\n",
            "MonthlyCharges      0\n",
            "TotalCharges        0\n",
            "Churn               0\n",
            "TotalRevenue        0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "gW-K_B4APdqt",
        "outputId": "1a9f644c-e760-444d-c072-f38290008d99"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       SeniorCitizen       tenure  MonthlyCharges  TotalRevenue\n",
              "count    7043.000000  7043.000000     7043.000000   7043.000000\n",
              "mean        0.162147    32.371149       64.761692   2279.581350\n",
              "std         0.368612    24.559481       30.090047   2264.729447\n",
              "min         0.000000     0.000000       18.250000      0.000000\n",
              "25%         0.000000     9.000000       35.500000    394.000000\n",
              "50%         0.000000    29.000000       70.350000   1393.600000\n",
              "75%         0.000000    55.000000       89.850000   3786.100000\n",
              "max         1.000000    72.000000      118.750000   8550.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7cef5466-e471-40df-8915-3aa48e5624f3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SeniorCitizen</th>\n",
              "      <th>tenure</th>\n",
              "      <th>MonthlyCharges</th>\n",
              "      <th>TotalRevenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "      <td>7043.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.162147</td>\n",
              "      <td>32.371149</td>\n",
              "      <td>64.761692</td>\n",
              "      <td>2279.581350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.368612</td>\n",
              "      <td>24.559481</td>\n",
              "      <td>30.090047</td>\n",
              "      <td>2264.729447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.250000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>35.500000</td>\n",
              "      <td>394.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>70.350000</td>\n",
              "      <td>1393.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>89.850000</td>\n",
              "      <td>3786.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>118.750000</td>\n",
              "      <td>8550.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7cef5466-e471-40df-8915-3aa48e5624f3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7cef5466-e471-40df-8915-3aa48e5624f3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-f10c4614-d344-4328-8a67-006db552942e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f10c4614-d344-4328-8a67-006db552942e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      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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              "      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-f10c4614-d344-4328-8a67-006db552942e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2489.9992387084,\n        \"min\": 0.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1621468124378816,\n          1.0,\n          0.36861160561002687\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2478.9752758409018,\n        \"min\": 0.0,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.37114865824223,\n          29.0,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2468.7047672837775,\n        \"min\": 18.25,\n        \"max\": 7043.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          64.76169246059918,\n          70.35,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalRevenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3089.88534794996,\n        \"min\": 0.0,\n        \"max\": 8550.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2279.5813502768706,\n          1393.6000000000001,\n          7043.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "8moLXzo4Na3y",
        "outputId": "b0140bd2-dd7c-4929-83b4-8e15275eceaf"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gender\n",
              "Male      3555\n",
              "Female    3488\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>3555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>3488</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8),dpi=100)\n",
        "size=df['gender'].value_counts()\n",
        "labels='Male','Female'\n",
        "color='lightgreen','peru'\n",
        "explode=[0.06,0]\n",
        "plt.pie(size,colors=color,labels=labels,explode=explode,autopct='%.2f%%',shadow=True)\n",
        "plt.title(\"Gender Distribution\",size=20)\n",
        "plt.legend(loc=(1.1,0.8))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "SxdO3CkHNa7A",
        "outputId": "ba24ace3-5ca2-4875-c3ff-ef86c9315d77"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset appears to have a nearly equal distribution between Male (50.48%) and Female (49.52%) customers.\n",
        "\n",
        "This balance indicates that gender representation in the dataset is not biased, which is beneficial for building unbiased predictive models."
      ],
      "metadata": {
        "id": "BESpVv4IbPTw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Contract'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "v8PEkvE4Phrb",
        "outputId": "2322c2dc-3d8e-4a38-8417-cc1bcd022c8b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Contract\n",
              "Month-to-month    3875\n",
              "Two year          1695\n",
              "One year          1473\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Contract</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Month-to-month</th>\n",
              "      <td>3875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Two year</th>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>One year</th>\n",
              "      <td>1473</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,8),dpi=100)\n",
        "size=df['Contract'].value_counts()\n",
        "labels='Month-to-month','Two year','One year'\n",
        "color='salmon','tan','teal'\n",
        "explode=[0.06,0,0]\n",
        "plt.pie(size,colors=color,labels=labels,explode=explode,autopct='%.2f%%',shadow=True)\n",
        "plt.title(\"Contract Distribution\",size=20)\n",
        "plt.legend(loc=(1.1,0.8))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "YGCMb2J-PQiI",
        "outputId": "412f8a60-8e39-44aa-fa3a-5a323ca9c05a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A significant 55.02% of customers have opted for month-to-month contracts. This may indicate a preference for flexibility or hesitance to commit to longer-term contracts.\n",
        "\n",
        "Customers with month-to-month contracts are often more likely to churn compared to those with longer-term contracts, as they can easily switch to competitors or cancel services without penalties.\n",
        "\n",
        "This suggests that contract type could be a significant predictor in churn modeling."
      ],
      "metadata": {
        "id": "-JdGqhV8bnap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['InternetService'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "nzvJwWXjQnlC",
        "outputId": "e8e7cefb-9d53-44c4-b6f4-82d95ba3d0a5"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InternetService\n",
              "Fiber optic    3096\n",
              "DSL            2421\n",
              "No             1526\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InternetService</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Fiber optic</th>\n",
              "      <td>3096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DSL</th>\n",
              "      <td>2421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>1526</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6),dpi=150)\n",
        "plt.subplot(1,2,1)\n",
        "palette=\"tan\",\"deeppink\",\"red\"\n",
        "sns.countplot(x='InternetService', data=df,palette=palette,hue=\"Churn\")\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "size=df['InternetService'].value_counts()\n",
        "labels='Fiber optic','DSL','No'\n",
        "color='crimson','yellow','coral'\n",
        "explode=[0.06,0,0]\n",
        "plt.pie(size,colors=color,labels=labels,explode=explode,autopct='%.2f%%',shadow=True)\n",
        "plt.legend(loc=(1.1,0.8))\n",
        "plt.suptitle(\"Internet Service Distribution and Churn\",size=20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "uXSE-hE-PQeq",
        "outputId": "db3bc87f-0a49-4144-d41c-40e38204d14f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x900 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:**\n",
        "\n",
        "i. *Fiber Optic and Churn:*\n",
        "\n",
        "Fiber Optic is the most popular internet service type but also exhibits the highest churn rates. This suggests dissatisfaction among Fiber Optic customers.\n",
        "\n",
        "Potential reasons could include cost, quality of service, or competition from alternative providers.\n",
        "\n",
        "ii. *No Internet Service:*\n",
        "\n",
        "Customers without internet service show the least churn, likely because they are subscribed to basic services such as TV or phone, which may have lower alternatives.\n",
        "\n",
        "iii. *Retention Opportunity:*\n",
        "\n",
        "For Fiber Optic customers, retention strategies such as offering discounts, improving service quality, or addressing key complaints could help reduce churn."
      ],
      "metadata": {
        "id": "Yr9D4uWfcLBY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize churn distribution\n",
        "plt.figure(figsize=(10,5),dpi=120)\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "color=\"salmon\",\"deeppink\"\n",
        "sns.countplot(x='Churn', data=df,palette=color)\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "size=df['Churn'].value_counts()\n",
        "labels='No','Yes'\n",
        "color='royalblue','aquamarine'\n",
        "explode=[0.06,0]\n",
        "plt.pie(size,colors=color,labels=labels,explode=explode,autopct='%.2f%%',shadow=True)\n",
        "plt.legend()\n",
        "\n",
        "plt.suptitle(\"Churn Distribution\",size=20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "id": "nh6cqwupNXQL",
        "outputId": "893cbab5-00ff-4c8f-b478-4d31b70179f0"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-3e7e58e2572b>:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x='Churn', data=df,palette=color)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,5),dpi=120)\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "color=\"Black\",\"Skyblue\"\n",
        "sns.countplot(x='PaperlessBilling', data=df,palette=color,hue=\"Churn\")\n",
        "plt.title('Paperless Billing Distibution')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "color=\"Royalblue\",\"aquamarine\"\n",
        "sns.countplot(x='PhoneService', data=df,palette=color,hue=\"Churn\")\n",
        "plt.title('Phone Service Distibution')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "kDEq6R-VNZkr",
        "outputId": "b3b49293-2dce-436b-a8c1-476ae6bcc9a1"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x600 with 2 Axes>"
            ],
            "image/png": 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Q//73v1W/fn25ubmpSpUqatCggVkobdy4US+88IIOHDhgt+aDszWvsheHz6l+/fq6ePGiTp06JRcXF6WkpGjJkiVasmSJ07kkJSXZPc7LLYa//fabJDlcBp/T2bNn5efnpzlz5mjw4MEKCQlRy5Yt1a1bNz3yyCOqXbu2ebyJEydq/vz5WrFihTp06KCePXtq0KBBdrdFSlKlSpXUpUsX83H37t3VoEED9evXT6+//rrGjBlz3bkXRPXq1e0eZ4diZ86ckSSz8K1bt67DvnXr1nUaYgIAgNLnWjVatrzWBQ0aNLDr5+7urtq1a5vbs1WrVs2hzvPz89P3339vPv7tt98UFxenypUrO513znrOw8NDU6dO1dSpU5WYmKjt27dr4cKFWrlypcqUKaPly5fr1KlTRVIjNm/eXA0bNtR7772nYcOGSfrrtshKlSqZIZ0z2becZi/l4Ux+6k0ABUcQBuCGCA8PN7+RKKcdO3aoZ8+e6tixo2JiYhQYGKgyZcpo6dKlDguH5kX2p2SDBg3KtYho1qyZ3ePrXQ2Wc9zo6GiFhYU57ZN9ZdQDDzygDh066P3339cnn3yi6OhovfTSS1q7dq257sW8efM0ZMgQrV+/Xp988onGjh2rWbNm6euvv1a1atWuOZe77rpLkvTFF18UWxCW27ccGYZRLMcDAAAlI7caLaeirgvyMl5WVpaaNm2q+fPnO+0bEhLitD0wMFADBgxQ3759FRoaqpUrVyo2NrZIa8T+/fvrxRdf1OnTp1W+fHl98MEHevDBBwv0Dd455afeBFBwBGEAStSaNWvk6empzZs3y8PDw2xfunSp0/7Zn5TldPDgQZUtW9b8xLB8+fLKzMy0u5KqsOrUqSNJ8vHxydO4gYGBGjlypEaOHKmkpCS1aNFCL774ohmESVLTpk3VtGlT/eMf/9BXX32ldu3a6bXXXtMLL7xwzbGvXLkiSUpNTS3EGRVO9sK08fHxDtuctQEAgFtXdl3w66+/mlfAS9Lly5f/X3t3F9Jk/8dx/OOGFZbC1KlZJ6VJ6czCxEIyKfEBqaAnY+SB2IM9UHlWEOEa2ITSiDWKFJwFoaAVO5BKWB0NCqKRgdFBYqyDDEsrC2rd/wNR2m1/8bbuxs3er8Nd17V92dGXz/X7fX96+fLlrHqytLQ0+f1+bdq0aVYnY0dHR2vlypV68eKF3r59K7PZ/Nt6xMrKStlsNnV1dSk5OVmjo6PavXv3tM9M9JJ9fX0/XVH/4z0z7TcBzA4zwgCEldFoVFRUlILB4ORnAwMDkycB/Z3P5wvZdvfq1Svdvn1bJSUlMhqNMhqN2r59u7q6utTX1zfl+Z8d9T0Tubm5SktL07lz534aQE18bzAY1MjISMi1pKQkpaamTm77HB0dnQyzJmRnZ8tgMIRsDf1/PB6PpPGl+eGSmpoqi8Wi9vb2kP/jwYMHevr0adjqAgAAf15xcbHmzJmjixcvhqzqam1t1cjIyJRTwGdi165dCgQCunr16pRrnz9/1qdPnySNvyQdHByccs/79+/l8/lkMplkNpt/a4+4YsUKZWdnq6OjQx0dHVq4cKEKCwunfaakpESxsbE6e/asvnz5EnJt4j+bab8J4NewIgxAWFVUVKipqUllZWWyWq168+aNLl26pPT09JA5ERMsFotKS0t19OhRzZ07Vy6XS5Jks9km73E4HPJ6vcrPz9e+ffuUmZmp4eFhPX78WL29vRoeHv7HdRoMBrW0tKi8vFxZWVmqrq7WokWLFAgE5PV6FRcXJ4/How8fPmjx4sXasWOHcnJytGDBAvX29urRo0c6f/68pPFjsY8cOaKdO3cqIyND375907Vr1yYbtB8FAgFdv35d0vhbVb/frytXrigxMfFf2xY5Uw0NDdq6dasKCgpUXV2td+/eyel0ymKxhHW1GgAA+LPMZrNOnjwpm82msrIybdmyRc+fP5fL5VJeXl7IYPyZqqqqUmdnp2pra+X1elVQUKBgMKj+/n51dnbqzp07WrNmjfx+v6xWq8rLy7V+/XrFx8crEAjI7Xbr9evXunDhwuRWzN/ZI1ZWVur06dOaN2+eampqpsxV+7u4uDg1Nzdr7969ysvLk9Vqlclkkt/v19jYmNxu94z7TQC/hiAMQFht3LhRra2tcjgcOn78uJYsWaLGxkYNDAz8NAjbsGGD1q1bJ5vNpsHBQWVmZqqtrS1kpkNycrIePnyoM2fOqLu7Wy6XSwkJCcrKylJjY+Osay0qKpLP55PdbpfT6dTHjx+VkpKi/Px8HThwQJIUExOjQ4cO6e7du+ru7tb379+Vnp4ul8ulgwcPShpfyVVaWiqPx6NAIKCYmBjl5OSop6dHa9euDfnNJ0+eqKqqStJ4GJeYmKht27bJbreHnOYUDps3b9aNGzdUX1+vEydOaNmyZWpra5Pb7dazZ8/CWhsAAPiz6uvrZTab5XQ6VVdXp/j4eO3fv18NDQ2Kjo7+x99nMBh069YtNTc3q729XTdv3lRMTIyWLl2qY8eOKSMjQ5JUWFgou92unp4eNTU1aWhoSLGxsVq9erUaGxtDXjL+zh6xsrJSp06d0tjY2LSnRf6opqZGSUlJcjgcstvtio6O1vLly1VXVzd5z0z6TQC/JuovJh8D+I+IiorS4cOH5XQ6w10KprFq1SqZzWbdu3cv3KUAAAAAQAhmhAEAZuXr169TZp3dv39ffr9fRUVF4SkKAAAAAKbB1kgAwKwEAgEVFxdrz549Sk1NVX9/vy5fvqyUlBTV1taGuzwAAAAAmIIgDAAwKyaTSbm5uWppadHQ0JDmz5+viooKORwOJSQkhLs8AAAAAJiCGWEAAAAAAACICMwIAwAAAAAAQEQgCAMAAAAAAEBEIAgDAAAAAABARCAIAwAAAAAAQEQgCAMAAAAAAEBEIAgDAAAAAABARCAIAwAAAAAAQEQgCAMAAAAAAEBEIAgDAAAAAABARCAIAwAAAAAAQEQgCAMAAAAAAEBE+B+ej17yB7q74wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Machine Learning**"
      ],
      "metadata": {
        "id": "jVfHokhDak0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Purpose of the ML Model**\n",
        "\n",
        "The Customer Churn Prediction model aims to:\n",
        "\n",
        "**Identify At-Risk Customers:** Use historical data to predict which customers are most likely to churn.\n",
        "\n",
        "**Inform Retention Strategies:** By identifying high-risk customers, businesses can proactively offer discounts, promotions, or better service to encourage loyalty.\n",
        "\n",
        "**Optimize Resources:** Target marketing and retention efforts more effectively, focusing on the customers most likely to leave."
      ],
      "metadata": {
        "id": "vhHavUHSaUS_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preprocessing**"
      ],
      "metadata": {
        "id": "TbvhdxetT-bH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping irrelevant columns\n",
        "df.drop(['customerID'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "lTuK2VxnS7fO"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encoding categorical variables\n",
        "categorical_cols = df.select_dtypes(include='object').columns\n",
        "df[categorical_cols] = df[categorical_cols].apply(lambda col: col.astype('category').cat.codes)"
      ],
      "metadata": {
        "id": "eYtNoZd3UA68"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filling missing values if any\n",
        "df = df.fillna(df.median())"
      ],
      "metadata": {
        "id": "OyaU85FVUCSY"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting data into features (X) and target (y)\n",
        "X = df.drop(['Churn'], axis=1)\n",
        "y = df['Churn']"
      ],
      "metadata": {
        "id": "UyuuLHPxUD6n"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mn = MinMaxScaler()\n",
        "X = mn.fit_transform(X)"
      ],
      "metadata": {
        "id": "LYrw3_MsUF18"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "MinMaxScaler is very useful when the data is unbalanced."
      ],
      "metadata": {
        "id": "4CSUlRPBUTjf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)"
      ],
      "metadata": {
        "id": "RkZeXoI6UTFo"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Addressing Class Imbalance**\n",
        "\n",
        "**Synthetic Sampling:**\n",
        "\n",
        "SMOTE (Synthetic Minority Oversampling Technique): Generate synthetic examples for the minority class.\n",
        "\n",
        "**Class Weighting:**\n",
        "\n",
        "Assign higher weights to the minority class during model training."
      ],
      "metadata": {
        "id": "bFuYf72tdMbp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "smote = SMOTE(sampling_strategy='minority')\n",
        "X_sm, y_sm = smote.fit_resample(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x4GwOJARUsor",
        "outputId": "00acd280-af3d-455c-f72f-e8aeb2268ad3"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:474: FutureWarning: `BaseEstimator._validate_data` is deprecated in 1.6 and will be removed in 1.7. Use `sklearn.utils.validation.validate_data` instead. This function becomes public and is part of the scikit-learn developer API.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/utils/_tags.py:354: FutureWarning: The SMOTE or classes from which it inherits use `_get_tags` and `_more_tags`. Please define the `__sklearn_tags__` method, or inherit from `sklearn.base.BaseEstimator` and/or other appropriate mixins such as `sklearn.base.TransformerMixin`, `sklearn.base.ClassifierMixin`, `sklearn.base.RegressorMixin`, and `sklearn.base.OutlierMixin`. From scikit-learn 1.7, not defining `__sklearn_tags__` will raise an error.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training count:\")\n",
        "print(y_sm.value_counts())\n",
        "print(\"----------------------------------------\")\n",
        "print(\"Testing count:\")\n",
        "print(y_test.value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PYQ_WzYVUuZz",
        "outputId": "d21f1406-69b6-4215-d72d-e283f30b28ac"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training count:\n",
            "Churn\n",
            "0    3622\n",
            "1    3622\n",
            "Name: count, dtype: int64\n",
            "----------------------------------------\n",
            "Testing count:\n",
            "Churn\n",
            "0    1552\n",
            "1     561\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset is divided into 70% for training and 30% for testing"
      ],
      "metadata": {
        "id": "IhK3Iyj5UzcI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Training and Hyperparameter Tuning**"
      ],
      "metadata": {
        "id": "Kaw3AS07VIyH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Logistic Regression** is a linear model for binary classification tasks. It predicts probabilities for the two classes (e.g., churn or no churn) using a logistic function.\n",
        "\n",
        "**Random Forest** is an ensemble learning method that combines multiple decision trees to improve prediction accuracy and control overfitting.\n",
        "\n",
        "**Gradient Boosting** builds an ensemble of trees sequentially, optimizing each new tree to correct errors made by the previous ones.\n",
        "\n",
        "**Support Vector Machine(SVM)** finds a hyperplane in a high-dimensional space that maximizes the margin between classes. With a kernel trick, it can capture nonlinear relationships."
      ],
      "metadata": {
        "id": "-hVSPw7UeGMG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize models\n",
        "models = {\n",
        "    \"Logistic Regression\": LogisticRegression(random_state=42),\n",
        "    \"Random Forest\": RandomForestClassifier(random_state=42),\n",
        "    \"Gradient Boosting\": GradientBoostingClassifier(random_state=42),\n",
        "    \"Support Vector Machine\": SVC(probability=True, random_state=42),\n",
        "}\n"
      ],
      "metadata": {
        "id": "FaR3dIJ2UxUh"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Hyperparameters** are configurations set before training a machine learning model that govern its behavior, such as the complexity of the model or how it learns.\n",
        "\n",
        "Examples include the number of trees in a Random Forest, the learning rate in Gradient Boosting, or the maximum number of iterations in Logistic Regression. Tuning these parameters (e.g., using GridSearchCV) helps optimize the model’s performance and avoid overfitting or underfitting."
      ],
      "metadata": {
        "id": "mAXtLJSves6J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameters for GridSearchCV (selected models only)\n",
        "param_grids = {\n",
        "    \"Logistic Regression\":{\n",
        "        'max_iter': [100,250,500,1000]\n",
        "    },\n",
        "    \"Random Forest\": {\n",
        "        'n_estimators': [50, 100, 150],\n",
        "        'max_depth': [5, 10, 15],\n",
        "        'min_samples_split': [2, 5, 10],\n",
        "    },\n",
        "    \"Gradient Boosting\": {\n",
        "        'n_estimators': [50, 100, 150],\n",
        "        'learning_rate': [0.01, 0.1, 0.2],\n",
        "        'max_depth': [3, 5, 7],\n",
        "    },\n",
        "}"
      ],
      "metadata": {
        "id": "CfuNHri0exw0"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "By training multiple models, we aim to compare their performances (e.g., using Accuracy,Recall, Persion and ROC-AUC) and identify the most suitable one for predicting customer churn. Each model brings unique strengths, allowing us to account for different data patterns and relationships."
      ],
      "metadata": {
        "id": "-p1zJdc3dvxp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_results = {}\n",
        "for name, model in models.items():\n",
        "    print(f\"\\nTraining {name}...\")\n",
        "    # If model has hyperparameters to tune\n",
        "    if name in param_grids:\n",
        "        grid_search = GridSearchCV(model, param_grids[name], scoring='roc_auc', cv=5, verbose=2)\n",
        "        grid_search.fit(X_sm, y_sm)\n",
        "        model = grid_search.best_estimator_\n",
        "        print(f\"Best Parameters for {name}: {grid_search.best_params_}\")\n",
        "    else:\n",
        "        model.fit(X_sm, y_sm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_vKRkAkDVheX",
        "outputId": "518f28aa-3567-4dfe-e914-97e3145257d4"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training Logistic Regression...\n",
            "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
            "[CV] END .......................................max_iter=100; total time=   0.1s\n",
            "[CV] END .......................................max_iter=100; total time=   0.0s\n",
            "[CV] END .......................................max_iter=100; total time=   0.0s\n",
            "[CV] END .......................................max_iter=100; total time=   0.0s\n",
            "[CV] END .......................................max_iter=100; total time=   0.1s\n",
            "[CV] END .......................................max_iter=250; total time=   0.1s\n",
            "[CV] END .......................................max_iter=250; total time=   0.0s\n",
            "[CV] END .......................................max_iter=250; total time=   0.0s\n",
            "[CV] END .......................................max_iter=250; total time=   0.0s\n",
            "[CV] END .......................................max_iter=250; total time=   0.0s\n",
            "[CV] END .......................................max_iter=500; total time=   0.0s\n",
            "[CV] END .......................................max_iter=500; total time=   0.0s\n",
            "[CV] END .......................................max_iter=500; total time=   0.0s\n",
            "[CV] END .......................................max_iter=500; total time=   0.0s\n",
            "[CV] END .......................................max_iter=500; total time=   0.1s\n",
            "[CV] END ......................................max_iter=1000; total time=   0.0s\n",
            "[CV] END ......................................max_iter=1000; total time=   0.0s\n",
            "[CV] END ......................................max_iter=1000; total time=   0.0s\n",
            "[CV] END ......................................max_iter=1000; total time=   0.0s\n",
            "[CV] END ......................................max_iter=1000; total time=   0.0s\n",
            "Best Parameters for Logistic Regression: {'max_iter': 100}\n",
            "\n",
            "Training Random Forest...\n",
            "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n",
            "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=2, n_estimators=50; total time=   0.3s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=   0.6s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=   0.6s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=   0.7s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=   0.8s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=100; total time=   0.8s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=150; total time=   1.2s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=150; total time=   0.9s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=150; total time=   0.8s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=150; total time=   0.8s\n",
            "[CV] END .max_depth=5, min_samples_split=2, n_estimators=150; total time=   0.8s\n",
            "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   0.3s\n",
            "[CV] END ..max_depth=5, min_samples_split=5, n_estimators=50; total time=   0.3s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=   0.6s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=   0.6s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=   0.5s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=   0.5s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=100; total time=   0.6s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=150; total time=   0.8s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=150; total time=   0.8s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=150; total time=   0.8s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=150; total time=   1.0s\n",
            "[CV] END .max_depth=5, min_samples_split=5, n_estimators=150; total time=   1.2s\n",
            "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   0.5s\n",
            "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   0.3s\n",
            "[CV] END .max_depth=5, min_samples_split=10, n_estimators=50; total time=   0.3s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=   0.6s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=   0.6s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=   0.5s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=   0.5s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=100; total time=   0.5s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=150; total time=   0.8s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=150; total time=   0.8s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=150; total time=   0.8s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=150; total time=   0.8s\n",
            "[CV] END max_depth=5, min_samples_split=10, n_estimators=150; total time=   0.8s\n",
            "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=2, n_estimators=50; total time=   0.4s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=   1.1s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.9s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=100; total time=   0.8s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=150; total time=   1.3s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=150; total time=   1.3s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=2, n_estimators=150; total time=   1.2s\n",
            "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=   0.4s\n",
            "[CV] END .max_depth=10, min_samples_split=5, n_estimators=50; total time=   0.4s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=   1.1s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.8s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=100; total time=   0.8s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=150; total time=   1.3s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=5, n_estimators=150; total time=   1.3s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=   0.4s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=   0.4s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=   0.4s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=   0.4s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=50; total time=   0.5s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=100; total time=   0.8s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=150; total time=   1.2s\n",
            "[CV] END max_depth=10, min_samples_split=10, n_estimators=150; total time=   1.2s\n",
            "[CV] END .max_depth=15, min_samples_split=2, n_estimators=50; total time=   0.5s\n",
            "[CV] END .max_depth=15, min_samples_split=2, n_estimators=50; total time=   0.5s\n",
            "[CV] END .max_depth=15, min_samples_split=2, n_estimators=50; total time=   0.5s\n",
            "[CV] END .max_depth=15, min_samples_split=2, n_estimators=50; total time=   0.6s\n",
            "[CV] END .max_depth=15, min_samples_split=2, n_estimators=50; total time=   0.7s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=100; total time=   1.4s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=100; total time=   1.3s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=150; total time=   1.5s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=150; total time=   1.5s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=150; total time=   1.5s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=150; total time=   1.5s\n",
            "[CV] END max_depth=15, min_samples_split=2, n_estimators=150; total time=   1.8s\n",
            "[CV] END .max_depth=15, min_samples_split=5, n_estimators=50; total time=   0.7s\n",
            "[CV] END .max_depth=15, min_samples_split=5, n_estimators=50; total time=   0.8s\n",
            "[CV] END .max_depth=15, min_samples_split=5, n_estimators=50; total time=   0.7s\n",
            "[CV] END .max_depth=15, min_samples_split=5, n_estimators=50; total time=   0.6s\n",
            "[CV] END .max_depth=15, min_samples_split=5, n_estimators=50; total time=   0.5s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=150; total time=   1.5s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=150; total time=   1.5s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=150; total time=   1.6s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=150; total time=   2.1s\n",
            "[CV] END max_depth=15, min_samples_split=5, n_estimators=150; total time=   1.7s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=50; total time=   0.5s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=50; total time=   0.5s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=50; total time=   0.5s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=50; total time=   0.5s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=50; total time=   0.5s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=100; total time=   1.0s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=100; total time=   0.9s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=150; total time=   1.4s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=150; total time=   1.8s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=150; total time=   2.0s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=150; total time=   1.4s\n",
            "[CV] END max_depth=15, min_samples_split=10, n_estimators=150; total time=   1.4s\n",
            "Best Parameters for Random Forest: {'max_depth': 15, 'min_samples_split': 2, 'n_estimators': 150}\n",
            "\n",
            "Training Gradient Boosting...\n",
            "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n",
            "[CV] END ...learning_rate=0.01, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ...learning_rate=0.01, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ...learning_rate=0.01, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ...learning_rate=0.01, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ...learning_rate=0.01, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   1.7s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   2.2s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   1.8s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   1.6s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time=   1.6s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=150; total time=   2.5s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=150; total time=   2.5s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=150; total time=   2.8s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=150; total time=   2.8s\n",
            "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=150; total time=   2.4s\n",
            "[CV] END ...learning_rate=0.01, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ...learning_rate=0.01, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ...learning_rate=0.01, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ...learning_rate=0.01, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ...learning_rate=0.01, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   3.7s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   2.6s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   2.5s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   2.5s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time=   2.6s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=150; total time=   4.9s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=150; total time=   3.9s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=150; total time=   3.8s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=150; total time=   4.7s\n",
            "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=150; total time=   3.8s\n",
            "[CV] END ...learning_rate=0.01, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ...learning_rate=0.01, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ...learning_rate=0.01, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ...learning_rate=0.01, max_depth=7, n_estimators=50; total time=   2.5s\n",
            "[CV] END ...learning_rate=0.01, max_depth=7, n_estimators=50; total time=   2.0s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   3.9s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   3.8s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   4.6s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   3.7s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time=   3.6s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=150; total time=   6.9s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=150; total time=   5.8s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=150; total time=   6.5s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=150; total time=   5.6s\n",
            "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=150; total time=   5.9s\n",
            "[CV] END ....learning_rate=0.1, max_depth=3, n_estimators=50; total time=   1.1s\n",
            "[CV] END ....learning_rate=0.1, max_depth=3, n_estimators=50; total time=   1.1s\n",
            "[CV] END ....learning_rate=0.1, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ....learning_rate=0.1, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ....learning_rate=0.1, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   1.7s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   1.6s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   1.6s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   1.6s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time=   1.8s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=150; total time=   3.3s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=150; total time=   2.5s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=150; total time=   2.3s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=150; total time=   2.4s\n",
            "[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=150; total time=   2.4s\n",
            "[CV] END ....learning_rate=0.1, max_depth=5, n_estimators=50; total time=   1.8s\n",
            "[CV] END ....learning_rate=0.1, max_depth=5, n_estimators=50; total time=   1.8s\n",
            "[CV] END ....learning_rate=0.1, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ....learning_rate=0.1, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ....learning_rate=0.1, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   2.7s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   2.7s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   3.2s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   2.8s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time=   2.5s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=150; total time=   4.0s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=150; total time=   4.8s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=150; total time=   3.9s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=150; total time=   3.8s\n",
            "[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=150; total time=   4.2s\n",
            "[CV] END ....learning_rate=0.1, max_depth=7, n_estimators=50; total time=   2.5s\n",
            "[CV] END ....learning_rate=0.1, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ....learning_rate=0.1, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ....learning_rate=0.1, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ....learning_rate=0.1, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   4.7s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   4.0s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   3.7s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   4.0s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time=   4.3s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=150; total time=   5.9s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=150; total time=   6.7s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=150; total time=   5.5s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=150; total time=   6.3s\n",
            "[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=150; total time=   5.6s\n",
            "[CV] END ....learning_rate=0.2, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ....learning_rate=0.2, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ....learning_rate=0.2, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ....learning_rate=0.2, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ....learning_rate=0.2, max_depth=3, n_estimators=50; total time=   0.8s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   2.0s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   2.3s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   1.6s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   1.5s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time=   1.6s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=150; total time=   2.5s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=150; total time=   2.4s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=150; total time=   3.1s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=150; total time=   2.5s\n",
            "[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=150; total time=   2.4s\n",
            "[CV] END ....learning_rate=0.2, max_depth=5, n_estimators=50; total time=   1.4s\n",
            "[CV] END ....learning_rate=0.2, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ....learning_rate=0.2, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ....learning_rate=0.2, max_depth=5, n_estimators=50; total time=   1.3s\n",
            "[CV] END ....learning_rate=0.2, max_depth=5, n_estimators=50; total time=   1.7s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   3.2s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   2.7s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   2.6s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   2.5s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time=   3.2s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=150; total time=   4.3s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=150; total time=   3.9s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=150; total time=   4.1s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=150; total time=   4.4s\n",
            "[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=150; total time=   3.8s\n",
            "[CV] END ....learning_rate=0.2, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ....learning_rate=0.2, max_depth=7, n_estimators=50; total time=   1.9s\n",
            "[CV] END ....learning_rate=0.2, max_depth=7, n_estimators=50; total time=   2.5s\n",
            "[CV] END ....learning_rate=0.2, max_depth=7, n_estimators=50; total time=   2.2s\n",
            "[CV] END ....learning_rate=0.2, max_depth=7, n_estimators=50; total time=   1.8s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   3.9s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   4.0s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   4.5s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   3.7s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time=   3.6s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=150; total time=   6.8s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=150; total time=   5.7s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=150; total time=   6.4s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=150; total time=   5.5s\n",
            "[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=150; total time=   6.1s\n",
            "Best Parameters for Gradient Boosting: {'learning_rate': 0.1, 'max_depth': 7, 'n_estimators': 150}\n",
            "\n",
            "Training Support Vector Machine...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Evaluation**"
      ],
      "metadata": {
        "id": "BUDJzXllVj6U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Evaluation Metrics:**\n",
        "\n",
        "**Accuracy:** Measures the overall correctness of the model by dividing the number of correct predictions by the total predictions.\n",
        "\n",
        "**ROC-AUC:** Evaluates the ability of the model to distinguish between classes, based on the predicted probabilities.\n",
        "\n",
        "**Recall:** Focuses on the model's ability to correctly identify positive samples.\n",
        "\n",
        "**Classification Report:** Provides a detailed summary of precision, recall, F1-score, and support for each class."
      ],
      "metadata": {
        "id": "c3Y5-ygme-gD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for name, model in models.items():\n",
        "    print(f\"\\nEvaluation of {name}...\")\n",
        "    model.fit(X_sm, y_sm)\n",
        "    # Make predictions\n",
        "    y_pred = model.predict(X_test)\n",
        "    y_proba = model.predict_proba(X_test)[:, 1]\n",
        "    # Evaluate performance\n",
        "    accuracy=accuracy_score(y_test, y_pred)\n",
        "    roc_auc = roc_auc_score(y_test, y_proba)\n",
        "    recall=recall_score(y_test,y_pred)\n",
        "    print(f\"Accuracy:{accuracy}\")\n",
        "    print(f\"ROC-AUC Score: {roc_auc}\")\n",
        "    print(f\"Recall Score: {recall}\")\n",
        "    print(f\"Classification Report for {name}:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "    print(\"-------------------------------------------------------------------------------\")\n",
        "    # Store results for comparison\n",
        "    model_results[name] = {\n",
        "        \"model\": model,\n",
        "        \"Accuracy\": accuracy,\n",
        "        \"roc_auc\": roc_auc,\n",
        "        \"recall\": recall,\n",
        "        \"classification_report\": classification_report(y_test, y_pred, output_dict=True),\n",
        "    }"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jigFKn-YVkM8",
        "outputId": "287a5b1b-ee69-4498-b5c7-8f3689104dcf"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Evaluation of Logistic Regression...\n",
            "Accuracy:0.7368670137245622\n",
            "ROC-AUC Score: 0.8400896089457338\n",
            "Recall Score: 0.7914438502673797\n",
            "Classification Report for Logistic Regression:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.72      0.80      1552\n",
            "           1       0.50      0.79      0.61       561\n",
            "\n",
            "    accuracy                           0.74      2113\n",
            "   macro avg       0.70      0.75      0.71      2113\n",
            "weighted avg       0.80      0.74      0.75      2113\n",
            "\n",
            "-------------------------------------------------------------------------------\n",
            "\n",
            "Evaluation of Random Forest...\n",
            "Accuracy:0.7870326549929011\n",
            "ROC-AUC Score: 0.8189846463421357\n",
            "Recall Score: 0.5989304812834224\n",
            "Classification Report for Random Forest:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.86      0.86      1552\n",
            "           1       0.60      0.60      0.60       561\n",
            "\n",
            "    accuracy                           0.79      2113\n",
            "   macro avg       0.73      0.73      0.73      2113\n",
            "weighted avg       0.79      0.79      0.79      2113\n",
            "\n",
            "-------------------------------------------------------------------------------\n",
            "\n",
            "Evaluation of Gradient Boosting...\n",
            "Accuracy:0.7775674396592522\n",
            "ROC-AUC Score: 0.8396416790708785\n",
            "Recall Score: 0.7005347593582888\n",
            "Classification Report for Gradient Boosting:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.81      0.84      1552\n",
            "           1       0.57      0.70      0.63       561\n",
            "\n",
            "    accuracy                           0.78      2113\n",
            "   macro avg       0.72      0.75      0.73      2113\n",
            "weighted avg       0.80      0.78      0.78      2113\n",
            "\n",
            "-------------------------------------------------------------------------------\n",
            "\n",
            "Evaluation of Support Vector Machine...\n",
            "Accuracy:0.7491717936583058\n",
            "ROC-AUC Score: 0.8161690050903211\n",
            "Recall Score: 0.7094474153297683\n",
            "Classification Report for Support Vector Machine:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.76      0.82      1552\n",
            "           1       0.52      0.71      0.60       561\n",
            "\n",
            "    accuracy                           0.75      2113\n",
            "   macro avg       0.70      0.74      0.71      2113\n",
            "weighted avg       0.78      0.75      0.76      2113\n",
            "\n",
            "-------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare models\n",
        "comparison_df = pd.DataFrame({\n",
        "    \"Model\": list(model_results.keys()),\n",
        "    \"Accuracy Score\": [res[\"Accuracy\"] for res in model_results.values()],\n",
        "})\n",
        "comparison_df = comparison_df.sort_values(by=\"Accuracy Score\", ascending=False)\n",
        "\n",
        "# Print model comparison\n",
        "print(\"\\nModel Comparison:\")\n",
        "print(comparison_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiQuOUiPVnyf",
        "outputId": "34c67484-46c3-431c-de88-0fd838161f85"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model Comparison:\n",
            "                    Model  Accuracy Score\n",
            "1           Random Forest        0.787033\n",
            "2       Gradient Boosting        0.777567\n",
            "3  Support Vector Machine        0.749172\n",
            "0     Logistic Regression        0.736867\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Here's an interpretation of the results:**\n",
        "\n",
        "**Gradient Boosting** performs the best with an accuracy score of 0.777, making it the most accurate model for this dataset. It effectively balances bias and variance through boosting.\n",
        "\n",
        "**Random Forest** closely follows with an accuracy score of 0.787. Its ensemble approach, combining multiple decision trees, offers robust performance.\n",
        "\n",
        "**Support Vector Machine (SVM)** achieves a slightly lower accuracy of 0.749, showing its ability to classify well but falling short compared to ensemble methods.\n",
        "\n",
        "**Logistic Regression** has the lowest accuracy at 0.736, as it is a simpler linear model that might struggle to capture complex patterns in the data."
      ],
      "metadata": {
        "id": "wbcTnguJfq-G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize Accuracy Scores\n",
        "plt.figure(figsize=(12,8),dpi=120)\n",
        "sns.barplot(x=\"Accuracy Score\", y=\"Model\", data=comparison_df, palette=\"cool\")\n",
        "plt.title(\"Model Accuracy Comparison\",fontsize=20)\n",
        "plt.xlabel(\"Accuracy\",color='green')\n",
        "plt.ylabel(\"Classification Model\",color='green')\n",
        "plt.xlim(0.5,1)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "6VzREGnxVpPL",
        "outputId": "5c5dd7f4-a2e2-44f6-a481-56782fd33e29"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-3a08ff4a05e0>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=\"Accuracy Score\", y=\"Model\", data=comparison_df, palette=\"cool\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x960 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Insights:**\n",
        "\n",
        "Ensemble models (Gradient Boosting and Random Forest) outperform simpler models, likely due to their ability to model complex relationships."
      ],
      "metadata": {
        "id": "FFsOAlMrflzj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot ROC Curves\n",
        "plt.figure(figsize=(12, 8),dpi=120)\n",
        "for name, res in model_results.items():\n",
        "    RocCurveDisplay.from_estimator(res[\"model\"], X_test, y_test, name=name, ax=plt.gca())\n",
        "plt.title(\"ROC Curves for All Models\",fontsize=20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        },
        "id": "mZuAQs5oVq6N",
        "outputId": "f9329a24-2f80-4f14-a5a3-dd602343c7a6"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x960 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Deep Learning**"
      ],
      "metadata": {
        "id": "FJPyGGBxeI46"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Model Architecture**\n",
        "\n",
        "**Input Layer:**\n",
        "\n",
        "The first layer has 64 neurons with ReLU activation, and the input size matches the number of features in X_train.\n",
        "\n",
        "**Hidden Layers:**\n",
        "\n",
        "A sequence of dense layers:\n",
        "\n",
        "*Second layer:* 32 neurons with ReLU activation.\n",
        "\n",
        "*Third layer:* 32 neurons with ReLU activation.\n",
        "\n",
        "Dropout layers are used after each hidden layer (with rates 0.3 and 0.2) to reduce overfitting by randomly deactivating some neurons during training.\n",
        "\n",
        "**Output Layer:**\n",
        "\n",
        "A single neuron with a sigmoid activation function, which outputs a probability value for binary classification."
      ],
      "metadata": {
        "id": "sjweckRogJYb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n",
        "\n",
        "model = Sequential([\n",
        "    Dense(64, activation='relu', input_dim=X_train.shape[1]),\n",
        "    Dropout(0.3),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dropout(0.2),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.fit(X_sm, y_sm, epochs=40, batch_size=8, validation_split=0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwOONUzaWneE",
        "outputId": "aa264fbf-1a8a-4e94-966f-609bb1f46bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.7000 - loss: 0.5676 - val_accuracy: 0.7453 - val_loss: 0.5799\n",
            "Epoch 2/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7614 - loss: 0.4931 - val_accuracy: 0.6556 - val_loss: 0.7230\n",
            "Epoch 3/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7848 - loss: 0.4682 - val_accuracy: 0.6763 - val_loss: 0.6578\n",
            "Epoch 4/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7906 - loss: 0.4474 - val_accuracy: 0.6605 - val_loss: 0.6651\n",
            "Epoch 5/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.7876 - loss: 0.4583 - val_accuracy: 0.7150 - val_loss: 0.5617\n",
            "Epoch 6/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7874 - loss: 0.4528 - val_accuracy: 0.7005 - val_loss: 0.5628\n",
            "Epoch 7/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7806 - loss: 0.4572 - val_accuracy: 0.6839 - val_loss: 0.6154\n",
            "Epoch 8/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7924 - loss: 0.4477 - val_accuracy: 0.6239 - val_loss: 0.6575\n",
            "Epoch 9/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7793 - loss: 0.4544 - val_accuracy: 0.7308 - val_loss: 0.5506\n",
            "Epoch 10/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8025 - loss: 0.4321 - val_accuracy: 0.8012 - val_loss: 0.4747\n",
            "Epoch 11/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.7939 - loss: 0.4430 - val_accuracy: 0.6998 - val_loss: 0.5987\n",
            "Epoch 12/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8011 - loss: 0.4288 - val_accuracy: 0.7315 - val_loss: 0.5508\n",
            "Epoch 13/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7910 - loss: 0.4417 - val_accuracy: 0.7529 - val_loss: 0.4955\n",
            "Epoch 14/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.7928 - loss: 0.4309 - val_accuracy: 0.7516 - val_loss: 0.5349\n",
            "Epoch 15/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8046 - loss: 0.4289 - val_accuracy: 0.7640 - val_loss: 0.5127\n",
            "Epoch 16/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.8070 - loss: 0.4278 - val_accuracy: 0.7440 - val_loss: 0.5162\n",
            "Epoch 17/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8046 - loss: 0.4180 - val_accuracy: 0.7798 - val_loss: 0.4885\n",
            "Epoch 18/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.7945 - loss: 0.4321 - val_accuracy: 0.7798 - val_loss: 0.4933\n",
            "Epoch 19/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8084 - loss: 0.4209 - val_accuracy: 0.7861 - val_loss: 0.4624\n",
            "Epoch 20/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8080 - loss: 0.4228 - val_accuracy: 0.7467 - val_loss: 0.5088\n",
            "Epoch 21/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8194 - loss: 0.4036 - val_accuracy: 0.7826 - val_loss: 0.5030\n",
            "Epoch 22/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8009 - loss: 0.4196 - val_accuracy: 0.6839 - val_loss: 0.5912\n",
            "Epoch 23/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8119 - loss: 0.4057 - val_accuracy: 0.7667 - val_loss: 0.5017\n",
            "Epoch 24/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8089 - loss: 0.4121 - val_accuracy: 0.7391 - val_loss: 0.5772\n",
            "Epoch 25/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8144 - loss: 0.4065 - val_accuracy: 0.7909 - val_loss: 0.4813\n",
            "Epoch 26/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8092 - loss: 0.4211 - val_accuracy: 0.7792 - val_loss: 0.4800\n",
            "Epoch 27/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8158 - loss: 0.4163 - val_accuracy: 0.7695 - val_loss: 0.5209\n",
            "Epoch 28/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8188 - loss: 0.4099 - val_accuracy: 0.7743 - val_loss: 0.5103\n",
            "Epoch 29/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8105 - loss: 0.4121 - val_accuracy: 0.7709 - val_loss: 0.5249\n",
            "Epoch 30/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8176 - loss: 0.3911 - val_accuracy: 0.7557 - val_loss: 0.5385\n",
            "Epoch 31/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8256 - loss: 0.3985 - val_accuracy: 0.8033 - val_loss: 0.5018\n",
            "Epoch 32/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8301 - loss: 0.3817 - val_accuracy: 0.7764 - val_loss: 0.4946\n",
            "Epoch 33/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8202 - loss: 0.4059 - val_accuracy: 0.7847 - val_loss: 0.4761\n",
            "Epoch 34/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8223 - loss: 0.3966 - val_accuracy: 0.8068 - val_loss: 0.4919\n",
            "Epoch 35/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8207 - loss: 0.3979 - val_accuracy: 0.8033 - val_loss: 0.4519\n",
            "Epoch 36/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.8132 - loss: 0.4070 - val_accuracy: 0.8033 - val_loss: 0.4506\n",
            "Epoch 37/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8311 - loss: 0.3879 - val_accuracy: 0.7591 - val_loss: 0.5254\n",
            "Epoch 38/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8211 - loss: 0.3930 - val_accuracy: 0.8171 - val_loss: 0.4412\n",
            "Epoch 39/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8203 - loss: 0.3879 - val_accuracy: 0.7612 - val_loss: 0.5083\n",
            "Epoch 40/40\n",
            "\u001b[1m725/725\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8317 - loss: 0.3918 - val_accuracy: 0.8061 - val_loss: 0.4696\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7a57ebc72710>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report\n",
        "\n",
        "# Predict probabilities and convert to binary predictions\n",
        "y_proba = model.predict(X_test)  # Outputs probabilities\n",
        "y_pred = (y_proba > 0.5).astype(int)  # Convert probabilities to binary predictions (0 or 1)\n",
        "\n",
        "# Accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "# Precision and Recall\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "print(f\"Precision: {precision:.2f}\")\n",
        "print(f\"Recall: {recall:.2f}\")\n",
        "\n",
        "# Full Classification Report\n",
        "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMs9hq3IWrWj",
        "outputId": "a70d464f-c53d-4805-ddd7-2b1b16c40123"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m67/67\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "Accuracy: 0.76\n",
            "Precision: 0.54\n",
            "Recall: 0.63\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.81      0.83      1552\n",
            "           1       0.54      0.63      0.58       561\n",
            "\n",
            "    accuracy                           0.76      2113\n",
            "   macro avg       0.70      0.72      0.71      2113\n",
            "weighted avg       0.77      0.76      0.77      2113\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The model achieved an accuracy of 76%, meaning it correctly predicted 76% of the total instances. While this is reasonable, it may not fully reflect performance on imbalanced datasets.\n",
        "\n",
        "The model performs well for the majority class but struggles with the minority class, likely due to class imbalance.\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "NJ5alA--g4TR"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AsckGxsYXBmI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}