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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing Required Libraries**"
      ],
      "metadata": {
        "id": "waMNi935zNBQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "uFX5JySJzFoH"
      },
      "outputs": [],
      "source": [
        "# Importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.inspection import permutation_importance\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Loading the Dataset**"
      ],
      "metadata": {
        "id": "kk_tnuC3z5u6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "data = pd.read_csv('/content/fgsm_combined.csv')"
      ],
      "metadata": {
        "id": "2kJ7npSnzIIc"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the first few rows of the dataset\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sgZuXILBzIK-",
        "outputId": "02225b2a-04ed-4976-8e88-4895222e7b0c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        mDt        mA       mQ           vDt            vA           vQ  \\\n",
              "0  0.360856  248.8398  87.7588      6.663187  30442.801336    71.624223   \n",
              "1  0.531202  253.7944  88.4112      9.800575  29874.502529  1289.189315   \n",
              "2  1.782531  242.6194  87.5246  11173.371567  26826.760544   886.010995   \n",
              "3  0.406041  240.8434  89.1816      7.026983  27719.288876  1288.858621   \n",
              "4  0.249315  249.3014  88.2282      9.445476  27890.219000   497.916020   \n",
              "\n",
              "         sDt        sA         sQ          kDt        kA             kQ  g  \\\n",
              "0   8.834394  2.066294   0.496486    79.484593  4.008716      -2.286133  0   \n",
              "1   7.049183  1.826051  37.559813    50.049128  2.747029      -2.996934  0   \n",
              "2  70.643651  2.040208  44.030430  4990.675072  3.896140  794161.629392  0   \n",
              "3   8.497210  1.930113  37.509779    73.791058  3.379000      -2.997717  0   \n",
              "4   6.977533  1.813368  51.995506    54.314777  4.868793      -2.678185  1   \n",
              "\n",
              "   attack  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       1  "
            ],
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              "      <th></th>\n",
              "      <th>mDt</th>\n",
              "      <th>mA</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.360856</td>\n",
              "      <td>248.8398</td>\n",
              "      <td>87.7588</td>\n",
              "      <td>6.663187</td>\n",
              "      <td>30442.801336</td>\n",
              "      <td>71.624223</td>\n",
              "      <td>8.834394</td>\n",
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              "      <th>1</th>\n",
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              "      <td>253.7944</td>\n",
              "      <td>88.4112</td>\n",
              "      <td>9.800575</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.782531</td>\n",
              "      <td>242.6194</td>\n",
              "      <td>87.5246</td>\n",
              "      <td>11173.371567</td>\n",
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              "      <td>886.010995</td>\n",
              "      <td>70.643651</td>\n",
              "      <td>2.040208</td>\n",
              "      <td>44.030430</td>\n",
              "      <td>4990.675072</td>\n",
              "      <td>3.896140</td>\n",
              "      <td>794161.629392</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.406041</td>\n",
              "      <td>240.8434</td>\n",
              "      <td>89.1816</td>\n",
              "      <td>7.026983</td>\n",
              "      <td>27719.288876</td>\n",
              "      <td>1288.858621</td>\n",
              "      <td>8.497210</td>\n",
              "      <td>1.930113</td>\n",
              "      <td>37.509779</td>\n",
              "      <td>73.791058</td>\n",
              "      <td>3.379000</td>\n",
              "      <td>-2.997717</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.249315</td>\n",
              "      <td>249.3014</td>\n",
              "      <td>88.2282</td>\n",
              "      <td>9.445476</td>\n",
              "      <td>27890.219000</td>\n",
              "      <td>497.916020</td>\n",
              "      <td>6.977533</td>\n",
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              "      <td>51.995506</td>\n",
              "      <td>54.314777</td>\n",
              "      <td>4.868793</td>\n",
              "      <td>-2.678185</td>\n",
              "      <td>1</td>\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5999,\n  \"fields\": [\n    {\n      \"column\": \"mDt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5341705276643537,\n        \"min\": -0.054406,\n        \"max\": 2.794139,\n        \"num_unique_values\": 4914,\n        \"samples\": [\n          0.437256,\n          0.129,\n          0.351127\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.481916179156103,\n        \"min\": 236.053,\n        \"max\": 302.39282,\n        \"num_unique_values\": 5758,\n        \"samples\": [\n          242.6098,\n          247.735,\n          284.8436\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7936108445069919,\n        \"min\": 86.093,\n        \"max\": 90.0264,\n        \"num_unique_values\": 4534,\n        \"samples\": [\n          87.0424,\n          86.7206,\n          89.019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vDt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5134.729014056887,\n        \"min\": 3.025636,\n        \"max\": 25994.494057,\n        \"num_unique_values\": 5511,\n        \"samples\": [\n          4171.946,\n          10.256436,\n          10.032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3936.2328792288677,\n        \"min\": 23919.525,\n        \"max\": 45217.273,\n        \"num_unique_values\": 5859,\n        \"samples\": [\n          32111.305913,\n          30686.715,\n          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 \"std\": 0.24514262458714578,\n        \"min\": 1.036581,\n        \"max\": 2.665,\n        \"num_unique_values\": 4896,\n        \"samples\": [\n          1.691241,\n          1.797598,\n          1.542\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.570890359119023,\n        \"min\": -0.149014,\n        \"max\": 59.22274,\n        \"num_unique_values\": 5683,\n        \"samples\": [\n          1.119022,\n          39.433,\n          57.232254\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kDt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1823.1690852514485,\n        \"min\": 25.644376,\n        \"max\": 4992.658,\n        \"num_unique_values\": 5657,\n        \"samples\": [\n          57.345165,\n          56.69,\n          4978.4614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3293783128102432,\n        \"min\": 0.467,\n        \"max\": 7.417582,\n        \"num_unique_values\": 5561,\n        \"samples\": [\n          3.623033,\n          2.814,\n          4.701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66704959.21084197,\n        \"min\": -3.3,\n        \"max\": 671738700.0,\n        \"num_unique_values\": 4697,\n        \"samples\": [\n          223.33479,\n          -2.682949,\n          -2.746\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attack\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Exploration & Visualization**"
      ],
      "metadata": {
        "id": "paiGGEmG4BO3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KGcGny-5gc1",
        "outputId": "9e05dc4e-05af-445b-d597-29334c3c8421"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5999 entries, 0 to 5998\n",
            "Data columns (total 14 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   mDt     5999 non-null   float64\n",
            " 1   mA      5999 non-null   float64\n",
            " 2   mQ      5999 non-null   float64\n",
            " 3   vDt     5999 non-null   float64\n",
            " 4   vA      5999 non-null   float64\n",
            " 5   vQ      5999 non-null   float64\n",
            " 6   sDt     5999 non-null   float64\n",
            " 7   sA      5999 non-null   float64\n",
            " 8   sQ      5999 non-null   float64\n",
            " 9   kDt     5999 non-null   float64\n",
            " 10  kA      5999 non-null   float64\n",
            " 11  kQ      5999 non-null   float64\n",
            " 12  g       5999 non-null   int64  \n",
            " 13  attack  5999 non-null   int64  \n",
            "dtypes: float64(12), int64(2)\n",
            "memory usage: 656.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "cp5edb4Z5iIr",
        "outputId": "ac1c5130-bf01-44d5-b461-249dad033b13"
      },
      "execution_count": 5,
      "outputs": [
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          "data": {
            "text/plain": [
              "               mDt           mA           mQ           vDt            vA  \\\n",
              "count  5999.000000  5999.000000  5999.000000   5999.000000   5999.000000   \n",
              "mean      0.592374   261.689582    87.870095   1435.235298  32290.103727   \n",
              "std       0.534171    13.481916     0.793611   5134.729014   3936.232879   \n",
              "min      -0.054406   236.053000    86.093000      3.025636  23919.525000   \n",
              "25%       0.292000   252.110600    87.233800      7.168168  29315.173000   \n",
              "50%       0.481113   260.481000    87.810600      9.127226  32027.189568   \n",
              "75%       0.684334   268.819990    88.433400     13.106241  35043.197624   \n",
              "max       2.794139   302.392820    90.026400  25994.494057  45217.273000   \n",
              "\n",
              "                vQ          sDt           sA           sQ          kDt  \\\n",
              "count  5999.000000  5999.000000  5999.000000  5999.000000  5999.000000   \n",
              "mean    754.113751    19.318958     1.796075    29.605885   958.250483   \n",
              "std     721.848086    23.560225     0.245143    21.570890  1823.169085   \n",
              "min      50.750000     4.835799     1.036581    -0.149014    25.644376   \n",
              "25%      72.008023     7.140055     1.643984     0.867098    53.493665   \n",
              "50%     480.171651     8.059316     1.807203    37.162071    65.954690   \n",
              "75%    1281.729600     9.456667     1.969000    44.685058    89.741900   \n",
              "max    2919.690000    70.665000     2.665000    59.222740  4992.658000   \n",
              "\n",
              "                kA            kQ            g       attack  \n",
              "count  5999.000000  5.999000e+03  5999.000000  5999.000000  \n",
              "mean      3.399353  8.492864e+06     0.250042     0.500083  \n",
              "std       1.329378  6.670496e+07     0.433073     0.500042  \n",
              "min       0.467000 -3.300000e+00     0.000000     0.000000  \n",
              "25%       2.476828 -2.992000e+00     0.000000     0.000000  \n",
              "50%       3.322000 -2.686019e+00     0.000000     1.000000  \n",
              "75%       4.314688 -1.453289e+00     0.500000     1.000000  \n",
              "max       7.417582  6.717387e+08     1.000000     1.000000  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mDt</th>\n",
              "      <th>mA</th>\n",
              "      <th>mQ</th>\n",
              "      <th>vDt</th>\n",
              "      <th>vA</th>\n",
              "      <th>vQ</th>\n",
              "      <th>sDt</th>\n",
              "      <th>sA</th>\n",
              "      <th>sQ</th>\n",
              "      <th>kDt</th>\n",
              "      <th>kA</th>\n",
              "      <th>kQ</th>\n",
              "      <th>g</th>\n",
              "      <th>attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5.999000e+03</td>\n",
              "      <td>5999.000000</td>\n",
              "      <td>5999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.592374</td>\n",
              "      <td>261.689582</td>\n",
              "      <td>87.870095</td>\n",
              "      <td>1435.235298</td>\n",
              "      <td>32290.103727</td>\n",
              "      <td>754.113751</td>\n",
              "      <td>19.318958</td>\n",
              "      <td>1.796075</td>\n",
              "      <td>29.605885</td>\n",
              "      <td>958.250483</td>\n",
              "      <td>3.399353</td>\n",
              "      <td>8.492864e+06</td>\n",
              "      <td>0.250042</td>\n",
              "      <td>0.500083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.534171</td>\n",
              "      <td>13.481916</td>\n",
              "      <td>0.793611</td>\n",
              "      <td>5134.729014</td>\n",
              "      <td>3936.232879</td>\n",
              "      <td>721.848086</td>\n",
              "      <td>23.560225</td>\n",
              "      <td>0.245143</td>\n",
              "      <td>21.570890</td>\n",
              "      <td>1823.169085</td>\n",
              "      <td>1.329378</td>\n",
              "      <td>6.670496e+07</td>\n",
              "      <td>0.433073</td>\n",
              "      <td>0.500042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.054406</td>\n",
              "      <td>236.053000</td>\n",
              "      <td>86.093000</td>\n",
              "      <td>3.025636</td>\n",
              "      <td>23919.525000</td>\n",
              "      <td>50.750000</td>\n",
              "      <td>4.835799</td>\n",
              "      <td>1.036581</td>\n",
              "      <td>-0.149014</td>\n",
              "      <td>25.644376</td>\n",
              "      <td>0.467000</td>\n",
              "      <td>-3.300000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.292000</td>\n",
              "      <td>252.110600</td>\n",
              "      <td>87.233800</td>\n",
              "      <td>7.168168</td>\n",
              "      <td>29315.173000</td>\n",
              "      <td>72.008023</td>\n",
              "      <td>7.140055</td>\n",
              "      <td>1.643984</td>\n",
              "      <td>0.867098</td>\n",
              "      <td>53.493665</td>\n",
              "      <td>2.476828</td>\n",
              "      <td>-2.992000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.481113</td>\n",
              "      <td>260.481000</td>\n",
              "      <td>87.810600</td>\n",
              "      <td>9.127226</td>\n",
              "      <td>32027.189568</td>\n",
              "      <td>480.171651</td>\n",
              "      <td>8.059316</td>\n",
              "      <td>1.807203</td>\n",
              "      <td>37.162071</td>\n",
              "      <td>65.954690</td>\n",
              "      <td>3.322000</td>\n",
              "      <td>-2.686019e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.684334</td>\n",
              "      <td>268.819990</td>\n",
              "      <td>88.433400</td>\n",
              "      <td>13.106241</td>\n",
              "      <td>35043.197624</td>\n",
              "      <td>1281.729600</td>\n",
              "      <td>9.456667</td>\n",
              "      <td>1.969000</td>\n",
              "      <td>44.685058</td>\n",
              "      <td>89.741900</td>\n",
              "      <td>4.314688</td>\n",
              "      <td>-1.453289e+00</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.794139</td>\n",
              "      <td>302.392820</td>\n",
              "      <td>90.026400</td>\n",
              "      <td>25994.494057</td>\n",
              "      <td>45217.273000</td>\n",
              "      <td>2919.690000</td>\n",
              "      <td>70.665000</td>\n",
              "      <td>2.665000</td>\n",
              "      <td>59.222740</td>\n",
              "      <td>4992.658000</td>\n",
              "      <td>7.417582</td>\n",
              "      <td>6.717387e+08</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "H4TUDN4Q5mBz",
        "outputId": "3ae83af8-691d-4d1e-fb18-7f8c016dec54"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mDt       0\n",
              "mA        0\n",
              "mQ        0\n",
              "vDt       0\n",
              "vA        0\n",
              "vQ        0\n",
              "sDt       0\n",
              "sA        0\n",
              "sQ        0\n",
              "kDt       0\n",
              "kA        0\n",
              "kQ        0\n",
              "g         0\n",
              "attack    0\n",
              "dtype: int64"
            ],
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              "      <th>0</th>\n",
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              "  <tbody>\n",
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              "      <th>mDt</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>kA</th>\n",
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              "      <th>kQ</th>\n",
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              "      <th>g</th>\n",
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              "      <th>attack</th>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot to analyze the distribution of the feature 'mA' by 'g'\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='g', y='mA', data=data)\n",
        "plt.title('Boxplot of mA by Target Variable (g)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "t2-RlYjFzIPe",
        "outputId": "614d580c-cd75-4497-b5b8-cf9d3f7fbb09"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix\n",
        "plt.figure(figsize=(14, 10))\n",
        "correlation_matrix = data.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='icefire', fmt='.2f')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 853
        },
        "id": "VKk2rpg-zISH",
        "outputId": "c45b0c9b-f901-4077-aaf2-99d6e741f349"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preparation (Cleaning and Transformation)**"
      ],
      "metadata": {
        "id": "oFAHCLcK5QLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split features and target variable\n",
        "X = data.drop('attack', axis=1)  # Features\n",
        "y = data['attack']               # Target variable"
      ],
      "metadata": {
        "id": "pi-D__rFzIWk"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "QLlLMOFkzIZD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "Z6EZ2RO6zIcI"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Train and Evaluate Random Forest Model**"
      ],
      "metadata": {
        "id": "ZwbXwc8S6gs7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Random Forest Classifier\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "_SZqFS7vzIeS",
        "outputId": "1a424d34-8fa7-42c5-8efc-b02bf3b9a869"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\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: 0.5em;\n",
              "  text-align: center;\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(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions and Evaluation\n",
        "rf_predictions = rf_model.predict(X_test_scaled)\n",
        "rf_accuracy = accuracy_score(y_test, rf_predictions)"
      ],
      "metadata": {
        "id": "amlp1qk3zIhH"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report for Random Forest\n",
        "print(f\"Random Forest Accuracy: {rf_accuracy:.4f}\")\n",
        "print(\"\\nRandom Forest Classification Report:\\n\", classification_report(y_test, rf_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diJ9vtspzIja",
        "outputId": "e1328e94-bf1d-4d21-e137-2cab681105e0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.9733\n",
            "\n",
            "Random Forest Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.97       595\n",
            "           1       0.98      0.97      0.97       605\n",
            "\n",
            "    accuracy                           0.97      1200\n",
            "   macro avg       0.97      0.97      0.97      1200\n",
            "weighted avg       0.97      0.97      0.97      1200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix for Random Forest\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(confusion_matrix(y_test, rf_predictions), annot=True, fmt='d', cmap='PuRd')\n",
        "plt.title('Random Forest Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "DmnzeH9EzImI",
        "outputId": "10f7698c-bdad-477c-c5fe-f1ca005847e8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Train and Evaluate Decision Tree Model**"
      ],
      "metadata": {
        "id": "S9PLJfbM8Jo-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Decision Tree Classifier\n",
        "dt_model = DecisionTreeClassifier(random_state=42)\n",
        "dt_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "GV2io0cszIo-",
        "outputId": "b4ad9c2c-c87f-420b-d058-6dc720609b78"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 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-2 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-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 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-2 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-2 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-2 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-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 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-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 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: 0.5em;\n",
              "  text-align: center;\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-2 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-2 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-2 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-2 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-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions and Evaluation\n",
        "dt_predictions = dt_model.predict(X_test_scaled)\n",
        "dt_accuracy = accuracy_score(y_test, dt_predictions)"
      ],
      "metadata": {
        "id": "HRY3Mlq8zIsM"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report for Decision Tree\n",
        "print(f\"Decision Tree Accuracy: {dt_accuracy:.4f}\")\n",
        "print(\"\\nDecision Tree Classification Report:\\n\", classification_report(y_test, dt_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rY4VlhWazIu0",
        "outputId": "c4313b73-4959-4245-93ce-59ab27bd0035"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy: 0.9683\n",
            "\n",
            "Decision Tree Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.97      0.97       595\n",
            "           1       0.97      0.96      0.97       605\n",
            "\n",
            "    accuracy                           0.97      1200\n",
            "   macro avg       0.97      0.97      0.97      1200\n",
            "weighted avg       0.97      0.97      0.97      1200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix for Decision Tree\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(confusion_matrix(y_test, dt_predictions), annot=True, fmt='d', cmap='BuGn')\n",
        "plt.title('Decision Tree Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "n41ISw8N8TI4",
        "outputId": "07e7b4d1-f117-4836-84d5-61fa14b70168"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Train and Evaluate XGBoost Model**"
      ],
      "metadata": {
        "id": "3gPDzH3y8fii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the XGBoost Classifier\n",
        "xgb_model = XGBClassifier(n_estimators=100, random_state=42)\n",
        "xgb_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "5ufXP9oH8Tli",
        "outputId": "b11efa01-97e3-4bee-9a98-912288a8cbfb"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              feature_weights=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
              "              n_jobs=None, num_parallel_tree=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 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-3 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-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 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-3 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-3 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-3 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-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 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-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 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: 0.5em;\n",
              "  text-align: center;\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-3 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-3 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-3 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-3 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-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              feature_weights=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
              "              n_jobs=None, num_parallel_tree=None, ...)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://xgboost.readthedocs.io/en/release_3.2.0/python/python_api.html#xgboost.XGBClassifier\">?<span>Documentation for XGBClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              feature_weights=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=100,\n",
              "              n_jobs=None, num_parallel_tree=None, ...)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions and Evaluation\n",
        "xgb_predictions = xgb_model.predict(X_test_scaled)\n",
        "xgb_accuracy = accuracy_score(y_test, xgb_predictions)"
      ],
      "metadata": {
        "id": "Y48-mOCM8ga6"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report for XGBoost\n",
        "print(f\"XGBoost Accuracy: {xgb_accuracy:.4f}\")\n",
        "print(\"\\nXGBoost Classification Report:\\n\", classification_report(y_test, xgb_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85UaoyHB8gdW",
        "outputId": "6c16293e-aa14-4687-81bb-7a950800321d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Accuracy: 0.9742\n",
            "\n",
            "XGBoost Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.98      0.97       595\n",
            "           1       0.98      0.97      0.97       605\n",
            "\n",
            "    accuracy                           0.97      1200\n",
            "   macro avg       0.97      0.97      0.97      1200\n",
            "weighted avg       0.97      0.97      0.97      1200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix for XGBoost\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(confusion_matrix(y_test, xgb_predictions), annot=True, fmt='d', cmap='Oranges')\n",
        "plt.title('XGBoost Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "SKaOalOQ8gfa",
        "outputId": "9cea794e-84d8-4e10-c329-cbc09dc6d72a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Mitigation with Data Sanitization**"
      ],
      "metadata": {
        "id": "Eb9Zvmov8xfV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Sanitization and Poisoning Attack Simulation\n",
        "\n",
        "# Label Flipping Attack (simulating poisoning attack)\n",
        "X_poisoned = X.copy()\n",
        "y_poisoned = y.copy()\n",
        "\n",
        "# Flip the labels for 20% of the samples randomly\n",
        "flip_percentage = 0.2\n",
        "num_flips = int(len(y_poisoned) * flip_percentage)\n",
        "flip_indices = np.random.choice(len(y_poisoned), size=num_flips, replace=False)\n",
        "y_poisoned.iloc[flip_indices] = 1 - y_poisoned.iloc[flip_indices]  # Flip the labels"
      ],
      "metadata": {
        "id": "1QY-M9Fz8gh6"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model on poisoned data\n",
        "X_train_poisoned, X_test_poisoned, y_train_poisoned, y_test_poisoned = train_test_split(X_poisoned, y_poisoned, test_size=0.2, random_state=42)\n",
        "X_train_poisoned_scaled = scaler.fit_transform(X_train_poisoned)\n",
        "X_test_poisoned_scaled = scaler.transform(X_test_poisoned)"
      ],
      "metadata": {
        "id": "vZBmltb88gjr"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Rebuild the model (Random Forest)\n",
        "rf_model_poisoned = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model_poisoned.fit(X_train_poisoned_scaled, y_train_poisoned)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Tf_vbT-X8gl9",
        "outputId": "e5ca1ad0-8e29-444e-cf0c-f72bdf80aee0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 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-4 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-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 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-4 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-4 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-4 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-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 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-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 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: 0.5em;\n",
              "  text-align: center;\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-4 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-4 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-4 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-4 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-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model with poisoned data\n",
        "rf_poisoned_predictions = rf_model_poisoned.predict(X_test_poisoned_scaled)\n",
        "rf_poisoned_accuracy = accuracy_score(y_test_poisoned, rf_poisoned_predictions)\n",
        "\n",
        "print(f\"Random Forest Accuracy after Label Flipping Attack: {rf_poisoned_accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ovnxu_vR8goJ",
        "outputId": "b6d02db4-92a4-471d-8a2f-9ea83bd74172"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy after Label Flipping Attack: 0.7333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply Data Sanitization\n",
        "\n",
        "# Simulate simple \"sanitization\" by removing the flipped labels\n",
        "sanitized_y = y_poisoned.copy()\n",
        "sanitized_y[flip_indices] = y[flip_indices]  # Restore the original labels"
      ],
      "metadata": {
        "id": "GdXuP4Ic8gqL"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Re-train with sanitized data\n",
        "rf_model_sanitized = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_model_sanitized.fit(X_train_poisoned_scaled, sanitized_y.loc[y_train_poisoned.index])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "Get64mNO8gsT",
        "outputId": "03c3e707-77ca-4a07-e7e0-5af4b8884d75"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\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-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 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-5 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-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 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-5 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-5 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-5 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-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 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-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 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: 0.5em;\n",
              "  text-align: center;\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-5 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-5 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-5 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-5 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-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation of Sanitized Model\n",
        "rf_sanitized_predictions = rf_model_sanitized.predict(X_test_poisoned_scaled)\n",
        "rf_sanitized_accuracy = accuracy_score(y_test_poisoned, rf_sanitized_predictions)\n",
        "\n",
        "print(f\"Random Forest Accuracy after Sanitization: {rf_sanitized_accuracy:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uHYQrw0i8gu1",
        "outputId": "8a494b26-f5c7-4bee-c325-a866f48f5487"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy after Sanitization: 0.7917\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Comparison of Model Performance**"
      ],
      "metadata": {
        "id": "36wF1oprAFoD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the results to compare model performance\n",
        "\n",
        "# Data for plotting\n",
        "methods = ['Original Data', 'Poisoned Data', 'Sanitized Data']\n",
        "accuracies = [\n",
        "    rf_accuracy,                    # Accuracy with original data\n",
        "    rf_poisoned_accuracy,           # Accuracy with poisoned data\n",
        "    rf_sanitized_accuracy           # Accuracy after sanitization\n",
        "]\n",
        "\n",
        "# Plotting the results\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.barplot(x=methods, y=accuracies, palette='husl')\n",
        "\n",
        "# Add title and labels\n",
        "plt.title('Comparison of Model Accuracy: Data Poisoning and Sanitization')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.xlabel('Data Type')\n",
        "plt.ylim(0, 1)  # Limit the y-axis to [0, 1] for accuracy values\n",
        "\n",
        "# Add percentage labels on top of the bars\n",
        "for i, accuracy in enumerate(accuracies):\n",
        "    plt.text(i, accuracy + 0.02, f'{accuracy * 100:.2f}%', ha='center', fontsize=12)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "hHB7ZbKY_Rqh",
        "outputId": "ff3cc494-1a3b-45e5-ba98-66eda74d2d71"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Performance vs. Data Poisoning Severity (Simulated Label Flipping)**"
      ],
      "metadata": {
        "id": "ukA-36Y-ABvp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize performance degradation as label flipping percentage increases\n",
        "\n",
        "# Vary the poisoning severity (percentage of flipped labels)\n",
        "flip_percentages = np.linspace(0, 0.5, 11)  # Vary from 0% to 50%\n",
        "accuracies_poisoning = []\n",
        "\n",
        "# Test the model with different levels of label flipping\n",
        "for flip_percentage in flip_percentages:\n",
        "    # Flip labels for a given percentage\n",
        "    num_flips = int(len(y_poisoned) * flip_percentage)\n",
        "    flip_indices = np.random.choice(len(y_poisoned), size=num_flips, replace=False)\n",
        "    y_poisoned.iloc[flip_indices] = 1 - y_poisoned.iloc[flip_indices]  # Flip the labels\n",
        "\n",
        "    # Re-train and test the model\n",
        "    X_train_poisoned, X_test_poisoned, y_train_poisoned, y_test_poisoned = train_test_split(X_poisoned, y_poisoned, test_size=0.2, random_state=42)\n",
        "    X_train_poisoned_scaled = scaler.fit_transform(X_train_poisoned)\n",
        "    X_test_poisoned_scaled = scaler.transform(X_test_poisoned)\n",
        "\n",
        "    # Train and evaluate model on poisoned data\n",
        "    rf_model_poisoned.fit(X_train_poisoned_scaled, y_train_poisoned)\n",
        "    rf_poisoned_predictions = rf_model_poisoned.predict(X_test_poisoned_scaled)\n",
        "    accuracies_poisoning.append(accuracy_score(y_test_poisoned, rf_poisoned_predictions))\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(flip_percentages * 100, accuracies_poisoning, marker='o', color='r', linestyle='-', linewidth=2)\n",
        "plt.title('Impact of Label Flipping on Model Accuracy')\n",
        "plt.xlabel('Percentage of Label Flipping')\n",
        "plt.ylabel('Model Accuracy')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "acbjrUVm_TEd",
        "outputId": "b419c82b-74fa-401b-a66c-e7d76c7073c6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}