2_8_4_Synthetic_Credit_Risk_Scoring.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 2px solid purple;\">\n",
        "<hr style=\"border: 2px solid purple;\">"
      ],
      "metadata": {
        "id": "GmKYqqJvK8PD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Project Overview\n",
        "\n",
        "This project focuses on building a machine learning pipeline for fraud detection using the Credit Card Fraud Detection dataset.\n",
        "The steps involved include data preprocessing, visualization, outlier detection, feature scaling, and the generation of synthetic data using a Generative Adversarial Network (GAN).\n",
        "\n",
        "1. **Data Loading and Inspection**:\n",
        "   The dataset is downloaded from a public source, and initial exploratory data analysis (EDA) is conducted by inspecting the first few rows, data types, and summary statistics.\n",
        "\n",
        "2. **Data Cleaning**:\n",
        "   Missing and duplicate values are identified and handled by removing duplicates. The data is then cleaned for outliers using the Interquartile Range (IQR) method to enhance model robustness.\n",
        "\n",
        "3. **Visualization**:\n",
        "   Various visualizations like histograms, correlation heatmaps, and scatter plots are created to understand feature distributions and relationships.\n",
        "\n",
        "4. **Feature Scaling**:\n",
        "   The features are scaled using StandardScaler to normalize the data, ensuring optimal model performance.\n",
        "\n",
        "5. **Generative Adversarial Network (GAN)**:\n",
        "   A GAN is defined with a generator and discriminator. The generator creates synthetic data resembling real data, while the discriminator evaluates the authenticity of the generated data. The GAN is trained over 5000 epochs.\n",
        "\n",
        "6. **Synthetic Data Generation**:\n",
        "   The trained generator is used to create synthetic samples, which are compared to real data distributions using kernel density estimation (KDE) plots for a visual evaluation of the similarity.\n",
        "\n",
        "7. **Model Training and Evaluation**:\n",
        "   A RandomForestClassifier is trained on the combined real and synthetic data. The classifier's performance is evaluated on a test set, and accuracy and classification metrics are reported.\n",
        "\n",
        "The overall goal of this project is to generate synthetic data for fraud detection, improve model generalization by augmenting the dataset, and evaluate the model's performance on detecting fraudulent transactions."
      ],
      "metadata": {
        "id": "-Ei1nGwKL9S4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "CywvRnvoOXiy"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uU_2ocOkOaSS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lZ8P3TfiOWwc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # To suppress warnings"
      ],
      "metadata": {
        "id": "MIV023hhDvUO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Ignore all warnings"
      ],
      "metadata": {
        "id": "XOxk-b0VETUU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m8XelcYMETW_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dLWU-InIETZI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "BYbdfR5RO3vP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "i4ADdsXfETbm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "fqyEklwKETd0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "EgO62vlAEThI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "0lweo-AfETjo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "Ut_Vxt16ETmM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from urllib.request import urlretrieve"
      ],
      "metadata": {
        "id": "b97Y2GIiETof"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "uwbTBwg1ETq_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn"
      ],
      "metadata": {
        "id": "oPyEWM_nETtP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.optim as optim"
      ],
      "metadata": {
        "id": "hDPsV9rcETvd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "8c3mFgyEDvWk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "NvbsiZOhDvaS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report"
      ],
      "metadata": {
        "id": "TW1ZxwewDvcm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "rXuzdYERO0AK"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9-UpPDkRGCsz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SyzMGh8lGCvy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_fO8gPGKGJH5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "2CS_h7pWGJTa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Load the Dataset\n",
        "- Download and load the dataset directly from the web.\n",
        "- Inspect the first few rows and general data structure."
      ],
      "metadata": {
        "id": "Ha6kiLPwOmOa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the dataset"
      ],
      "metadata": {
        "id": "bOgivYwwGC2H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv\"\n"
      ],
      "metadata": {
        "id": "dgHiUH8DGC47"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = \"/content/creditcard.csv\""
      ],
      "metadata": {
        "id": "Go7bIH4rGC8J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "urlretrieve(url, file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vlflNNaGDAz",
        "outputId": "081bdef9-78d8-4900-fb9c-e36c28e2fc83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/creditcard.csv', <http.client.HTTPMessage at 0x7a759c5c05e0>)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Dataset downloaded and saved to {file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WifyLixpGDMK",
        "outputId": "b211bee5-6ada-4555-f14a-fde1e6dbdd26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded and saved to /content/creditcard.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0ULgmztfGdAT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5d94tgVbGdEQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset"
      ],
      "metadata": {
        "id": "xbcsQBoiGdHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(file_path)"
      ],
      "metadata": {
        "id": "oFX6oZ5TGdLw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5GNnXhnkGlU-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print initial details"
      ],
      "metadata": {
        "id": "C_k_yA8AGlX7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Head:\")\n",
        "print(data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VV__2J1DGlb6",
        "outputId": "a747a510-5769-449b-fd00-09974991ba63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Head:\n",
            "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
            "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
            "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
            "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
            "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
            "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
            "\n",
            "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
            "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
            "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
            "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
            "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
            "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
            "\n",
            "        V26       V27       V28  Amount  Class  \n",
            "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
            "1  0.125895 -0.008983  0.014724    2.69      0  \n",
            "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
            "3 -0.221929  0.062723  0.061458  123.50      0  \n",
            "4  0.502292  0.219422  0.215153   69.99      0  \n",
            "\n",
            "[5 rows x 31 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KS3AcgocGrJe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mErkq87DGrND"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Info:\")\n",
        "print(data.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3412iRwgGrQd",
        "outputId": "2f3c4b93-40b4-48f2-82c8-163bd364fee7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 284807 entries, 0 to 284806\n",
            "Data columns (total 31 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   Time    284807 non-null  float64\n",
            " 1   V1      284807 non-null  float64\n",
            " 2   V2      284807 non-null  float64\n",
            " 3   V3      284807 non-null  float64\n",
            " 4   V4      284807 non-null  float64\n",
            " 5   V5      284807 non-null  float64\n",
            " 6   V6      284807 non-null  float64\n",
            " 7   V7      284807 non-null  float64\n",
            " 8   V8      284807 non-null  float64\n",
            " 9   V9      284807 non-null  float64\n",
            " 10  V10     284807 non-null  float64\n",
            " 11  V11     284807 non-null  float64\n",
            " 12  V12     284807 non-null  float64\n",
            " 13  V13     284807 non-null  float64\n",
            " 14  V14     284807 non-null  float64\n",
            " 15  V15     284807 non-null  float64\n",
            " 16  V16     284807 non-null  float64\n",
            " 17  V17     284807 non-null  float64\n",
            " 18  V18     284807 non-null  float64\n",
            " 19  V19     284807 non-null  float64\n",
            " 20  V20     284807 non-null  float64\n",
            " 21  V21     284807 non-null  float64\n",
            " 22  V22     284807 non-null  float64\n",
            " 23  V23     284807 non-null  float64\n",
            " 24  V24     284807 non-null  float64\n",
            " 25  V25     284807 non-null  float64\n",
            " 26  V26     284807 non-null  float64\n",
            " 27  V27     284807 non-null  float64\n",
            " 28  V28     284807 non-null  float64\n",
            " 29  Amount  284807 non-null  float64\n",
            " 30  Class   284807 non-null  int64  \n",
            "dtypes: float64(30), int64(1)\n",
            "memory usage: 67.4 MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NuSylSAKGleu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "18CP0udQGlhZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Description:\")\n",
        "print(data.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BTpp4z1Glke",
        "outputId": "a7678f39-ab42-497f-d387-0dd82ad85909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Description:\n",
            "                Time            V1            V2            V3            V4  \\\n",
            "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
            "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
            "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
            "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
            "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
            "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
            "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
            "\n",
            "                 V5            V6            V7            V8            V9  \\\n",
            "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
            "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
            "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
            "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
            "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
            "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
            "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
            "\n",
            "       ...           V21           V22           V23           V24  \\\n",
            "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
            "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
            "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
            "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
            "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
            "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
            "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
            "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
            "\n",
            "                V25           V26           V27           V28         Amount  \\\n",
            "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
            "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
            "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
            "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
            "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
            "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
            "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
            "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
            "\n",
            "               Class  \n",
            "count  284807.000000  \n",
            "mean        0.001727  \n",
            "std         0.041527  \n",
            "min         0.000000  \n",
            "25%         0.000000  \n",
            "50%         0.000000  \n",
            "75%         0.000000  \n",
            "max         1.000000  \n",
            "\n",
            "[8 rows x 31 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "CdMU1g7DGytU"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KEXui6WYGzok"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dtx8mpHJGzsE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I3QvKajbGzvG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "jaDj6s8xG0HL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Check for Missing and Duplicate Values\n",
        "- Check for missing values and duplicate rows.\n",
        "- Remove duplicates to ensure data is clean."
      ],
      "metadata": {
        "id": "dvY8FiP4O-jC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for Missing and Duplicate Values"
      ],
      "metadata": {
        "id": "RCsTSVYcG1CM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing Values:\")\n",
        "print(data.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HMwGRo4SG1GA",
        "outputId": "540c76b2-1e57-4f21-a8e2-5e2a6ead30a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing Values:\n",
            "Time      0\n",
            "V1        0\n",
            "V2        0\n",
            "V3        0\n",
            "V4        0\n",
            "V5        0\n",
            "V6        0\n",
            "V7        0\n",
            "V8        0\n",
            "V9        0\n",
            "V10       0\n",
            "V11       0\n",
            "V12       0\n",
            "V13       0\n",
            "V14       0\n",
            "V15       0\n",
            "V16       0\n",
            "V17       0\n",
            "V18       0\n",
            "V19       0\n",
            "V20       0\n",
            "V21       0\n",
            "V22       0\n",
            "V23       0\n",
            "V24       0\n",
            "V25       0\n",
            "V26       0\n",
            "V27       0\n",
            "V28       0\n",
            "Amount    0\n",
            "Class     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qNfyGjP3G1I5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y-H95Gu_G1Lz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDuplicate Rows:\")\n",
        "print(data.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h5YZ0P4nG1PH",
        "outputId": "a7effa25-4378-4eb4-ab97-0b11e445874c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Duplicate Rows:\n",
            "1081\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZXPWU8XPG1SH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HQNaJd_8Gxj0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicates"
      ],
      "metadata": {
        "id": "jL8-HWthGxnU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop_duplicates()"
      ],
      "metadata": {
        "id": "JRkQOBuDGxsE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "NurubdjMHQsd"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UuVg0OwvHRjW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cyz3u64VHRmz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iN5MTqh_HRp2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "NFT0pQMDHSa-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Visualizations\n",
        "- Use histograms to explore feature distributions.\n",
        "- Create a heatmap to check feature correlations.\n",
        "- Visualize relationships between key features with scatter plots."
      ],
      "metadata": {
        "id": "ey8LLstHPJbK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw histograms"
      ],
      "metadata": {
        "id": "JLx7bHYTHTU5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.hist(bins=50, figsize=(20, 15))\n",
        "plt.suptitle(\"Feature Histograms\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "wkuQa0LtHTYd",
        "outputId": "02075b69-9a38-467c-b78a-f97d9e12da0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 36 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cpuf3lCgHTbl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iftlW3I_HT01"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation heatmap"
      ],
      "metadata": {
        "id": "BkvwGc2mHT4R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(data.corr(), annot=False, cmap='coolwarm')\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "sjy6L-hTHRuV",
        "outputId": "f965e893-0a88-487e-dff2-52ebe7e2f781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pNj0a9zEHRzu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "224xQQEOHR21"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot for two key features (e.g., V1 vs V2)"
      ],
      "metadata": {
        "id": "LudvnfgvHR56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.scatterplot(x=data['V1'], y=data['V2'], hue=data['Class'], alpha=0.6, palette=\"coolwarm\")\n",
        "plt.title(\"Scatter Plot of V1 vs V2\")\n",
        "plt.xlabel(\"V1\")\n",
        "plt.ylabel(\"V2\")\n",
        "plt.legend(title=\"Class\", loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "NBPfy8r4GxvD",
        "outputId": "faac3101-71d7-40f8-9bec-6138be799000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "d-rVuJ8JHvT4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Az3sMjMwHwJf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oqniVl8GHwNM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o5zftn6iHwZ_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "jkhKT0JXHwlo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Outlier Detection Using IQR\n",
        "- Detect and remove outliers using the Interquartile Range (IQR) method.\n",
        "- Clean the data to make sure only relevant data points are kept."
      ],
      "metadata": {
        "id": "AY3MywNBPVl2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_outliers_iqr(df, columns):\n",
        "    for column in columns:\n",
        "        Q1 = df[column].quantile(0.25)\n",
        "        Q3 = df[column].quantile(0.75)\n",
        "        IQR = Q3 - Q1\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)].index\n",
        "        df.drop(outliers, inplace=True)\n",
        "    return df"
      ],
      "metadata": {
        "id": "MAE59B4XGxxv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zlCT4IQVGdPs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Detect and remove outliers"
      ],
      "metadata": {
        "id": "qT-bnqx_GDRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_columns = data.columns[:-1]  # Exclude 'Class' (target column)"
      ],
      "metadata": {
        "id": "1Zmk8fSuDvfF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = detect_outliers_iqr(data, numeric_columns)"
      ],
      "metadata": {
        "id": "nAgppqi9DvhT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "3h6kAvYKIEEv"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y2r5dKfzIE-v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KZ-iDJC3IFC5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OBpvUOwVIFG5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "W-izZP87IFeM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Scaling the Features\n",
        "- Scale the features to bring them to a common range.\n",
        "- Use StandardScaler to normalize the data."
      ],
      "metadata": {
        "id": "-T4YSpvUPgRW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "gvHxGEhNDvl7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_features = scaler.fit_transform(data.iloc[:, :-1])  # Exclude the 'Class' column\n"
      ],
      "metadata": {
        "id": "tBKLsujIDvoT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data = pd.DataFrame(scaled_features, columns=data.columns[:-1])"
      ],
      "metadata": {
        "id": "c8DykINnDvq7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data['Class'] = data['Class'].values  # Add back the target column"
      ],
      "metadata": {
        "id": "7auV3fPPDZYU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pyvE5a-kDZdx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the cleaned and scaled dataset"
      ],
      "metadata": {
        "id": "GPNWnWsWDZjP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processed_file_path = \"/content/scaled_credit_risk_data.csv\""
      ],
      "metadata": {
        "id": "7hzlIC5jDZlx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_data.to_csv(processed_file_path, index=False)"
      ],
      "metadata": {
        "id": "gKAehiZNCjex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"\\nProcessed and scaled dataset saved to {processed_file_path}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJHm1J25C_MI",
        "outputId": "8f7330e5-6b13-4f70-f219-3890b2149d9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Processed and scaled dataset saved to /content/scaled_credit_risk_data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "KgUZPMN7IgVP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uzLvDlNkIhQ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "90tYD94NIhVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p95N2V4AIhZI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "RNh3KdRkIhr_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 6: Define GAN Architecture\n",
        "- Define the generator and discriminator models.\n",
        "- The generator creates synthetic data, while the discriminator distinguishes between real and fake data."
      ],
      "metadata": {
        "id": "IQVvvCJBPp4J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self, input_dim, output_dim):\n",
        "        super(Generator, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(input_dim, 128),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(128, 256),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(256, output_dim),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        return self.model(x)"
      ],
      "metadata": {
        "id": "kxCKmlH8Iilk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qS32Kz3DC_PE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Discriminator(nn.Module):\n",
        "    def __init__(self, input_dim):\n",
        "        super(Discriminator, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(input_dim, 256),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(256, 128),\n",
        "            nn.LeakyReLU(0.2),\n",
        "            nn.Linear(128, 1),\n",
        "            nn.Sigmoid()\n",
        "        )\n",
        "    def forward(self, x):\n",
        "        return self.model(x)"
      ],
      "metadata": {
        "id": "mHdtwRDLA8NU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w1gl0YoDIyqt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jev0TnP7IyuQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize GAN components"
      ],
      "metadata": {
        "id": "F0o7MJJ0IyzM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_dim = 10  # Noise vector dimension"
      ],
      "metadata": {
        "id": "VQ6JO7c1Iy4I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_dim = data.shape[1] - 1  # Number of features (excluding 'Class')"
      ],
      "metadata": {
        "id": "G5AKwAwzIy71"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = Generator(input_dim, output_dim)"
      ],
      "metadata": {
        "id": "tVYwjasYIzAC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "discriminator = Discriminator(output_dim)"
      ],
      "metadata": {
        "id": "ORljFE-QI9Iz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = 0.0002"
      ],
      "metadata": {
        "id": "CXykNP6lI9Mt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gen_optimizer = optim.Adam(generator.parameters(), lr=lr)"
      ],
      "metadata": {
        "id": "YzLRsWI-I9Qw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "disc_optimizer = optim.Adam(discriminator.parameters(), lr=lr)"
      ],
      "metadata": {
        "id": "p3Pg5gnKI9Ud"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.BCELoss()"
      ],
      "metadata": {
        "id": "Sz1VqUahI9YL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "6-sj_AOqJMHQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B425pfUzJM5p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "100K5ZOiJM-Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UhDqxUYkJNC4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "sC9tVyb-JNeg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 7: Train GAN\n",
        "- Train the generator and discriminator in a loop.\n",
        "- The generator tries to fool the discriminator with fake data, and the discriminator learns to distinguish real from fake.\n",
        "- Train through multiple epochs, adjusting weights each time."
      ],
      "metadata": {
        "id": "NoVLtuoNP07E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 5000"
      ],
      "metadata": {
        "id": "tvtwh-AKI9d6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64"
      ],
      "metadata": {
        "id": "I2wmWMOII9h9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pThxVwhnI9l0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(epochs):\n",
        "    # Sample real data and generate fake data\n",
        "    real_data = torch.tensor(data.drop(columns=['Class']).sample(batch_size).values, dtype=torch.float32)\n",
        "    z = torch.randn(batch_size, input_dim)\n",
        "    fake_data = generator(z).detach()\n",
        "\n",
        "    # Train Discriminator\n",
        "    disc_real = discriminator(real_data)\n",
        "    disc_fake = discriminator(fake_data)\n",
        "    disc_loss = loss_fn(disc_real, torch.ones(batch_size, 1)) + \\\n",
        "                loss_fn(disc_fake, torch.zeros(batch_size, 1))\n",
        "    disc_optimizer.zero_grad()\n",
        "    disc_loss.backward()\n",
        "    disc_optimizer.step()\n",
        "\n",
        "    # Train Generator\n",
        "    z = torch.randn(batch_size, input_dim)\n",
        "    fake_data = generator(z)\n",
        "    disc_fake = discriminator(fake_data)\n",
        "    gen_loss = loss_fn(disc_fake, torch.ones(batch_size, 1))\n",
        "    gen_optimizer.zero_grad()\n",
        "    gen_loss.backward()\n",
        "    gen_optimizer.step()\n",
        "\n",
        "    # Print losses every 500 epochs\n",
        "    if epoch % 500 == 0:\n",
        "        print(f\"Epoch {epoch} | D Loss: {disc_loss.item():.4f} | G Loss: {gen_loss.item():.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s_Zg_w89JZyi",
        "outputId": "dfa1b7ea-380a-4503-d4c9-cb2e0396ae9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0 | D Loss: 100.6798 | G Loss: 0.7132\n",
            "Epoch 500 | D Loss: 0.0226 | G Loss: 3.8164\n",
            "Epoch 1000 | D Loss: 0.0033 | G Loss: 5.7157\n",
            "Epoch 1500 | D Loss: 0.0010 | G Loss: 6.8902\n",
            "Epoch 2000 | D Loss: 0.0005 | G Loss: 7.5277\n",
            "Epoch 2500 | D Loss: 0.0004 | G Loss: 7.7314\n",
            "Epoch 3000 | D Loss: 0.0002 | G Loss: 8.4336\n",
            "Epoch 3500 | D Loss: 0.0002 | G Loss: 8.6356\n",
            "Epoch 4000 | D Loss: 0.0001 | G Loss: 8.8880\n",
            "Epoch 4500 | D Loss: 0.0001 | G Loss: 9.2782\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "2wVBcYqZJe9k"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qdhfVe_hJfwL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lavD9M1qJf0g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uWML654xJf4w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "4jVXPkgZJgFr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 8: Generate Synthetic Data and Compare Distributions\n",
        "- Generate synthetic data using the trained generator.\n",
        "- Compare the synthetic data’s distribution to the real data to assess quality."
      ],
      "metadata": {
        "id": "WFFfIlzzP9d4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_samples = 10000"
      ],
      "metadata": {
        "id": "vITEEBsAJhGq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z = torch.randn(synthetic_samples, input_dim)"
      ],
      "metadata": {
        "id": "zyGZJzTpJhK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "synthetic_data = generator(z).detach().numpy()"
      ],
      "metadata": {
        "id": "YMryYZYcJhVq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hTGCXdt-JhcF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(data['V1'], label=\"Real Data (V1)\")\n",
        "sns.kdeplot(synthetic_data[:, 0], label=\"Synthetic Data (V1)\")\n",
        "plt.legend()\n",
        "plt.title(\"Real vs Synthetic Data (Feature V1)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "EHi0ypnsJZ3h",
        "outputId": "2df54bde-d032-4161-a272-dbc11a2fab52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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FtG3bFgDw22+/ISsrCzt27NDbU1bQIZ6nbcOLfOZMTExQs2ZNxMXFFXVzULNmTQgh4Orq+sxDSufPn8c///yDNWvWYPDgwVJ7QTfFfNo25u01TE1N1Wu/ceNGkerdt28f2rRpo3eYryiUSiV69+6NtWvXIjExEWFhYXjnnXeg0WiKNZ9cXFzcU68IpfKH5xzRK6dDhw546623sHjxYmRmZsLe3h4dOnTAqlWrCryZW3JysvTfxsbG+faOhIWFvdAdt+vXr4+GDRti48aN2LhxI6pUqaJ3r5Xc3Nx8hybs7e1RtWpVZGVlPXPuAwcOFLg35/fffwfw5DJ9uUGDBuH+/fsYOXIk0tPT893PpyjyzlP67xdeYRXl91KUdXl7e+Pu3btYunRpvr6i7PmSy83Nxbhx4/D3339j3LhxUKlUAP5v75h8Xq1Wi+Dg4HxzWFpaFlj/i37m3N3dcfr06cJuiqRXr14wNjbGF198kW/9Qgjcu3dPqi+vTd4fFBSUb86n/Z6cnZ1hbGyMw4cP67UvX7680PX27dsXubm5mDt3br6+x48fF/pzOHDgQOTk5GDkyJFITk7Wu7dRcWm1WsTGxqJ169YvPBeVDdxzRK+kSZMmoU+fPggJCcGoUaOwbNkytG3bFg0bNsTw4cNRo0YNJCYmIjIyEv/++690T5nu3btjzpw5GDp0KFq3bo3z589j/fr1L3xjt379+mHmzJkwNzeHn5+f3iGDBw8eoHr16ujduzcaN24MKysr7Nu3D6dOncLChQufOe/YsWPx8OFDfPDBB6hXrx6ys7Nx/PhxbNy4ES4uLhg6dKje+CZNmuCNN95AWFgY6tev/8xLwJ+nWbNmAIBx48bB09OzWIcnCvt7efPNN2FsbIxvvvkGWq0WSqVSurfQfw0ePBhr167FhAkTcPLkSbz99tvIyMjAvn378PHHH6NHjx7PrEmr1WLdunUAgIcPH0p3yI6NjUX//v31vpy7dOkCMzMzvPfee1Lg/PHHH2Fvb58v8DVr1gwrVqzAl19+iVq1asHe3h7vvPPOC3/mevTogf/973/4559/inRScc2aNfHll19i2rRpuH79Onr27Alra2vExcVh27ZtGDFiBCZOnIh69eqhZs2amDhxIm7dugWVSoUtW7YUeCjvaZ8JtVqNPn364Pvvv4dCoUDNmjWxc+fOIt2otH379hg5ciQCAwMRHR2NLl26wNTUFFeuXEFYWBiCgoLQu3fvQs1TvXp1/Prrr7CwsECvXr0KHPfbb79Jn7+cnBycO3cOX375JQDg/fffR6NGjaSx+/btgxDiuZ8tKkdK89I4opKUdyn/qVOn8vXl5uaKmjVripo1a4rHjx8LIYSIjY0VgwcPFhqNRpiamopq1aqJ7t27i82bN0vLZWZmik8//VRUqVJFWFhYiDZt2ojIyMh8l4sX9lL+PFeuXJEupT569KheX1ZWlpg0aZJo3LixsLa2FpaWlqJx48Zi+fLlz5139+7dwtfXV9SrV09YWVkJMzMzUatWLTF27FiRmJhY4DLz588XAMRXX32Vry9vuxYsWJCvD4CYNWuW9PPjx4/F2LFjhZ2dnVAoFNIl3EWZQ4jC/V6EEOLHH38UNWrUEMbGxnqXhf/3dyPEk0vPP//8c+Hq6ipMTU2FRqMRvXv3FrGxsQW+J3nat2+vd9m7lZWVqF27tvjoo4/E3r17C1xmx44dolGjRsLc3Fy4uLiIb775RrqdQFxcnDQuISFBeHl5CWtrawFAqrmwn7mnycrKEpUrVxZz587Va8+7lD85OfmZy2/ZskW0bdtWWFpaCktLS1GvXj3h7++vd2l7TEyM8PDwEFZWVqJy5cpi+PDh4uzZs/n+DjztMyGEEMnJycLb21tUqFBBVKxYUYwcOVJcuHChwEv5LS0tn1rvDz/8IJo1ayYsLCyEtbW1aNiwoZg8ebK4ffv2c9+rPJMmTRIARN++fZ86Ju+WAgW9/vv3vl+/fqJt27aFXj+VfQohirmfmYjKpbzncF2/fj3fFWVUPs2dOxfBwcG4cuVKsc8ho+JJSEiAq6srNmzYwD1HrxCGI6LXiBACjRs3RqVKlZ57XxgqP9LT01GjRg0sWrSoRM6hocKbOnUq9u/fj5MnTxq6FCpBDEdEr4GMjAzs2LEDBw4cwI8//ohff/2VTxAnInoKhiOi18D169fh6uoKGxsbfPzxx5g3b56hSyIiKrMYjoiIiIhkeJ8jIiIiIhmGIyIiIiIZ3gSyEHQ6HW7fvg1ra2s+WJCIiKicEELgwYMHqFq1ar7n9T0Lw1Eh3L59G46OjoYug4iIiIrh5s2bqF69eqHHMxwVgrW1NYAnb27eM5WIiIiobEtLS4Ojo6P0PV5YDEeFkHcoTaVSMRwRERGVM0U9JYYnZBMRERHJMBwRERERyTAcEREREcnwnKMSlJubi5ycHEOXQfRSmJmZFelSWCKi8sqg4Sg3NxezZ8/GunXrkJCQgKpVq2LIkCGYPn26dPKUEAKzZs3Cjz/+iNTUVLRp0wYrVqxA7dq1pXlSUlIwduxY/PbbbzAyMoK3tzeCgoJgZWUljTl37hz8/f1x6tQp2NnZYezYsZg8eXKJbIcQAgkJCUhNTS2R+YjKIiMjI7i6usLMzMzQpRARvVQGDUfffPMNVqxYgTVr1qBBgwY4ffo0hg4dCrVajXHjxgEA5s+fjyVLlmDNmjVwdXXFjBkz4OnpiZiYGJibmwMABg4ciDt37iA8PBw5OTkYOnQoRowYgdDQUABPLuXr0qULPDw8sHLlSpw/fx6+vr6wsbHBiBEjXng78oKRvb09KlSowBtF0isn70aod+7cgZOTEz/jRPRqEwbk5eUlfH199dp69eolBg4cKIQQQqfTCY1GIxYsWCD1p6amCqVSKX755RchhBAxMTECgDh16pQ0Zvfu3UKhUIhbt24JIYRYvny5qFixosjKypLGTJkyRdStW7dQdWq1WgFAaLXafH2PHz8WMTEx4u7du4XcaqLyKTU1VcTExIjs7GxDl0JEVCjP+v5+FoOeQNC6dWtERETgn3/+AQCcPXsWR48exbvvvgsAiIuLQ0JCAjw8PKRl1Go1WrZsicjISABAZGQkbGxs0Lx5c2mMh4cHjIyMcOLECWlMu3bt9A4HeHp64vLly7h//36+urKyspCWlqb3epq8c4wqVKhQ3LeBqFzI+/uTm5tr4EqIiF4ugx5Wmzp1KtLS0lCvXj0YGxsjNzcX8+bNw8CBAwE8OVwFAA4ODnrLOTg4SH0JCQmwt7fX6zcxMYGtra3eGFdX13xz5PVVrFhRry8wMBBffPFFkbaFhxnoVcfPOBG9Lgy652jTpk1Yv349QkNDcebMGaxZswbffvst1qxZY8iyMG3aNGi1Wul18+ZNg9ZDREREpceg4WjSpEmYOnUq+vfvj4YNG2LQoEEICAhAYGAgAECj0QAAEhMT9ZZLTEyU+jQaDZKSkvT6Hz9+jJSUFL0xBc0hX4ecUqmUHhXCR4a8uCFDhqBnz56GLqPY2rVrJ53cX5KmTp2KsWPHlvi8RET0Ygwajh4+fJjvvinGxsbQ6XQAAFdXV2g0GkREREj9aWlpOHHiBNzd3QEA7u7uSE1NRVRUlDRm//790Ol0aNmypTTm8OHDevcgCg8PR926dfMdUnudDBkyBAqFAgqFAqampnB1dcXkyZORmZlZqnUcPHhQqsPIyAhqtRpNmjTB5MmTcefOnSLPp1AosH379hKpbceOHUhMTET//v2RnZ2NypUr4+uvvy5w7Ny5c+Hg4ICcnBzcuXMHAwYMQJ06dWBkZITx48fnGz9x4kSsWbMG165dK5FaiYioZBg0HL333nuYN28edu3ahevXr2Pbtm347rvv8MEHHwB48iU3fvx4fPnll9ixYwfOnz+PwYMHo2rVqtKeiPr166Nr164YPnw4Tp48iWPHjmHMmDHo378/qlatCgAYMGAAzMzM4Ofnh4sXL2Ljxo0ICgrChAkTDLXpZUbXrl1x584dXLt2DYsWLcKqVaswa9Ysg9Ry+fJl3L59G6dOncKUKVOwb98+vPHGGzh//rxB6gGAJUuWYOjQoTAyMoKZmRk++ugjBAcH5xsnhEBISAgGDx4MU1NTZGVlwc7ODtOnT0fjxo0LnLty5crw9PTEihUrXvZmEBFRUbyci+cKJy0tTXzyySfCyclJmJubixo1aojPP/9c75J7nU4nZsyYIRwcHIRSqRSdOnUSly9f1pvn3r174sMPPxRWVlZCpVKJoUOHigcPHuiNOXv2rGjbtq1QKpWiWrVq4uuvvy50nc+6FPDRo0ciJiZGPHr0qIhbb3g+Pj6iR48eem29evUSTZo0kX7Ozc0VX331lXBxcRHm5uaiUaNGIiwsTOp//Pix8PX1lfrr1KkjFi9e/Nz1yB04cEAAEPfv39drf/jwoahbt65o06aN1Hby5Enh4eEhKlWqJFQqlWjXrp2IioqS+p2dnQUA6eXs7CyEEOLq1avi/fffF/b29sLS0lI0b95chIeHP/P9SUpKEgqFQly4cEFqO3funAAgjhw5UuA2/P333/nmad++vfjkk08KXMeaNWtE9erVn1lHWVGeP+tUjqQlCJF0+fnjiAqhuJfyG/RqNWtrayxevBiLFy9+6hiFQoE5c+Zgzpw5Tx1ja2v73HNCGjVqhCNHjhS31CIRQuBRjmEud7YwNS72VUUXLlzA8ePH4ezsLLUFBgZi3bp1WLlyJWrXro3Dhw/jo48+gp2dHdq3bw+dTofq1asjLCwMlSpVwvHjxzFixAhUqVIFffv2fbFtsbDAqFGjEBAQgKSkJNjb2+PBgwfw8fHB999/DyEEFi5ciG7duuHKlSuwtrbGqVOnYG9vj+DgYHTt2hXGxsYAgPT0dHTr1g3z5s2DUqnE2rVr8d577+Hy5ctwcnIqcP1Hjx5FhQoVUL9+famtYcOGaNGiBVavXo22bdtK7cHBwWjdujXq1atXpG1866238O+//+L69etwcXEp+ptE9KpZWOfJnwExgLqaYWuh1xafrfYSPMrJhdvMPQZZd8wcT1QwK/yvdefOnbCyssLjx4+RlZUFIyMjLF26FMCT+z199dVX2Ldvn3SOV40aNXD06FGsWrUK7du3h6mpqd5tD1xdXREZGYlNmza9cDgCIIWN69evw97eHu+8845e/w8//AAbGxscOnQI3bt3h52dHQDAxsZG72T7xo0b6x3emjt3LrZt24YdO3ZgzJgxBa77xo0bcHBwyHdenJ+fHyZOnIglS5bAysoKDx48wObNm7FkyZIib1/eod8bN24wHBHJJZxnOCKD4VMkX3MdO3ZEdHQ0Tpw4AR8fHwwdOhTe3t4AgKtXr+Lhw4fo3LkzrKyspNfatWsRGxsrzbFs2TI0a9YMdnZ2sLKywg8//ID4+PgSqU8IAeD/7rGTmJiI4cOHo3bt2lCr1VCpVEhPT3/u+tLT0zFx4kTUr18fNjY2sLKywt9///3M5R49eiQ9okbuww8/RG5uLjZt2gQA2LhxI4yMjNCvX78ib5+FhQWAJxcnEBFR2cA9Ry+BhakxYuZ4GmzdRWFpaYlatWoBAFavXo3GjRvj559/hp+fH9LT0wEAu3btQrVq+v8Hp1QqAQAbNmzAxIkTsXDhQri7u8Pa2hoLFiyQ7k7+ov7++28AkPaq+Pj44N69ewgKCoKzszOUSiXc3d2RnZ39zHkmTpyI8PBwfPvtt6hVqxYsLCzQu3fvZy5XuXLlAu+grlKp0Lt3bwQHB8PX1xfBwcHo27ev3oOOCyslJQUApD1eRERkeAxHL4FCoSjSoa2ywsjICJ999hkmTJiAAQMGwM3NDUqlEvHx8Wjfvn2Byxw7dgytW7fGxx9/LLXJ9yq9iEePHuGHH35Au3btpPBw7NgxLF++HN26dQMA3Lx5E3fv3tVbztTUNN8jLo4dO4YhQ4ZIV0Kmp6fj+vXrz1x/kyZNkJCQgPv37+e75YOfnx86dOiAnTt34vjx41iwYEGxtvHChQswNTVFgwYNirU8ERGVPB5WIz19+vSBsbExli1bBmtra0ycOBEBAQFYs2YNYmNjcebMGXz//ffSXcxr166N06dPY8+ePfjnn38wY8YMnDp1qljrTkpKQkJCAq5cuYINGzagTZs2uHv3rt6l7rVr18b//vc//P333zhx4gQGDhwoHZrK4+LigoiICCnY5C23detWREdH4+zZsxgwYIB0P62nadKkCSpXroxjx47l62vXrh1q1aqFwYMHo169emjdunW+MdHR0YiOjkZ6ejqSk5MRHR2NmJgYvTFHjhzB22+/nW8biIjIcBiOSI+JiQnGjBmD+fPnIyMjA3PnzsWMGTMQGBgo3VNq165d0rPqRo4ciV69eqFfv35o2bIl7t27p7cXqSjq1q2LqlWrolmzZvj666/h4eGBCxcuwM3NTRrz888/4/79+2jatCkGDRqEcePG5Xu23sKFCxEeHg5HR0c0adIEAPDdd9+hYsWKaN26Nd577z14enqiadOmz6zH2NgYQ4cOxfr16/P1KRQK+Pr64v79+/D19S1w+SZNmqBJkyaIiopCaGgomjRpIu3xyrNhwwYMHz68UO8PERGVDoXIO+OVniotLQ1qtRparTbfo0QyMzMRFxcHV1fXAk/epfItISEBDRo0wJkzZ/RucVASdu/ejU8//RTnzp2DiUnZPwzLzzqVitnqJ39+uBGo29WwtVC596zv72fhniOiZ9BoNPj5559L7Oo7uYyMDAQHB5eLYERE9Drhv8pEz/GyHprbu3fvlzIvERG9GO45IiIiIpJhOCIiIiKSYTgiIiIikmE4IiIiIpJhOCIiIiKSYTgiIiIikmE4IiIiIpJhOCKDmD17Nt58882XMneHDh0wfvz4lzK3oUVERKB+/fr5Hqz7ou7evQt7e3v8+++/JTovEVF5xHD0GktOTsbo0aPh5OQEpVIJjUYDT0/PAh+0+iIUCgW2b99eonMCwMGDB6FQKJCamqrXvnXrVsydO7fY83bo0AEKhQIKhQJKpRLVqlXDe++9h61btxZ5rpIOgZMnT8b06dNhbGyMhQsXomLFisjMzMw37uHDh1CpVFiyZAkA4IcffkCHDh2gUqkKfM8qV66MwYMHY9asWSVWKxFRecVw9Brz9vbGX3/9hTVr1uCff/7Bjh070KFDB9y7d8/Qpb0QW1tbWFtbv9Acw4cPx507dxAbG4stW7bAzc0N/fv3x4gRI0qoyqI7evQoYmNj4e3tDQAYNGgQMjIyCgxtmzdvRnZ2Nj766CMAT8JS165d8dlnnz11/ryH7KakpLycDSAiKi8EPZdWqxUAhFarzdf36NEjERMTIx49emSAyorv/v37AoA4ePDgU8cMHTpUeHl56bVlZ2cLOzs78dNPPwkhhGjfvr0YO3asmDRpkqhYsaJwcHAQs2bNksY7OzsLANLL2dlZCCHErFmzROPGjcXatWuFs7OzUKlUol+/fiItLU1aNjc3V3z11VfCxcVFmJubi0aNGomwsDAhhBBxcXF68wIQPj4+Uk2ffPKJNE9mZqaYPHmyqF69ujAzMxM1a9aU6i/If5fPs3r1agFAhIeHS22TJ08WtWvXFhYWFsLV1VVMnz5dZGdnCyGECA4OzldjcHCwEEKIhQsXijfeeENUqFBBVK9eXYwePVo8ePDgqTUJIYS/v7/o3bu3XluvXr1Ep06dCtyGfv365Ws/cOCAACDu379f4DpcXV2f+t6U1886lTOzVE9el3YbuhJ6BTzr+/tZuOfoZRACyM4wzEuIQpVoZWUFKysrbN++HVlZWQWOGTZsGP744w/cuXNHatu5cycePnyIfv36SW1r1qyBpaUlTpw4gfnz52POnDkIDw8HAJw6dQoAEBwcjDt37kg/A0BsbCy2b9+OnTt3YufOnTh06BC+/vprqT8wMBBr167FypUrcfHiRQQEBOCjjz7CoUOH4OjoiC1btgAALl++jDt37iAoKKjA7Rg8eDB++eUXLFmyBH///TdWrVoFKyurQr1Pcj4+PqhYsaLenhpra2uEhIQgJiYGQUFB+PHHH7Fo0SIAQL9+/fDpp5+iQYMGuHPnDu7cuSO9b0ZGRliyZAkuXryINWvWYP/+/Zg8efIz13/kyBE0b95cr83Pzw/79+/HjRs3pLZr167h8OHD8PPzK/I2vvXWWzhy5EiRlyMiepXwwbMvQ85D4Kuqhln3Z7cBM8vnDjMxMUFISAiGDx+OlStXomnTpmjfvj369++PRo0aAQBat26NunXr4n//+5/0xR0cHIw+ffrohYtGjRpJ56rUrl0bS5cuRUREBDp37gw7OzsAgI2NDTQajV4NOp0OISEh0iGwQYMGISIiAvPmzUNWVha++uor7Nu3D+7u7gCAGjVq4OjRo1i1ahXat28PW1tbAIC9vT1sbGwK3M5//vkHmzZtQnh4ODw8PKR5isPIyAh16tTB9evXpbbp06dL/+3i4oKJEydiw4YNmDx5MiwsLGBlZQUTE5N82y4/YdzFxQVffvklRo0aheXLlz91/Tdu3EDVqvqfK09PT1StWhXBwcGYPXs2ACAkJASOjo7o1KlTkbexatWq+Ouvv4q8HBHRq4R7jl5j3t7euH37Nnbs2IGuXbvi4MGDaNq0KUJCQqQxw4YNQ3BwMAAgMTERu3fvhq+vr948eWEqT5UqVZCUlPTc9bu4uOidGyRf7urVq3j48CE6d+4s7eWysrLC2rVrERsbW+htjI6OhrGxMdq3b1/oZZ5FCAGFQiH9vHHjRrRp0wYajQZWVlaYPn064uPjnzvPvn370KlTJ1SrVg3W1tYYNGgQ7t27h4cPHz51mUePHsHc3FyvzdjYGD4+PggJCYEQAjqdDmvWrMHQoUNhZFT0v94WFhbPrIGI6HXAPUcvg2mFJ3twDLXuIjA3N0fnzp3RuXNnzJgxA8OGDcOsWbMwZMgQAE8OSU2dOhWRkZE4fvw4XF1d8fbbb+uv0tRU72eFQgGdTvf8Up+xXHp6OgBg165dqFatmt44pVJZ6O2zsLAo9Njnyc3NxZUrV9CiRQsAQGRkJAYOHIgvvvgCnp6eUKvV2LBhAxYuXPjMea5fv47u3btj9OjRmDdvHmxtbXH06FH4+fkhOzsbFSoU/DusXLky7t+/n6/d19cXgYGB2L9/P3Q6HW7evImhQ4cWaxtTUlKkvX1ERK8rhqOXQaEo1KGtssjNzU3vsvtKlSqhZ8+eCA4ORmRkZLG+dE1NTYt8Xx43NzcolUrEx8c/da+PmZkZADxz7oYNG0Kn0+HQoUPSYbXiWrNmDe7fvy9dLXb8+HE4Ozvj888/l8bIz/3Jq/G/9UVFRUGn02HhwoXS3p1NmzY9d/1NmjRBTExMvvaaNWuiffv2WL16NYQQ8PDwgLOzc5G3DwAuXLiADh06FGtZIqJXBcPRa+revXvo06cPfH190ahRI1hbW+P06dOYP38+evTooTd22LBh6N69O3Jzc+Hj41Pkdbm4uCAiIgJt2rSBUqlExYoVn7uMtbU1Jk6ciICAAOh0OrRt2xZarRbHjh2DSqWCj48PnJ2doVAosHPnTnTr1k06x+e/6/bx8YGvry+WLFmCxo0b48aNG0hKSkLfvn2fuv6HDx8iISEBjx8/xr///ott27Zh0aJFGD16NDp27AjgyflV8fHx2LBhA1q0aIFdu3Zh27Zt+dYfFxeH6OhoVK9eHdbW1qhVqxZycnLw/fff47333sOxY8ewcuXK574nnp6eWLNmTYF9fn5+GD58OADoHRbNk5CQgISEBFy9ehUAcP78eVhbW8PJyUk6d+vhw4eIiorCV1999dxaiIheaS/j0rlXzat4KX9mZqaYOnWqaNq0qVCr1aJChQqibt26Yvr06eLhw4d6Y3U6nXB2dhbdunXLN09Bl7336NFDuqxeCCF27NghatWqJUxMTPJdyi+3aNEiqT9vvYsXLxZ169YVpqamws7OTnh6eopDhw5JY+bMmSM0Go1QKBRPvZT/0aNHIiAgQFSpUkWYmZmJWrVqidWrVz/1vWnfvr106b2ZmZmoUqWK6N69u9i6dWu+sZMmTRKVKlUSVlZWol+/fmLRokVCrVZL/ZmZmcLb21vY2NjoXcr/3XffiSpVqggLCwvh6ekp1q5d+8xL7IUQ4t69e8Lc3FxcunQpX9/Dhw+FWq0Wtra2IjMzM1//rFmz8t1WQF6PEEKEhoaKunXrPnX95fWzTuUML+WnElTcS/kVQhTy2u/XWFpaGtRqNbRaLVQqlV5fZmYm4uLi4Orqmu9k2VdFeno6qlWrhuDgYPTq1cvQ5bzWJk2ahLS0NKxatarE527VqhXGjRuHAQMGFNj/OnzWqQyYrX7y54cbgbpdDVsLlXvP+v5+Fl6tRk+l0+mQlJSEuXPnwsbGBu+//76hS3rtff7553B2di7UCe9FcffuXfTq1Qsffvhhic5LRFQe8Zwjeqr4+Hi4urqievXqCAkJgYkJPy6GZmNj88xHgBRX5cqVn3sTSiKi1wW/7eipXFxcwKOuRET0uuFhNSIiIiIZhqMSwj0s9KrjZ5yIXhcMRy8o7y7PfOQCveqys7MBPHlkCRHRq8yg5xy5uLjku6MwAHz88cdYtmwZMjMz8emnn2LDhg3IysqCp6cnli9fDgcHB2lsfHw8Ro8ejQMHDsDKygo+Pj4IDAzUO3n44MGDmDBhAi5evAhHR0dMnz5dejzGizI2NoaNjY30TLAKFSroPXuL6FWg0+mQnJyMChUq8MR8Kh38d5QMyKD/yp06dUrv0QoXLlxA586d0adPHwBAQEAAdu3ahbCwMKjVaowZMwa9evXCsWPHADx5bISXlxc0Gg2OHz+OO3fuYPDgwTA1NZXu8hsXFwcvLy+MGjUK69evR0REBIYNG4YqVarA09OzRLYj74nrhXnYKlF5ZWRkBCcnJ4Z/InrllambQI4fPx47d+7ElStXkJaWBjs7O4SGhqJ3794AgEuXLqF+/fqIjIxEq1atsHv3bnTv3h23b9+W9iatXLkSU6ZMQXJyMszMzDBlyhTs2rULFy5ckNbTv39/pKam4o8//ihUXYW9iVRubi5ycnJe4B0gKrvMzMykZ8ERvTR5N4EcsAmoUzL/A0uvr+LeBLLM7B/Pzs7GunXrMGHCBCgUCkRFRSEnJ0fvYaH16tWDk5OTFI4iIyPRsGFDvcNsnp6eGD16NC5evIgmTZogMjIy3wNHPT09MX78+KfWkpWVhaysLOnntLS0Qm2DsbExz8cgIiIq58rM/wZu374dqamp0rlACQkJMDMzg42Njd44BwcHJCQkSGPkwSivP6/vWWPS0tLw6NGjAmsJDAyEWq2WXo6Oji+6eURERFROlJlw9PPPP+Pdd99F1apVDV0Kpk2bBq1WK71u3rxp6JKIiIiolJSJw2o3btzAvn37sHXrVqlNo9EgOzsbqampenuPEhMTpROgNRoNTp48qTdXYmKi1Jf3Z16bfIxKpYKFhUWB9SiVSiiVyhfeLiIiIip/ysSeo+DgYNjb28PLy0tqa9asGUxNTRERESG1Xb58GfHx8XB3dwcAuLu74/z583pXiYWHh0OlUsHNzU0aI58jb0zeHERERERyBg9HOp0OwcHB8PHx0bt/ilqthp+fHyZMmIADBw4gKioKQ4cOhbu7O1q1agUA6NKlC9zc3DBo0CCcPXsWe/bswfTp0+Hv7y/t+Rk1ahSuXbuGyZMn49KlS1i+fDk2bdqEgIAAg2wvERERlW0GP6y2b98+xMfHw9fXN1/fokWLYGRkBG9vb72bQOYxNjbGzp07MXr0aLi7u8PS0hI+Pj6YM2eONMbV1RW7du1CQEAAgoKCUL16dfz0008ldo8jIiIierWUqfsclVXFvU8CEREVEe9zRCWouN/fBj+sRkRERFSWMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJMBwRERERyTAcEREREckwHBERERHJGDwc3bp1Cx999BEqVaoECwsLNGzYEKdPn5b6hRCYOXMmqlSpAgsLC3h4eODKlSt6c6SkpGDgwIFQqVSwsbGBn58f0tPT9cacO3cOb7/9NszNzeHo6Ij58+eXyvYRERFR+WLQcHT//n20adMGpqam2L17N2JiYrBw4UJUrFhRGjN//nwsWbIEK1euxIkTJ2BpaQlPT09kZmZKYwYOHIiLFy8iPDwcO3fuxOHDhzFixAipPy0tDV26dIGzszOioqKwYMECzJ49Gz/88EOpbi8RERGVA8KApkyZItq2bfvUfp1OJzQajViwYIHUlpqaKpRKpfjll1+EEELExMQIAOLUqVPSmN27dwuFQiFu3bolhBBi+fLlomLFiiIrK0tv3XXr1i1UnVqtVgAQWq22SNtHRERFNEv15HX5D0NXQq+A4n5/G3TP0Y4dO9C8eXP06dMH9vb2aNKkCX788UepPy4uDgkJCfDw8JDa1Go1WrZsicjISABAZGQkbGxs0Lx5c2mMh4cHjIyMcOLECWlMu3btYGZmJo3x9PTE5cuXcf/+/Xx1ZWVlIS0tTe9FRERErweDhqNr165hxYoVqF27Nvbs2YPRo0dj3LhxWLNmDQAgISEBAODg4KC3nIODg9SXkJAAe3t7vX4TExPY2trqjSloDvk65AIDA6FWq6WXo6NjCWwtERERlQcGDUc6nQ5NmzbFV199hSZNmmDEiBEYPnw4Vq5caciyMG3aNGi1Wul18+ZNg9ZDREREpceg4ahKlSpwc3PTa6tfvz7i4+MBABqNBgCQmJioNyYxMVHq02g0SEpK0ut//PgxUlJS9MYUNId8HXJKpRIqlUrvRURERK8Hg4ajNm3a4PLly3pt//zzD5ydnQEArq6u0Gg0iIiIkPrT0tJw4sQJuLu7AwDc3d2RmpqKqKgoacz+/fuh0+nQsmVLaczhw4eRk5MjjQkPD0fdunX1rowjIiIiMmg4CggIwJ9//omvvvoKV69eRWhoKH744Qf4+/sDABQKBcaPH48vv/wSO3bswPnz5zF48GBUrVoVPXv2BPBkT1PXrl0xfPhwnDx5EseOHcOYMWPQv39/VK1aFQAwYMAAmJmZwc/PDxcvXsTGjRsRFBSECRMmGGrTiYiIqIwyMeTKW7RogW3btmHatGmYM2cOXF1dsXjxYgwcOFAaM3nyZGRkZGDEiBFITU1F27Zt8ccff8Dc3Fwas379eowZMwadOnWCkZERvL29sWTJEqlfrVZj79698Pf3R7NmzVC5cmXMnDlT715IRERERACgEEIIQxdR1qWlpUGtVkOr1fL8IyKil2m2+smfAzYBdTwNWwuVe8X9/jb440OIiIiIyhKGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiqDFIYugF5jDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyBg1Hs2fPhkKh0HvVq1dP6s/MzIS/vz8qVaoEKysreHt7IzExUW+O+Ph4eHl5oUKFCrC3t8ekSZPw+PFjvTEHDx5E06ZNoVQqUatWLYSEhJTG5hEREVE5ZPA9Rw0aNMCdO3ek19GjR6W+gIAA/PbbbwgLC8OhQ4dw+/Zt9OrVS+rPzc2Fl5cXsrOzcfz4caxZswYhISGYOXOmNCYuLg5eXl7o2LEjoqOjMX78eAwbNgx79uwp1e0kIiKi8sHE4AWYmECj0eRr12q1+PnnnxEaGop33nkHABAcHIz69evjzz//RKtWrbB3717ExMRg3759cHBwwJtvvom5c+diypQpmD17NszMzLBy5Uq4urpi4cKFAID69evj6NGjWLRoETw9PUt1W4mIiKjsM/ieoytXrqBq1aqoUaMGBg4ciPj4eABAVFQUcnJy4OHhIY2tV68enJycEBkZCQCIjIxEw4YN4eDgII3x9PREWloaLl68KI2Rz5E3Jm8OIiIiIjmD7jlq2bIlQkJCULduXdy5cwdffPEF3n77bVy4cAEJCQkwMzODjY2N3jIODg5ISEgAACQkJOgFo7z+vL5njUlLS8OjR49gYWGRr66srCxkZWVJP6elpb3wthIREVH5YNBw9O6770r/3ahRI7Rs2RLOzs7YtGlTgaGltAQGBuKLL74w2PqJiIjIcAx+WE3OxsYGderUwdWrV6HRaJCdnY3U1FS9MYmJidI5ShqNJt/Va3k/P2+MSqV6agCbNm0atFqt9Lp582ZJbB4RERGVA2UqHKWnpyM2NhZVqlRBs2bNYGpqioiICKn/8uXLiI+Ph7u7OwDA3d0d58+fR1JSkjQmPDwcKpUKbm5u0hj5HHlj8uYoiFKphEql0nsRERHR68Gg4WjixIk4dOgQrl+/juPHj+ODDz6AsbExPvzwQ6jVavj5+WHChAk4cOAAoqKiMHToULi7u6NVq1YAgC5dusDNzQ2DBg3C2bNnsWfPHkyfPh3+/v5QKpUAgFGjRuHatWuYPHkyLl26hOXLl2PTpk0ICAgw5KYTERFRGWXQc47+/fdffPjhh7h37x7s7OzQtm1b/Pnnn7CzswMALFq0CEZGRvD29kZWVhY8PT2xfPlyaXljY2Ps3LkTo0ePhru7OywtLeHj44M5c+ZIY1xdXbFr1y4EBAQgKCgI1atXx08//cTL+ImIiKhACiGEMHQRZV1aWhrUajW0Wi0PsRERvUyz1U/+HBAG1Oli2Fqo3Cvu93eZOueIiIiIyNAYjoiIiIhkGI6IiIiIZBiOiIiIiGQYjoiIiIhkGI6IiIiIZBiOiIiIiGQYjoiIiIhkGI6IiIiIZBiOiIiIiGSKFY6uXbtW0nUQERERlQnFCke1atVCx44dsW7dOmRmZpZ0TUREREQGU6xwdObMGTRq1AgTJkyARqPByJEjcfLkyZKujYiIiKjUFSscvfnmmwgKCsLt27exevVq3LlzB23btsUbb7yB7777DsnJySVdJxEREVGpeKETsk1MTNCrVy+EhYXhm2++wdWrVzFx4kQ4Ojpi8ODBuHPnTknVSURERFQqXigcnT59Gh9//DGqVKmC7777DhMnTkRsbCzCw8Nx+/Zt9OjRo6TqJCIiIioVJsVZ6LvvvkNwcDAuX76Mbt26Ye3atejWrRuMjJ5kLVdXV4SEhMDFxaUkayUiIiJ66YoVjlasWAFfX18MGTIEVapUKXCMvb09fv755xcqjoiIiKi0FSschYeHw8nJSdpTlEcIgZs3b8LJyQlmZmbw8fEpkSKJiIiISkuxzjmqWbMm7t69m689JSUFrq6uL1wUERERkaEUKxwJIQpsT09Ph7m5+QsVRERERGRIRTqsNmHCBACAQqHAzJkzUaFCBakvNzcXJ06cwJtvvlmiBRIRERGVpiKFo7/++gvAkz1H58+fh5mZmdRnZmaGxo0bY+LEiSVbIREREVEpKlI4OnDgAABg6NChCAoKgkqleilFERERERlKsa5WCw4OLuk6iIiIiMqEQoejXr16ISQkBCqVCr169Xrm2K1bt75wYURERESGUOhwpFaroVAopP8mIiIiehUVOhzJD6XxsBoRERG9qop1n6NHjx7h4cOH0s83btzA4sWLsXfv3hIrjIiIiMgQihWOevTogbVr1wIAUlNT8dZbb2HhwoXo0aMHVqxYUaIFEhEREZWmYoWjM2fO4O233wYAbN68GRqNBjdu3MDatWuxZMmSEi2QiIiIqDQVKxw9fPgQ1tbWAIC9e/eiV69eMDIyQqtWrXDjxo0SLZCIiIioNBUrHNWqVQvbt2/HzZs3sWfPHnTp0gUAkJSUxBtDEhERUblWrHA0c+ZMTJw4ES4uLmjZsiXc3d0BPNmL1KRJkxItkIiIiKg0FSsc9e7dG/Hx8Th9+jT++OMPqb1Tp05YtGhRsQr5+uuvoVAoMH78eKktMzMT/v7+qFSpEqysrODt7Y3ExES95eLj4+Hl5YUKFSrA3t4ekyZNwuPHj/XGHDx4EE2bNoVSqUStWrUQEhJSrBqJiIjo1VescAQAGo0GTZo0gZHR/03x1ltvoV69ekWe69SpU1i1ahUaNWqk1x4QEIDffvsNYWFhOHToEG7fvq13d+7c3Fx4eXkhOzsbx48fx5o1axASEoKZM2dKY+Li4uDl5YWOHTsiOjoa48ePx7Bhw7Bnz55ibDURERG96hRCCFHUhTIyMvD1118jIiICSUlJ0Ol0ev3Xrl0r9Fzp6elo2rQpli9fji+//BJvvvkmFi9eDK1WCzs7O4SGhqJ3794AgEuXLqF+/fqIjIxEq1atsHv3bnTv3h23b9+Gg4MDAGDlypWYMmUKkpOTYWZmhilTpmDXrl24cOGCtM7+/fsjNTVVb6/Xs6SlpUGtVkOr1fKcKiKil2n2/38Cw4AwoE4Xw9ZC5V5xv7+L9eDZYcOG4dChQxg0aBCqVKkiPVakOPz9/eHl5QUPDw98+eWXUntUVBRycnLg4eEhtdWrVw9OTk5SOIqMjETDhg2lYAQAnp6eGD16NC5evIgmTZogMjJSb468MfLDd/+VlZWFrKws6ee0tLRibx8RERGVL8UKR7t378auXbvQpk2bF1r5hg0bcObMGZw6dSpfX0JCAszMzGBjY6PX7uDggISEBGmMPBjl9ef1PWtMWloaHj16BAsLi3zrDgwMxBdffFHs7SIiIqLyq1jnHFWsWBG2trYvtOKbN2/ik08+wfr162Fubv5Cc5W0adOmQavVSq+bN28auiQiIiIqJcUKR3PnzsXMmTP1nq9WVFFRUUhKSkLTpk1hYmICExMTHDp0CEuWLIGJiQkcHByQnZ2N1NRUveUSExOh0WgAPDkp/L9Xr+X9/LwxKpWqwL1GAKBUKqFSqfReRERE9Hoo1mG1hQsXIjY2Fg4ODnBxcYGpqale/5kzZ547R6dOnXD+/Hm9tqFDh6JevXqYMmUKHB0dYWpqioiICHh7ewMALl++jPj4eOm+Su7u7pg3bx6SkpJgb28PAAgPD4dKpYKbm5s05vfff9dbT3h4uDQHERGVRUW+VoioxBQrHPXs2fOFV2xtbY033nhDr83S0hKVKlWS2v38/DBhwgTY2tpCpVJh7NixcHd3R6tWrQAAXbp0gZubGwYNGoT58+cjISEB06dPh7+/P5RKJQBg1KhRWLp0KSZPngxfX1/s378fmzZtwq5du154G4iIiOjVU6xwNGvWrJKuo0CLFi2CkZERvL29kZWVBU9PTyxfvlzqNzY2xs6dOzF69Gi4u7vD0tISPj4+mDNnjjTG1dUVu3btQkBAAIKCglC9enX89NNP8PT0LJVtICIiovKlWPc5AoDU1FRs3rwZsbGxmDRpEmxtbXHmzBk4ODigWrVqJV2nQfE+R0REpUS6z9EmoA7/J5ZeTKne5+jcuXPw8PCAWq3G9evXMXz4cNja2mLr1q2Ij4/H2rVrizMtERERkcEV62q1CRMmYMiQIbhy5YreZfjdunXD4cOHS6w4IiIiotJWrHB06tQpjBw5Ml97tWrVpJsvEhEREZVHxQpHSqWywEdq/PPPP7Czs3vhooiIiIgMpVjh6P3338ecOXOQk5MDAFAoFIiPj8eUKVOkexIRERERlUfFCkcLFy5Eeno67Ozs8OjRI7Rv3x61atWCtbU15s2bV9I1EhEREZWaYl2tplarER4ejmPHjuHs2bNIT09H06ZN4eHhUdL1EREREZWqIocjnU6HkJAQbN26FdevX4dCoYCrqys0Gg2EEFAoFC+jTiIiIqJSUaTDakIIvP/++xg2bBhu3bqFhg0bokGDBrhx4waGDBmCDz744GXVSURERFQqirTnKCQkBIcPH0ZERAQ6duyo17d//3707NkTa9euxeDBg0u0SCIiIqLSUqQ9R7/88gs+++yzfMEIAN555x1MnToV69evL7HiiIiIiEpbkcLRuXPn0LVr16f2v/vuuzh79uwLF0VERERkKEUKRykpKXBwcHhqv4ODA+7fv//CRREREREZSpHCUW5uLkxMnn6akrGxMR4/fvzCRRER0euOVz6T4RTphGwhBIYMGQKlUllgf1ZWVokURURERGQoRQpHPj4+zx3DK9WIiIioPCtSOAoODn5ZdRARERGVCcV6thoRERHRq4rhiIiIiEiG4YiIiIhIhuGIiIiISIbhiIiIiEiG4YiIiIhIhuGIiIiISIbhiIiIiEiG4YiIiIhIhuGIiIiISIbhiIiIiEiG4YiIiIhIhuGIiIiISIbhiIiIiEiG4YiIiIhIhuGIiIiISIbhiIiIiEjGoOFoxYoVaNSoEVQqFVQqFdzd3bF7926pPzMzE/7+/qhUqRKsrKzg7e2NxMREvTni4+Ph5eWFChUqwN7eHpMmTcLjx4/1xhw8eBBNmzaFUqlErVq1EBISUhqbR0REROWQQcNR9erV8fXXXyMqKgqnT5/GO++8gx49euDixYsAgICAAPz2228ICwvDoUOHcPv2bfTq1UtaPjc3F15eXsjOzsbx48exZs0ahISEYObMmdKYuLg4eHl5oWPHjoiOjsb48eMxbNgw7Nmzp9S3l4iIiMo+hRBCGLoIOVtbWyxYsAC9e/eGnZ0dQkND0bt3bwDApUuXUL9+fURGRqJVq1bYvXs3unfvjtu3b8PBwQEAsHLlSkyZMgXJyckwMzPDlClTsGvXLly4cEFaR//+/ZGamoo//vijUDWlpaVBrVZDq9VCpVKV/EYTEdETs9VP/hwQBtTpYthaqNwr7vd3mTnnKDc3Fxs2bEBGRgbc3d0RFRWFnJwceHh4SGPq1asHJycnREZGAgAiIyPRsGFDKRgBgKenJ9LS0qS9T5GRkXpz5I3Jm6MgWVlZSEtL03sRERHR68Hg4ej8+fOwsrKCUqnEqFGjsG3bNri5uSEhIQFmZmawsbHRG+/g4ICEhAQAQEJCgl4wyuvP63vWmLS0NDx69KjAmgIDA6FWq6WXo6NjSWwqERERlQMGD0d169ZFdHQ0Tpw4gdGjR8PHxwcxMTEGrWnatGnQarXS6+bNmwath4iIiEqPiaELMDMzQ61atQAAzZo1w6lTpxAUFIR+/fohOzsbqampenuPEhMTodFoAAAajQYnT57Umy/vajb5mP9e4ZaYmAiVSgULC4sCa1IqlVAqlSWyfURERFS+GHzP0X/pdDpkZWWhWbNmMDU1RUREhNR3+fJlxMfHw93dHQDg7u6O8+fPIykpSRoTHh4OlUoFNzc3aYx8jrwxeXMQERERyRl0z9G0adPw7rvvwsnJCQ8ePEBoaCgOHjyIPXv2QK1Ww8/PDxMmTICtrS1UKhXGjh0Ld3d3tGrVCgDQpUsXuLm5YdCgQZg/fz4SEhIwffp0+Pv7S3t+Ro0ahaVLl2Ly5Mnw9fXF/v37sWnTJuzatcuQm05ERERllEHDUVJSEgYPHow7d+5ArVajUaNG2LNnDzp37gwAWLRoEYyMjODt7Y2srCx4enpi+fLl0vLGxsbYuXMnRo8eDXd3d1haWsLHxwdz5syRxri6umLXrl0ICAhAUFAQqlevjp9++gmenp6lvr1ERERU9pW5+xyVRbzPERFRKeF9jqgElfv7HBERERGVBQxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgxHRERERDIMR0REREQyDEdEREREMgYNR4GBgWjRogWsra1hb2+Pnj174vLly3pjMjMz4e/vj0qVKsHKygre3t5ITEzUGxMfHw8vLy9UqFAB9vb2mDRpEh4/fqw35uDBg2jatCmUSiVq1aqFkJCQl715REREVA4ZNBwdOnQI/v7++PPPPxEeHo6cnBx06dIFGRkZ0piAgAD89ttvCAsLw6FDh3D79m306tVL6s/NzYWXlxeys7Nx/PhxrFmzBiEhIZg5c6Y0Ji4uDl5eXujYsSOio6Mxfvx4DBs2DHv27CnV7SUiIqKyTyGEEIYuIk9ycjLs7e1x6NAhtGvXDlqtFnZ2dggNDUXv3r0BAJcuXUL9+vURGRmJVq1aYffu3ejevTtu374NBwcHAMDKlSsxZcoUJCcnw8zMDFOmTMGuXbtw4cIFaV39+/dHamoq/vjjj+fWlZaWBrVaDa1WC5VK9XI2noiIgNnqJ38OCAPqdDFsLVTuFff7u0ydc6TVagEAtra2AICoqCjk5OTAw8NDGlOvXj04OTkhMjISABAZGYmGDRtKwQgAPD09kZaWhosXL0pj5HPkjcmb47+ysrKQlpam9yIiIqLXQ5kJRzqdDuPHj0ebNm3wxhtvAAASEhJgZmYGGxsbvbEODg5ISEiQxsiDUV5/Xt+zxqSlpeHRo0f5agkMDIRarZZejo6OJbKNREREVPaVmXDk7++PCxcuYMOGDYYuBdOmTYNWq5VeN2/eNHRJREREVEpMDF0AAIwZMwY7d+7E4cOHUb16daldo9EgOzsbqampenuPEhMTodFopDEnT57Umy/vajb5mP9e4ZaYmAiVSgULC4t89SiVSiiVyhLZNiIiIipfDLrnSAiBMWPGYNu2bdi/fz9cXV31+ps1awZTU1NERERIbZcvX0Z8fDzc3d0BAO7u7jh//jySkpKkMeHh4VCpVHBzc5PGyOfIG5M3BxEREVEeg+458vf3R2hoKH799VdYW1tL5wip1WpYWFhArVbDz88PEyZMgK2tLVQqFcaOHQt3d3e0atUKANClSxe4ublh0KBBmD9/PhISEjB9+nT4+/tLe39GjRqFpUuXYvLkyfD19cX+/fuxadMm7Nq1y2DbTkRERGWTQfccrVixAlqtFh06dECVKlWk18aNG6UxixYtQvfu3eHt7Y127dpBo9Fg69atUr+xsTF27twJY2NjuLu746OPPsLgwYMxZ84caYyrqyt27dqF8PBwNG7cGAsXLsRPP/0ET0/PUt1eIiIiKvvK1H2Oyire54iIqJTwPkdUgl6J+xwRERERGRrDEREREZEMwxERERGRDMMRERGVPQqFoSug1xjDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkYxBw9Hhw4fx3nvvoWrVqlAoFNi+fbtevxACM2fORJUqVWBhYQEPDw9cuXJFb0xKSgoGDhwIlUoFGxsb+Pn5IT09XW/MuXPn8Pbbb8Pc3ByOjo6YP3/+y940IiIiKqcMGo4yMjLQuHFjLFu2rMD++fPnY8mSJVi5ciVOnDgBS0tLeHp6IjMzUxozcOBAXLx4EeHh4di5cycOHz6MESNGSP1paWno0qULnJ2dERUVhQULFmD27Nn44YcfXvr2ERERUTkkyggAYtu2bdLPOp1OaDQasWDBAqktNTVVKJVK8csvvwghhIiJiREAxKlTp6Qxu3fvFgqFQty6dUsIIcTy5ctFxYoVRVZWljRmypQpom7duoWuTavVCgBCq9UWd/OIiKgwZqmevP7Za+hK6BVQ3O/vMnvOUVxcHBISEuDh4SG1qdVqtGzZEpGRkQCAyMhI2NjYoHnz5tIYDw8PGBkZ4cSJE9KYdu3awczMTBrj6emJy5cv4/79+wWuOysrC2lpaXovIiIiej2U2XCUkJAAAHBwcNBrd3BwkPoSEhJgb2+v129iYgJbW1u9MQXNIV/HfwUGBkKtVksvR0fHF98gIiIiKhfKbDgypGnTpkGr1UqvmzdvGrokIiIiKiVlNhxpNBoAQGJiol57YmKi1KfRaJCUlKTX//jxY6SkpOiNKWgO+Tr+S6lUQqVS6b2IiIjo9VBmw5Grqys0Gg0iIiKktrS0NJw4cQLu7u4AAHd3d6SmpiIqKkoas3//fuh0OrRs2VIac/jwYeTk5EhjwsPDUbduXVSsWLGUtoaIiIjKC4OGo/T0dERHRyM6OhrAk5Owo6OjER8fD4VCgfHjx+PLL7/Ejh07cP78eQwePBhVq1ZFz549AQD169dH165dMXz4cJw8eRLHjh3DmDFj0L9/f1StWhUAMGDAAJiZmcHPzw8XL17Exo0bERQUhAkTJhhoq4mIiKgsMzHkyk+fPo2OHTtKP+cFFh8fH4SEhGDy5MnIyMjAiBEjkJqairZt2+KPP/6Aubm5tMz69esxZswYdOrUCUZGRvD29saSJUukfrVajb1798Lf3x/NmjVD5cqVMXPmTL17IRERERHlUQghhKGLKOvS0tKgVquh1Wp5/hER0cs0W/3kz4GbgdqdDVsLlXvF/f4us+ccERERERkCwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkQzDEREREZEMwxERERGRDMMRERERkcxrFY6WLVsGFxcXmJubo2XLljh58qShSyIiIqIy5rUJRxs3bsSECRMwa9YsnDlzBo0bN4anpyeSkpIMXRoRERGVIa9NOPruu+8wfPhwDB06FG5ubli5ciUqVKiA1atXG7o0IiIiKkNMDF1AacjOzkZUVBSmTZsmtRkZGcHDwwORkZEGqyszJxcHL3PPFRFRnq7//8/T11NwN+uOQWuh0qU0MUbHevaGLgPAaxKO7t69i9zcXDg4OOi1Ozg44NKlS/nGZ2VlISsrS/pZq9UCANLS0kq0ruQHmRjx89ESnZOIqDy7YC4AAN/9cQFHda/NwQ0CYGdlhgOTOpbonHnf20KIIi33WoSjogoMDMQXX3yRr93R0dEA1RARvT7U0n99acAqyBBuAlC/pF/7gwcPoFarnz/w/3stwlHlypVhbGyMxMREvfbExERoNJp846dNm4YJEyZIP+t0OqSkpKBSpUpQKBQvvd7SkJaWBkdHR9y8eRMqlcrQ5ZRZfJ8Kh+/T8/E9Khy+T4XD9+n58t6jmJgYVK1atUjLvhbhyMzMDM2aNUNERAR69uwJ4EngiYiIwJgxY/KNVyqVUCqVem02NjalUGnpU6lU/ItVCHyfCofv0/PxPSocvk+Fw/fp+apVqwYjo6Idon0twhEATJgwAT4+PmjevDneeustLF68GBkZGRg6dKihSyMiIqIy5LUJR/369UNycjJmzpyJhIQEvPnmm/jjjz/ynaRNREREr7fXJhwBwJgxYwo8jPY6UiqVmDVrVr7Dh6SP71Ph8H16Pr5HhcP3qXD4Pj3fi7xHClHU69uIiIiIXmG8iQQRERGRDMMRERERkQzDEREREZEMwxERERGRDMMRAQDef/99ODk5wdzcHFWqVMGgQYNw+/ZtQ5dVZly/fh1+fn5wdXWFhYUFatasiVmzZiE7O9vQpZU58+bNQ+vWrVGhQoVX9uapxbFs2TK4uLjA3NwcLVu2xMmTJw1dUply+PBhvPfee6hatSoUCgW2b99u6JLKnMDAQLRo0QLW1tawt7dHz549cfnyZUOXVeasWLECjRo1km6Q6e7ujt27dxdpDoYjAgB07NgRmzZtwuXLl7FlyxbExsaid+/ehi6rzLh06RJ0Oh1WrVqFixcvYtGiRVi5ciU+++wzQ5dW5mRnZ6NPnz4YPXq0oUspMzZu3IgJEyZg1qxZOHPmDBo3bgxPT08kJSUZurQyIyMjA40bN8ayZcsMXUqZdejQIfj7++PPP/9EeHg4cnJy0KVLF2RkZBi6tDKlevXq+PrrrxEVFYXTp0/jnXfeQY8ePXDx4sVCz8FL+alAO3bsQM+ePZGVlQVTU1NDl1MmLViwACtWrMC1a9cMXUqZFBISgvHjxyM1NdXQpRhcy5Yt0aJFCyxduhTAk8cXOTo6YuzYsZg6daqBqyt7FAoFtm3bJj3uiQqWnJwMe3t7HDp0CO3atTN0OWWara0tFixYAD8/v0KN554jyiclJQXr169H69atGYyeQavVwtbW1tBlUBmXnZ2NqKgoeHh4SG1GRkbw8PBAZGSkASuj8k6r1QIA/x16htzcXGzYsAEZGRlwd3cv9HIMRySZMmUKLC0tUalSJcTHx+PXX381dEll1tWrV/H9999j5MiRhi6Fyri7d+8iNzc336OKHBwckJCQYKCqqLzT6XQYP3482rRpgzfeeMPQ5ZQ558+fh5WVFZRKJUaNGoVt27bBzc2t0MszHL3Cpk6dCoVC8czXpUuXpPGTJk3CX3/9hb1798LY2BiDBw/Gq37UtajvEQDcunULXbt2RZ8+fTB8+HADVV66ivM+EdHL4+/vjwsXLmDDhg2GLqVMqlu3LqKjo3HixAmMHj0aPj4+iImJKfTyPOfoFZacnIx79+49c0yNGjVgZmaWr/3ff/+Fo6Mjjh8/XqRdkeVNUd+j27dvo0OHDmjVqhVCQkJgZPR6/P9FcT5LPOfoiezsbFSoUAGbN2/WO4fGx8cHqamp3ENbAJ5z9GxjxozBr7/+isOHD8PV1dXQ5ZQLHh4eqFmzJlatWlWo8a/Vg2dfN3Z2drCzsyvWsjqdDgCQlZVVkiWVOUV5j27duoWOHTuiWbNmCA4Ofm2CEfBin6XXnZmZGZo1a4aIiAjpy16n0yEiIoIPwqYiEUJg7Nix2LZtGw4ePMhgVAQ6na5I32cMR4QTJ07g1KlTaNu2LSpWrIjY2FjMmDEDNWvWfKX3GhXFrVu30KFDBzg7O+Pbb79FcnKy1KfRaAxYWdkTHx+PlJQUxMfHIzc3F9HR0QCAWrVqwcrKyrDFGciECRPg4+OD5s2b46233sLixYuRkZGBoUOHGrq0MiM9PR1Xr16Vfo6Li0N0dDRsbW3h5ORkwMrKDn9/f4SGhuLXX3+FtbW1dM6aWq2GhYWFgasrO6ZNm4Z3330XTk5OePDgAUJDQ3Hw4EHs2bOn8JMIeu2dO3dOdOzYUdja2gqlUilcXFzEqFGjxL///mvo0sqM4OBgAaDAF+nz8fEp8H06cOCAoUszqO+//144OTkJMzMz8dZbb4k///zT0CWVKQcOHCjwc+Pj42Po0sqMp/0bFBwcbOjSyhRfX1/h7OwszMzMhJ2dnejUqZPYu3dvkebgOUdEREREMq/PSRNEREREhcBwRERERCTDcEREREQkw3BEREREJMNwRERERCTDcEREREQkw3BEREREJMNwRERERCTDcEREr7T33nsPXbt2LbDvyJEjUCgUOHfuHMaNG4dmzZpBqVTizTffLN0iiahMYTgiolean58fwsPD8e+//+brCw4ORvPmzdGoUSMAgK+vL/r161faJRJRGcNwRESvtO7du8POzg4hISF67enp6QgLC4Ofnx8AYMmSJfD390eNGjUMUCURlSUMR0T0SjMxMcHgwYMREhIC+aMkw8LCkJubiw8//NCA1RFRWcRwRESvPF9fX8TGxuLQoUNSW3BwMLy9vaFWqw1YGRGVRQxHRPTKq1evHlq3bo3Vq1cDAK5evYojR45Ih9SIiOQYjojoteDn54ctW7bgwYMHCA4ORs2aNdG+fXtDl0VEZRDDERG9Fvr27QsjIyOEhoZi7dq18PX1hUKhMHRZRFQGmRi6ACKi0mBlZYV+/fph2rRpSEtLw5AhQ/T6r169ivT0dCQkJODRo0eIjo4GALi5ucHMzKz0CyYig1EI+eUbRESvsMjISLRu3RrdunXDrl279Po6dOigd8J2nri4OLi4uJRShURUFjAcEREREcnwnCMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiIZhiMiIiIiGYYjIiIiIhmGIyIiIiKZ/we1IbwWzq8kbAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "6qKkNyX9JvlF"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pp2CXYcjJwSN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XkQaIKrbJwW1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CnBbKChBJwbG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "0xyqOnSoJwrl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 9: Train Classifier on Combined Dataset\n",
        "- Combine real and synthetic data to train a classifier.\n",
        "- Use the classifier to predict outcomes and enhance the model’s generalization."
      ],
      "metadata": {
        "id": "FOAvKpjoQJDG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_real = data.drop(columns=['Class']).values"
      ],
      "metadata": {
        "id": "-6se-Aq7Jxa-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_real = data['Class'].values"
      ],
      "metadata": {
        "id": "IrLb0OaRJxj0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_combined = np.vstack([X_real, synthetic_data])"
      ],
      "metadata": {
        "id": "JYyVdVNgJxon"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_combined = np.hstack([y_real, np.random.choice(y_real, synthetic_samples)])"
      ],
      "metadata": {
        "id": "8QE87Y2VJZ70"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_combined, y_combined, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "o2m6TrQcJZ_q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "sPXiK6RkJaDh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "UYmIYXr2KA77",
        "outputId": "0b495e38-7134-4072-8c28-d64c7d7e637b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "-1Yrd8HAKBDh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "XkuwOwyLQazK"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H652R77EQb7v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "e4kHy2rvKBIh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VrKQ3XbYKBNJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 1px solid purple;\">"
      ],
      "metadata": {
        "id": "K6AnMiCJQSrp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 10: Evaluation\n",
        "- Evaluate the classifier’s performance on the test set.\n",
        "- Check accuracy, precision, recall, and F1-score to measure the model’s effectiveness."
      ],
      "metadata": {
        "id": "FmluraI6QPzr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Classifier"
      ],
      "metadata": {
        "id": "NrlLHF6CKBSI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NacvObIUKBWh",
        "outputId": "8efacebb-b3a3-432e-8223-e74d448450ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9999165519255643\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2XucYlEGKMqY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83SZWfi3KNO4",
        "outputId": "0b68dae3-c83f-46cb-9794-66eacaecfc20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     23965\n",
            "           1       0.00      0.00      0.00         2\n",
            "\n",
            "    accuracy                           1.00     23967\n",
            "   macro avg       0.50      0.50      0.50     23967\n",
            "weighted avg       1.00      1.00      1.00     23967\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr style=\"border: 2px solid purple;\">\n",
        "<hr style=\"border: 2px solid purple;\">"
      ],
      "metadata": {
        "id": "Yg4ENBo3QVAW"
      }
    }
  ]
}