EnsembleModel_Threat_Detection.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Threat detection Model- Ensembling LSTM and ANN"
      ],
      "metadata": {
        "id": "DPcM5ISCKnf_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading Libraries"
      ],
      "metadata": {
        "id": "Fzqp2mr5Km6e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas.api.types import is_numeric_dtype\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_curve, auc, roc_auc_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import RFE\n",
        "import itertools\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from tensorflow.keras.models import Sequential, load_model\n",
        "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization, ReLU, LSTM\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import BinaryCrossentropy\n",
        "from google.colab import drive\n",
        "import os\n",
        "import joblib"
      ],
      "metadata": {
        "id": "9hA-WJtprsPd"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZGtWpvrX5aO",
        "outputId": "6d210f09-70f3-4593-dfb7-aa2226666b7e"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading Dataset"
      ],
      "metadata": {
        "id": "HRZkyyhqK8A1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "ds = pd.read_csv(\"/content/dataset.csv\")"
      ],
      "metadata": {
        "id": "VkvM4CXRx_Mc"
      },
      "execution_count": 47,
      "outputs": []
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      "source": [
        "ds.info()"
      ],
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        "outputId": "d79dc851-b72f-499d-89e0-f25223762c2b"
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      "execution_count": 48,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 25192 entries, 0 to 25191\n",
            "Data columns (total 42 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   duration                     25192 non-null  int64  \n",
            " 1   protocol_type                25192 non-null  object \n",
            " 2   service                      25192 non-null  object \n",
            " 3   flag                         25192 non-null  object \n",
            " 4   src_bytes                    25192 non-null  int64  \n",
            " 5   dst_bytes                    25192 non-null  int64  \n",
            " 6   land                         25192 non-null  int64  \n",
            " 7   wrong_fragment               25192 non-null  int64  \n",
            " 8   urgent                       25192 non-null  int64  \n",
            " 9   hot                          25192 non-null  int64  \n",
            " 10  num_failed_logins            25192 non-null  int64  \n",
            " 11  logged_in                    25192 non-null  int64  \n",
            " 12  num_compromised              25192 non-null  int64  \n",
            " 13  root_shell                   25192 non-null  int64  \n",
            " 14  su_attempted                 25192 non-null  int64  \n",
            " 15  num_root                     25192 non-null  int64  \n",
            " 16  num_file_creations           25192 non-null  int64  \n",
            " 17  num_shells                   25192 non-null  int64  \n",
            " 18  num_access_files             25192 non-null  int64  \n",
            " 19  num_outbound_cmds            25192 non-null  int64  \n",
            " 20  is_host_login                25192 non-null  int64  \n",
            " 21  is_guest_login               25192 non-null  int64  \n",
            " 22  count                        25192 non-null  int64  \n",
            " 23  srv_count                    25192 non-null  int64  \n",
            " 24  serror_rate                  25192 non-null  float64\n",
            " 25  srv_serror_rate              25192 non-null  float64\n",
            " 26  rerror_rate                  25192 non-null  float64\n",
            " 27  srv_rerror_rate              25192 non-null  float64\n",
            " 28  same_srv_rate                25192 non-null  float64\n",
            " 29  diff_srv_rate                25192 non-null  float64\n",
            " 30  srv_diff_host_rate           25192 non-null  float64\n",
            " 31  dst_host_count               25192 non-null  int64  \n",
            " 32  dst_host_srv_count           25192 non-null  int64  \n",
            " 33  dst_host_same_srv_rate       25192 non-null  float64\n",
            " 34  dst_host_diff_srv_rate       25192 non-null  float64\n",
            " 35  dst_host_same_src_port_rate  25192 non-null  float64\n",
            " 36  dst_host_srv_diff_host_rate  25192 non-null  float64\n",
            " 37  dst_host_serror_rate         25192 non-null  float64\n",
            " 38  dst_host_srv_serror_rate     25192 non-null  float64\n",
            " 39  dst_host_rerror_rate         25192 non-null  float64\n",
            " 40  dst_host_srv_rerror_rate     25192 non-null  float64\n",
            " 41  class                        25192 non-null  object \n",
            "dtypes: float64(15), int64(23), object(4)\n",
            "memory usage: 8.1+ MB\n"
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              "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
              "0         0           tcp  ftp_data   SF        491          0     0   \n",
              "1         0           udp     other   SF        146          0     0   \n",
              "2         0           tcp   private   S0          0          0     0   \n",
              "3         0           tcp      http   SF        232       8153     0   \n",
              "4         0           tcp      http   SF        199        420     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0               0       0    0  ...                  25   \n",
              "1               0       0    0  ...                   1   \n",
              "2               0       0    0  ...                  26   \n",
              "3               0       0    0  ...                 255   \n",
              "4               0       0    0  ...                 255   \n",
              "\n",
              "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                    0.17                    0.03   \n",
              "1                    0.00                    0.60   \n",
              "2                    0.10                    0.05   \n",
              "3                    1.00                    0.00   \n",
              "4                    1.00                    0.00   \n",
              "\n",
              "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                         0.17                         0.00   \n",
              "1                         0.88                         0.00   \n",
              "2                         0.00                         0.00   \n",
              "3                         0.03                         0.04   \n",
              "4                         0.00                         0.00   \n",
              "\n",
              "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                  0.00                      0.00                  0.05   \n",
              "1                  0.00                      0.00                  0.00   \n",
              "2                  1.00                      1.00                  0.00   \n",
              "3                  0.03                      0.01                  0.00   \n",
              "4                  0.00                      0.00                  0.00   \n",
              "\n",
              "   dst_host_srv_rerror_rate    class  \n",
              "0                      0.00   normal  \n",
              "1                      0.00   normal  \n",
              "2                      0.00  anomaly  \n",
              "3                      0.01   normal  \n",
              "4                      0.00   normal  \n",
              "\n",
              "[5 rows x 42 columns]"
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5e681e39-dfde-4765-96bb-abe50477b0f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ds"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "OTlalo0nMCUh",
        "outputId": "76bf3888-25b7-4525-dc7d-72b69fd8e439"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           duration     src_bytes     dst_bytes          land  wrong_fragment  \\\n",
              "count  25192.000000  2.519200e+04  2.519200e+04  25192.000000    25192.000000   \n",
              "mean     305.054104  2.433063e+04  3.491847e+03      0.000079        0.023738   \n",
              "std     2686.555640  2.410805e+06  8.883072e+04      0.008910        0.260221   \n",
              "min        0.000000  0.000000e+00  0.000000e+00      0.000000        0.000000   \n",
              "25%        0.000000  0.000000e+00  0.000000e+00      0.000000        0.000000   \n",
              "50%        0.000000  4.400000e+01  0.000000e+00      0.000000        0.000000   \n",
              "75%        0.000000  2.790000e+02  5.302500e+02      0.000000        0.000000   \n",
              "max    42862.000000  3.817091e+08  5.151385e+06      1.000000        3.000000   \n",
              "\n",
              "            urgent           hot  num_failed_logins     logged_in  \\\n",
              "count  25192.00000  25192.000000       25192.000000  25192.000000   \n",
              "mean       0.00004      0.198039           0.001191      0.394768   \n",
              "std        0.00630      2.154202           0.045418      0.488811   \n",
              "min        0.00000      0.000000           0.000000      0.000000   \n",
              "25%        0.00000      0.000000           0.000000      0.000000   \n",
              "50%        0.00000      0.000000           0.000000      0.000000   \n",
              "75%        0.00000      0.000000           0.000000      1.000000   \n",
              "max        1.00000     77.000000           4.000000      1.000000   \n",
              "\n",
              "       num_compromised  ...  dst_host_count  dst_host_srv_count  \\\n",
              "count     25192.000000  ...    25192.000000        25192.000000   \n",
              "mean          0.227850  ...      182.532074          115.063036   \n",
              "std          10.417352  ...       98.993895          110.646850   \n",
              "min           0.000000  ...        0.000000            0.000000   \n",
              "25%           0.000000  ...       84.000000           10.000000   \n",
              "50%           0.000000  ...      255.000000           61.000000   \n",
              "75%           0.000000  ...      255.000000          255.000000   \n",
              "max         884.000000  ...      255.000000          255.000000   \n",
              "\n",
              "       dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "count            25192.000000            25192.000000   \n",
              "mean                 0.519791                0.082539   \n",
              "std                  0.448944                0.187191   \n",
              "min                  0.000000                0.000000   \n",
              "25%                  0.050000                0.000000   \n",
              "50%                  0.510000                0.030000   \n",
              "75%                  1.000000                0.070000   \n",
              "max                  1.000000                1.000000   \n",
              "\n",
              "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "count                 25192.000000                 25192.000000   \n",
              "mean                      0.147453                     0.031844   \n",
              "std                       0.308367                     0.110575   \n",
              "min                       0.000000                     0.000000   \n",
              "25%                       0.000000                     0.000000   \n",
              "50%                       0.000000                     0.000000   \n",
              "75%                       0.060000                     0.020000   \n",
              "max                       1.000000                     1.000000   \n",
              "\n",
              "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "count          25192.000000              25192.000000          25192.000000   \n",
              "mean               0.285800                  0.279846              0.117800   \n",
              "std                0.445316                  0.446075              0.305869   \n",
              "min                0.000000                  0.000000              0.000000   \n",
              "25%                0.000000                  0.000000              0.000000   \n",
              "50%                0.000000                  0.000000              0.000000   \n",
              "75%                1.000000                  1.000000              0.000000   \n",
              "max                1.000000                  1.000000              1.000000   \n",
              "\n",
              "       dst_host_srv_rerror_rate  \n",
              "count              25192.000000  \n",
              "mean                   0.118769  \n",
              "std                    0.317333  \n",
              "min                    0.000000  \n",
              "25%                    0.000000  \n",
              "50%                    0.000000  \n",
              "75%                    0.000000  \n",
              "max                    1.000000  \n",
              "\n",
              "[8 rows x 38 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a795213-1295-4050-9df6-678f0baa2925\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>src_bytes</th>\n",
              "      <th>dst_bytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrong_fragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>num_failed_logins</th>\n",
              "      <th>logged_in</th>\n",
              "      <th>num_compromised</th>\n",
              "      <th>...</th>\n",
              "      <th>dst_host_count</th>\n",
              "      <th>dst_host_srv_count</th>\n",
              "      <th>dst_host_same_srv_rate</th>\n",
              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25192.000000</td>\n",
              "      <td>2.519200e+04</td>\n",
              "      <td>2.519200e+04</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.00000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "      <td>25192.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>305.054104</td>\n",
              "      <td>2.433063e+04</td>\n",
              "      <td>3.491847e+03</td>\n",
              "      <td>0.000079</td>\n",
              "      <td>0.023738</td>\n",
              "      <td>0.00004</td>\n",
              "      <td>0.198039</td>\n",
              "      <td>0.001191</td>\n",
              "      <td>0.394768</td>\n",
              "      <td>0.227850</td>\n",
              "      <td>...</td>\n",
              "      <td>182.532074</td>\n",
              "      <td>115.063036</td>\n",
              "      <td>0.519791</td>\n",
              "      <td>0.082539</td>\n",
              "      <td>0.147453</td>\n",
              "      <td>0.031844</td>\n",
              "      <td>0.285800</td>\n",
              "      <td>0.279846</td>\n",
              "      <td>0.117800</td>\n",
              "      <td>0.118769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2686.555640</td>\n",
              "      <td>2.410805e+06</td>\n",
              "      <td>8.883072e+04</td>\n",
              "      <td>0.008910</td>\n",
              "      <td>0.260221</td>\n",
              "      <td>0.00630</td>\n",
              "      <td>2.154202</td>\n",
              "      <td>0.045418</td>\n",
              "      <td>0.488811</td>\n",
              "      <td>10.417352</td>\n",
              "      <td>...</td>\n",
              "      <td>98.993895</td>\n",
              "      <td>110.646850</td>\n",
              "      <td>0.448944</td>\n",
              "      <td>0.187191</td>\n",
              "      <td>0.308367</td>\n",
              "      <td>0.110575</td>\n",
              "      <td>0.445316</td>\n",
              "      <td>0.446075</td>\n",
              "      <td>0.305869</td>\n",
              "      <td>0.317333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.400000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.790000e+02</td>\n",
              "      <td>5.302500e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>42862.000000</td>\n",
              "      <td>3.817091e+08</td>\n",
              "      <td>5.151385e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>884.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 38 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-6a795213-1295-4050-9df6-678f0baa2925 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6a795213-1295-4050-9df6-678f0baa2925');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8bf561bd-7be6-481c-9cf5-44d50af7ccdf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8bf561bd-7be6-481c-9cf5-44d50af7ccdf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8bf561bd-7be6-481c-9cf5-44d50af7ccdf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds.describe(include='object')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "HON5qphLMEdy",
        "outputId": "7b581377-81e6-44e0-9099-e91a95de8907"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       protocol_type service   flag   class\n",
              "count          25192   25192  25192   25192\n",
              "unique             3      66     11       2\n",
              "top              tcp    http     SF  normal\n",
              "freq           20526    8003  14973   13449"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-124eacf3-1258-4ef4-a079-03c6f474a063\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>protocol_type</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>25192</td>\n",
              "      <td>25192</td>\n",
              "      <td>25192</td>\n",
              "      <td>25192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>3</td>\n",
              "      <td>66</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>20526</td>\n",
              "      <td>8003</td>\n",
              "      <td>14973</td>\n",
              "      <td>13449</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-124eacf3-1258-4ef4-a079-03c6f474a063')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-124eacf3-1258-4ef4-a079-03c6f474a063 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-124eacf3-1258-4ef4-a079-03c6f474a063');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-56322bfe-2db7-4e3e-ba78-911a893f1210\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56322bfe-2db7-4e3e-ba78-911a893f1210')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-56322bfe-2db7-4e3e-ba78-911a893f1210 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ds\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"protocol_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"20526\",\n          \"25192\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"service\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          66,\n          \"8003\",\n          \"25192\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flag\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          11,\n          \"14973\",\n          \"25192\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"13449\",\n          \"25192\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "pGcxkHzMLEJY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the class distribution\n",
        "ds['class'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_5U-c7eyCCF",
        "outputId": "1f6214f8-4038-493f-fe15-33b62f7d93c7"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "normal     13449\n",
              "anomaly    11743\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=ds['class'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "E8s76YXBbFno",
        "outputId": "e5cc3120-1888-43af-94df-d62189465c99"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Dg42aCz+nvqb+cwAAANwams6cOaOioiIVFRVJ+vbm76KiIp04cUIWi0UTJkzQCy+8oPfee0/79+/X6NGjZbPZjN+wi4mJ0aBBgzR27Fjt3r1b27dvV3p6ukaMGCGbzSZJGjVqlHx8fJSamqqDBw9q9erVWrBggTIyMow+Hn/8cWVnZ2vOnDk6dOiQpk+frj179ig9Pf1yfyUAAMBDebvzw/fs2aPbbrvNeF0fZMaMGaPly5dr0qRJqqqq0rhx41RRUaF+/fopOztbfn5+xntWrlyp9PR03XHHHfLy8tKwYcO0cOFCYzwwMFCbN29WWlqaevbsqfbt22vatGkuz3K6+eab9fbbb2vKlCn6f//v/6lTp05av369unXrdhm+BQAA0BJ4zHOaWjqe0wS4D89pAq5cPKcJAACghSE0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABM8OjQdP78eU2dOlXR0dHy9/fXz372Mz3//PNyOp1GjdPp1LRp0xQRESF/f38lJCToyJEjLvOUl5crJSVFVqtVQUFBSk1N1ZkzZ1xq9u3bp1tuuUV+fn6KjIxUVlbWZVkjAABoGTw6NM2aNUtLlizRK6+8ouLiYs2aNUtZWVlatGiRUZOVlaWFCxdq6dKl2rVrl9q2bavExESdPXvWqElJSdHBgweVk5OjDRs2KC8vT+PGjTPGHQ6HBg4cqKioKBUWFmr27NmaPn26li1bdlnXCwAAPJe3uxv4Pjt27NDQoUOVlJQkSerYsaPeeecd7d69W9K3Z5nmz5+vKVOmaOjQoZKkt956S2FhYVq/fr1GjBih4uJiZWdnq6CgQL169ZIkLVq0SIMHD9bLL78sm82mlStXqqamRm+88YZ8fHzUtWtXFRUVae7cuS7hCgAAXL08+kzTzTffrNzcXH366aeSpI8//lh///vf9atf/UqSdPToUdntdiUkJBjvCQwMVJ8+fZSfny9Jys/PV1BQkBGYJCkhIUFeXl7atWuXUdO/f3/5+PgYNYmJiTp8+LBOnz590d6qq6vlcDhcNgAAcOXy6DNNTz/9tBwOh7p06aJWrVrp/PnzevHFF5WSkiJJstvtkqSwsDCX94WFhRljdrtdoaGhLuPe3t4KCQlxqYmOjm4wR/1YcHBwg95mzpypGTNmNMEqAQBAS+DRZ5reffddrVy5Um+//bY++ugjrVixQi+//LJWrFjh7taUmZmpyspKYyspKXF3SwAAoBl59JmmiRMn6umnn9aIESMkSXFxcTp+/LhmzpypMWPGKDw8XJJUWlqqiIgI432lpaXq0aOHJCk8PFxlZWUu8547d07l5eXG+8PDw1VaWupSU/+6vua/+fr6ytfX98cvEgAAtAgefabp66+/lpeXa4utWrVSXV2dJCk6Olrh4eHKzc01xh0Oh3bt2qX4+HhJUnx8vCoqKlRYWGjUbNmyRXV1derTp49Rk5eXp9raWqMmJydHnTt3vuilOQAAcPXx6NA0ZMgQvfjii9q4caOOHTumdevWae7cubrrrrskSRaLRRMmTNALL7yg9957T/v379fo0aNls9mUnJwsSYqJidGgQYM0duxY7d69W9u3b1d6erpGjBghm80mSRo1apR8fHyUmpqqgwcPavXq1VqwYIEyMjLctXQAAOBhPPry3KJFizR16lQ9+uijKisrk81m00MPPaRp06YZNZMmTVJVVZXGjRuniooK9evXT9nZ2fLz8zNqVq5cqfT0dN1xxx3y8vLSsGHDtHDhQmM8MDBQmzdvVlpamnr27Kn27dtr2rRpPG4AAAAYLM4LH6+NRnM4HAoMDFRlZaWsVmuzfU7PiW8129xAS1U4e7S7W2gSHN9AQ819fF/Kz2+PvjwHAADgKRoVmm6//XZVVFQ02O9wOHT77bf/2J4AAAA8TqNC09atW1VTU9Ng/9mzZ/W3v/3tRzcFAADgaS7pRvB9+/YZ//3JJ58YT9SWpPPnzys7O1s/+clPmq47AAAAD3FJoalHjx6yWCyyWCwXvQzn7++vRYsWNVlzAAAAnuKSQtPRo0fldDr105/+VLt371aHDh2MMR8fH4WGhqpVq1ZN3iQAAIC7XVJoioqKkiTjidwAAABXi0Y/3PLIkSP68MMPVVZW1iBEXfjwSQAAgCtBo0LTa6+9pkceeUTt27dXeHi4LBaLMWaxWAhNAADgitOo0PTCCy/oxRdf1OTJk5u6HwAAAI/UqOc0nT59Wr/5zW+auhcAAACP1ajQ9Jvf/EabN29u6l4AAAA8VqMuz11//fWaOnWqdu7cqbi4OLVu3dpl/LHHHmuS5gAAADxFo0LTsmXLFBAQoG3btmnbtm0uYxaLhdAEAACuOI0KTUePHm3qPgAAADxao+5pAgAAuNo06kzTAw888L3jb7zxRqOaAQAA8FSNCk2nT592eV1bW6sDBw6ooqLion/IFwAAoKVrVGhat25dg311dXV65JFH9LOf/exHNwUAAOBpmuyeJi8vL2VkZGjevHlNNSUAAIDHaNIbwT///HOdO3euKacEAADwCI26PJeRkeHy2ul06uTJk9q4caPGjBnTJI0BAAB4kkaFpr1797q89vLyUocOHTRnzpwf/M06AACAlqhRoenDDz9s6j4AAAA8WqNCU71Tp07p8OHDkqTOnTurQ4cOTdIUAACAp2nUjeBVVVV64IEHFBERof79+6t///6y2WxKTU3V119/3dQ9AgAAuF2jQlNGRoa2bdum999/XxUVFaqoqNCf//xnbdu2TU8++WRT9wgAAOB2jbo896c//Ul//OMfNWDAAGPf4MGD5e/vr3vuuUdLlixpqv4AAAA8QqPONH399dcKCwtrsD80NJTLcwAA4IrUqNAUHx+vZ599VmfPnjX2ffPNN5oxY4bi4+ObrDkAAABP0ajLc/Pnz9egQYN07bXXqnv37pKkjz/+WL6+vtq8eXOTNggAAOAJGhWa4uLidOTIEa1cuVKHDh2SJI0cOVIpKSny9/dv0gYBAAA8QaNC08yZMxUWFqaxY8e67H/jjTd06tQpTZ48uUmaAwAA8BSNuqfp1VdfVZcuXRrs79q1q5YuXfqjmwIAAPA0jQpNdrtdERERDfZ36NBBJ0+e/NFNAQAAeJpGhabIyEht3769wf7t27fLZrP96KYAAAA8TaPuaRo7dqwmTJig2tpa3X777ZKk3NxcTZo0iSeCAwCAK1KjQtPEiRP15Zdf6tFHH1VNTY0kyc/PT5MnT1ZmZmaTNggAAOAJGhWaLBaLZs2apalTp6q4uFj+/v7q1KmTfH19m7o/AAAAj9Co0FQvICBAvXv3bqpeAAAAPFajbgQHAAC42nh8aPrXv/6le++9V+3atZO/v7/i4uK0Z88eY9zpdGratGmKiIiQv7+/EhISdOTIEZc5ysvLlZKSIqvVqqCgIKWmpurMmTMuNfv27dMtt9wiPz8/RUZGKisr67KsDwAAtAweHZpOnz6tvn37qnXr1vrLX/6iTz75RHPmzFFwcLBRk5WVpYULF2rp0qXatWuX2rZtq8TERJc/JpySkqKDBw8qJydHGzZsUF5ensaNG2eMOxwODRw4UFFRUSosLNTs2bM1ffp0LVu27LKuFwAAeK4fdU9Tc5s1a5YiIyP15ptvGvuio6ON/3Y6nZo/f76mTJmioUOHSpLeeusthYWFaf369RoxYoSKi4uVnZ2tgoIC9erVS5K0aNEiDR48WC+//LJsNptWrlypmpoavfHGG/Lx8VHXrl1VVFSkuXPnuoQrAABw9fLoM03vvfeeevXqpd/85jcKDQ3VL37xC7322mvG+NGjR2W325WQkGDsCwwMVJ8+fZSfny9Jys/PV1BQkBGYJCkhIUFeXl7atWuXUdO/f3/5+PgYNYmJiTp8+LBOnz7d3MsEAAAtgEeHpn/84x9asmSJOnXqpE2bNumRRx7RY489phUrVkj69s+5SFJYWJjL+8LCwowxu92u0NBQl3Fvb2+FhIS41Fxsjgs/479VV1fL4XC4bAAA4Mrl0Zfn6urq1KtXL/3ud7+TJP3iF7/QgQMHtHTpUo0ZM8atvc2cOVMzZsxwaw8AAODy8egzTREREYqNjXXZFxMToxMnTkiSwsPDJUmlpaUuNaWlpcZYeHi4ysrKXMbPnTun8vJyl5qLzXHhZ/y3zMxMVVZWGltJSUljlggAAFoIjw5Nffv21eHDh132ffrpp4qKipL07U3h4eHhys3NNcYdDod27dql+Ph4SVJ8fLwqKipUWFho1GzZskV1dXXq06ePUZOXl6fa2lqjJicnR507d3b5Tb0L+fr6ymq1umwAAODK5dGh6YknntDOnTv1u9/9Tp999pnefvttLVu2TGlpaZK+/XMuEyZM0AsvvKD33ntP+/fv1+jRo2Wz2ZScnCzp2zNTgwYN0tixY7V7925t375d6enpGjFihGw2myRp1KhR8vHxUWpqqg4ePKjVq1drwYIFysjIcNfSAQCAh/Hoe5p69+6tdevWKTMzU88995yio6M1f/58paSkGDWTJk1SVVWVxo0bp4qKCvXr10/Z2dny8/MzalauXKn09HTdcccd8vLy0rBhw7Rw4UJjPDAwUJs3b1ZaWpp69uyp9u3ba9q0aTxuAAAAGCxOp9Pp7iauBA6HQ4GBgaqsrGzWS3U9J77VbHMDLVXh7NHubqFJcHwDDTX38X0pP789+vIcAACApyA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACY0KJC00svvSSLxaIJEyYY+86ePau0tDS1a9dOAQEBGjZsmEpLS13ed+LECSUlJalNmzYKDQ3VxIkTde7cOZearVu36sYbb5Svr6+uv/56LV++/DKsCAAAtBQtJjQVFBTo1Vdf1c9//nOX/U888YTef/99rVmzRtu2bdMXX3yhX//618b4+fPnlZSUpJqaGu3YsUMrVqzQ8uXLNW3aNKPm6NGjSkpK0m233aaioiJNmDBBDz74oDZt2nTZ1gcAADxbiwhNZ86cUUpKil577TUFBwcb+ysrK/WHP/xBc+fO1e23366ePXvqzTff1I4dO7Rz505J0ubNm/XJJ5/o//7v/9SjRw/96le/0vPPP6/FixerpqZGkrR06VJFR0drzpw5iomJUXp6uu6++27NmzfPLesFAACep0WEprS0NCUlJSkhIcFlf2FhoWpra132d+nSRdddd53y8/MlSfn5+YqLi1NYWJhRk5iYKIfDoYMHDxo1/z13YmKiMcfFVFdXy+FwuGwAAODK5e3uBn7IqlWr9NFHH6mgoKDBmN1ul4+Pj4KCglz2h4WFyW63GzUXBqb68fqx76txOBz65ptv5O/v3+CzZ86cqRkzZjR6XQAAoGXx6DNNJSUlevzxx7Vy5Ur5+fm5ux0XmZmZqqysNLaSkhJ3twQAAJqRR4emwsJClZWV6cYbb5S3t7e8vb21bds2LVy4UN7e3goLC1NNTY0qKipc3ldaWqrw8HBJUnh4eIPfpqt//UM1Vqv1omeZJMnX11dWq9VlAwAAVy6PDk133HGH9u/fr6KiImPr1auXUlJSjP9u3bq1cnNzjfccPnxYJ06cUHx8vCQpPj5e+/fvV1lZmVGTk5Mjq9Wq2NhYo+bCOepr6ucAAADw6HuarrnmGnXr1s1lX9u2bdWuXTtjf2pqqjIyMhQSEiKr1arx48crPj5eN910kyRp4MCBio2N1X333aesrCzZ7XZNmTJFaWlp8vX1lSQ9/PDDeuWVVzRp0iQ98MAD2rJli959911t3Ljx8i4YAAB4LI8OTWbMmzdPXl5eGjZsmKqrq5WYmKjf//73xnirVq20YcMGPfLII4qPj1fbtm01ZswYPffcc0ZNdHS0Nm7cqCeeeEILFizQtddeq9dff12JiYnuWBIAAPBAFqfT6XR3E1cCh8OhwMBAVVZWNuv9TT0nvtVscwMtVeHs0e5uoUlwfAMNNffxfSk/vz36niYAAABPQWgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJjg0aFp5syZ6t27t6655hqFhoYqOTlZhw8fdqk5e/as0tLS1K5dOwUEBGjYsGEqLS11qTlx4oSSkpLUpk0bhYaGauLEiTp37pxLzdatW3XjjTfK19dX119/vZYvX97cywMAAC2IR4embdu2KS0tTTt37lROTo5qa2s1cOBAVVVVGTVPPPGE3n//fa1Zs0bbtm3TF198oV//+tfG+Pnz55WUlKSamhrt2LFDK1as0PLlyzVt2jSj5ujRo0pKStJtt92moqIiTZgwQQ8++KA2bdp0WdcLAAA8l8XpdDrd3YRZp06dUmhoqLZt26b+/fursrJSHTp00Ntvv627775bknTo0CHFxMQoPz9fN910k/7yl7/ozjvv1BdffKGwsDBJ0tKlSzV58mSdOnVKPj4+mjx5sjZu3KgDBw4YnzVixAhVVFQoOzvbVG8Oh0OBgYGqrKyU1Wpt+sX/R8+JbzXb3EBLVTh7tLtbaBIc30BDzX18X8rPb48+0/TfKisrJUkhISGSpMLCQtXW1iohIcGo6dKli6677jrl5+dLkvLz8xUXF2cEJklKTEyUw+HQwYMHjZoL56ivqZ/jYqqrq+VwOFw2AABw5Woxoamurk4TJkxQ37591a1bN0mS3W6Xj4+PgoKCXGrDwsJkt9uNmgsDU/14/dj31TgcDn3zzTcX7WfmzJkKDAw0tsjIyB+9RgAA4LlaTGhKS0vTgQMHtGrVKne3IknKzMxUZWWlsZWUlLi7JQAA0Iy83d2AGenp6dqwYYPy8vJ07bXXGvvDw8NVU1OjiooKl7NNpaWlCg8PN2p2797tMl/9b9ddWPPfv3FXWloqq9Uqf3//i/bk6+srX1/fH702AADQMnj0mSan06n09HStW7dOW7ZsUXR0tMt4z5491bp1a+Xm5hr7Dh8+rBMnTig+Pl6SFB8fr/3796usrMyoycnJkdVqVWxsrFFz4Rz1NfVzAAAAePSZprS0NL399tv685//rGuuuca4BykwMFD+/v4KDAxUamqqMjIyFBISIqvVqvHjxys+Pl433XSTJGngwIGKjY3Vfffdp6ysLNntdk2ZMkVpaWnGmaKHH35Yr7zyiiZNmqQHHnhAW7Zs0bvvvquNGze6be0AAMCzePSZpiVLlqiyslIDBgxQRESEsa1evdqomTdvnu68804NGzZM/fv3V3h4uNauXWuMt2rVShs2bFCrVq0UHx+ve++9V6NHj9Zzzz1n1ERHR2vjxo3KyclR9+7dNWfOHL3++utKTEy8rOsFAACey6PPNJl5hJSfn58WL16sxYsXf2dNVFSUPvjgg++dZ8CAAdq7d+8l9wgAAK4OHn2mCQAAwFMQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCa/svixYvVsWNH+fn5qU+fPtq9e7e7WwIAAB6A0HSB1atXKyMjQ88++6w++ugjde/eXYmJiSorK3N3awAAwM0ITReYO3euxo4dq/vvv1+xsbFaunSp2rRpozfeeMPdrQEAADcjNP1HTU2NCgsLlZCQYOzz8vJSQkKC8vPz3dgZAADwBN7ubsBT/Pvf/9b58+cVFhbmsj8sLEyHDh1qUF9dXa3q6mrjdWVlpSTJ4XA0a5/nq79p1vmBlqi5j7vLheMbaKi5j+/6+Z1O5w/WEpoaaebMmZoxY0aD/ZGRkW7oBri6BS562N0tAGgml+v4/uqrrxQYGPi9NYSm/2jfvr1atWql0tJSl/2lpaUKDw9vUJ+ZmamMjAzjdV1dncrLy9WuXTtZLJZm7xfu5XA4FBkZqZKSElmtVne3A6AJcXxfXZxOp7766ivZbLYfrCU0/YePj4969uyp3NxcJScnS/o2COXm5io9Pb1Bva+vr3x9fV32BQUFXYZO4UmsViv/UwWuUBzfV48fOsNUj9B0gYyMDI0ZM0a9evXSL3/5S82fP19VVVW6//773d0aAABwM0LTBYYPH65Tp05p2rRpstvt6tGjh7KzsxvcHA4AAK4+hKb/kp6eftHLccCFfH199eyzzza4RAug5eP4xnexOM38jh0AAMBVjodbAgAAmEBoAgAAMIHQBAAAYAKhCfAgHTt21Pz5893dBoAmNGDAAE2YMMHdbaAJEJoAAABMIDQBl6CmpsbdLQAA3ITQhCvagAED9Nhjj2nSpEkKCQlReHi4pk+fboyfOHFCQ4cOVUBAgKxWq+655x6Xvz84ffp09ejRQ6+//rqio6Pl5+cnSbJYLHr11Vd15513qk2bNoqJiVF+fr4+++wzDRgwQG3bttXNN9+szz//3Jjr888/19ChQxUWFqaAgAD17t1bf/3rXy/bdwG0ZNnZ2erXr5+CgoLUrl073XnnncbxdezYMVksFq1du1a33Xab2rRpo+7duys/P99ljj/96U/q2rWrfH191bFjR82ZM8dlvGPHjnrhhRc0evRoBQQEKCoqSu+9955OnTpl/H/i5z//ufbs2WO858svv9TIkSP1k5/8RG3atFFcXJzeeeed71zHc889p27dujXY36NHD02dOvXHfEW4DAhNuOKtWLFCbdu21a5du5SVlaXnnntOOTk5qqur09ChQ1VeXq5t27YpJydH//jHPzR8+HCX93/22Wf605/+pLVr16qoqMjY//zzz2v06NEqKipSly5dNGrUKD300EPKzMzUnj175HQ6XR6UeubMGQ0ePFi5ubnau3evBg0apCFDhujEiROX66sAWqyqqiplZGRoz549ys3NlZeXl+666y7V1dUZNc8884yeeuopFRUV6YYbbtDIkSN17tw5SVJhYaHuuecejRgxQvv379f06dM1depULV++3OVz5s2bp759+2rv3r1KSkrSfffdp9GjR+vee+/VRx99pJ/97GcaPXq06h9xePbsWfXs2VMbN27UgQMHNG7cON13333avXv3RdfxwAMPqLi4WAUFBca+vXv3at++ffzJrpbACVzBbr31Vme/fv1c9vXu3ds5efJk5+bNm52tWrVynjhxwhg7ePCgU5Jz9+7dTqfT6Xz22WedrVu3dpaVlbnMIck5ZcoU43V+fr5TkvMPf/iDse+dd95x+vn5fW9/Xbt2dS5atMh4HRUV5Zw3b94lrxO42pw6dcopybl//37n0aNHnZKcr7/+ujFefywXFxc7nU6nc9SoUc7/+Z//cZlj4sSJztjYWON1VFSU89577zVenzx50inJOXXqVGNf/bF+8uTJ7+wtKSnJ+eSTTxqvb731Vufjjz9uvP7Vr37lfOSRR4zX48ePdw4YMOASVg934UwTrng///nPXV5HRESorKxMxcXFioyMVGRkpDEWGxuroKAgFRcXG/uioqLUoUOH7523/u8TxsXFuew7e/asHA6HpG/PND311FOKiYlRUFCQAgICVFxczJkmwIQjR45o5MiR+ulPfyqr1aqOHTtKksvxc+ExGRERIUkqKyuTJBUXF6tv374uc/bt21dHjhzR+fPnLzrHdx3XF857/vx5Pf/884qLi1NISIgCAgK0adOm7z2ux44dq3feeUdnz55VTU2N3n77bT3wwAPmvwy4DX97Dle81q1bu7y2WCwup/R/SNu2bX9wXovF8p376j/rqaeeUk5Ojl5++WVdf/318vf31913383N5YAJQ4YMUVRUlF577TXZbDbV1dWpW7duLsfP9x1/Zl3qcT179mwtWLBA8+fPV1xcnNq2basJEyZ873E9ZMgQ+fr6at26dfLx8VFtba3uvvvuS+oT7kFowlUrJiZGJSUlKikpMc42ffLJJ6qoqFBsbGyTf9727dv129/+VnfddZekb888HTt2rMk/B7jSfPnllzp8+LBee+013XLLLZKkv//975c0R0xMjLZv3+6yb/v27brhhhvUqlWrRve2fft2DR06VPfee6+kb8PUp59++r3/D/H29taYMWP05ptvysfHRyNGjJC/v3+je8DlQ2jCVSshIUFxcXFKSUnR/Pnzde7cOT366KO69dZb1atXryb/vE6dOmnt2rUaMmSILBaLpk6desn/CgauRsHBwWrXrp2WLVumiIgInThxQk8//fQlzfHkk0+qd+/eev755zV8+HDl5+frlVde0e9///sf1VunTp30xz/+UTt27FBwcLDmzp2r0tLSH/yH14MPPqiYmBhJahDm4Lm4pwlXLYvFoj//+c8KDg5W//79lZCQoJ/+9KdavXp1s3ze3LlzFRwcrJtvvllDhgxRYmKibrzxxmb5LOBK4uXlpVWrVqmwsFDdunXTE088odmzZ1/SHDfeeKPeffddrVq1St26ddO0adP03HPP6be//e2P6m3KlCm68cYblZiYqAEDBig8PFzJyck/+L5OnTrp5ptvVpcuXdSnT58f1QMuH4vT+Z/fmwQAAJeF0+lUp06d9OijjyojI8Pd7cAkLs8BAHAZnTp1SqtWrZLdbufZTC0MoQkAgMsoNDRU7du317JlyxQcHOzudnAJCE0AAFxG3BXTcnEjOAAAgAmEJgAAABMITQAAACYQmgAAAEwgNAG46h07dkwWi0VFRUXubgWAByM0AQAAmEBoAgAAMIHQBOCqUVdXp6ysLF1//fXy9fXVddddpxdffLFB3fnz55Wamqro6Gj5+/urc+fOWrBggUvN1q1b9ctf/lJt27ZVUFCQ+vbtq+PHj0uSPv74Y91222265pprZLVa1bNnT+3Zs+eyrBFA8+HhlgCuGpmZmXrttdc0b9489evXTydPntShQ4ca1NXV1enaa6/VmjVr1K5dO+3YsUPjxo1TRESE7rnnHp07d07JyckaO3as3nnnHdXU1Gj37t2yWCySpJSUFP3iF7/QkiVL1KpVKxUVFal169aXe7kAmhh/sBfAVeGrr75Shw4d9Morr+jBBx90GTt27Jiio6O1d+9e9ejR46LvT09Pl91u1x//+EeVl5erXbt22rp1q2699dYGtVarVYsWLdKYMWOaYykA3ITLcwCuCsXFxaqurtYdd9xhqn7x4sXq2bOnOnTooICAAC1btkwnTpyQJIWEhOi3v/2tEhMTNWTIEC1YsEAnT5403puRkaEHH3xQCQkJeumll/T55583y5oAXF6EJgBXBX9/f9O1q1at0lNPPaXU1FRt3rxZRUVFuv/++1VTU2PUvPnmm8rPz9fNN9+s1atX64YbbtDOnTslSdOnT9fBgweVlJSkLVu2KDY2VuvWrWvyNQG4vLg8B+CqcPbsWYWEhGjhwoU/eHlu/Pjx+uSTT5Sbm2vUJCQk6N///vd3PsspPj5evXv31sKFCxuMjRw5UlVVVXrvvfeadE0ALi/ONAG4Kvj5+Wny5MmaNGmS3nrrLX3++efauXOn/vCHPzSo7dSpk/bs2aNNmzbp008/1dSpU1VQUGCMHz16VJmZmcrPz9fx48e1efNmHTlyRDExMfrmm2+Unp6urVu36vjx49q+fbsKCgoUExNzOZcLoBnw23MArhpTp06Vt7e3pk2bpi+++EIRERF6+OGHG9Q99NBD2rt3r4YPHy6LxaKRI0fq0Ucf1V/+8hdJUps2bXTo0CGtWLFCX375pSIiIpSWlqaHHnpI586d05dffqnRo0ertLRU7du3169//WvNmDHjci8XQBPj8hwAAIAJXJ4DAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAn/HxkSOV7aY4i6AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize protocol types\n",
        "ds['protocol_type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sd49X745MbOc",
        "outputId": "31495a9e-efc5-4c96-f100-0b3a8364edc7"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "protocol_type\n",
              "tcp     20526\n",
              "udp      3011\n",
              "icmp     1655\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=ds['protocol_type'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "PIlQX51abKk3",
        "outputId": "4af7ee2f-6806-4bfc-9b52-6afd87322bb4"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Heatmap for correlation\n",
        "sns.heatmap(ds.corr(numeric_only=True))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ZL6FMQ8uMi6z",
        "outputId": "4cf4af11-9adf-45de-9d97-76ce9b516008"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MizEvxiEpSsd"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80Z6B1mis4l6",
        "outputId": "8c62033a-d1ab-4d80-a81c-6f11453ef8d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "duration                       0\n",
              "protocol_type                  0\n",
              "service                        0\n",
              "flag                           0\n",
              "src_bytes                      0\n",
              "dst_bytes                      0\n",
              "land                           0\n",
              "wrong_fragment                 0\n",
              "urgent                         0\n",
              "hot                            0\n",
              "num_failed_logins              0\n",
              "logged_in                      0\n",
              "num_compromised                0\n",
              "root_shell                     0\n",
              "su_attempted                   0\n",
              "num_root                       0\n",
              "num_file_creations             0\n",
              "num_shells                     0\n",
              "num_access_files               0\n",
              "num_outbound_cmds              0\n",
              "is_host_login                  0\n",
              "is_guest_login                 0\n",
              "count                          0\n",
              "srv_count                      0\n",
              "serror_rate                    0\n",
              "srv_serror_rate                0\n",
              "rerror_rate                    0\n",
              "srv_rerror_rate                0\n",
              "same_srv_rate                  0\n",
              "diff_srv_rate                  0\n",
              "srv_diff_host_rate             0\n",
              "dst_host_count                 0\n",
              "dst_host_srv_count             0\n",
              "dst_host_same_srv_rate         0\n",
              "dst_host_diff_srv_rate         0\n",
              "dst_host_same_src_port_rate    0\n",
              "dst_host_srv_diff_host_rate    0\n",
              "dst_host_serror_rate           0\n",
              "dst_host_srv_serror_rate       0\n",
              "dst_host_rerror_rate           0\n",
              "dst_host_srv_rerror_rate       0\n",
              "class                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "ds.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NK-b6txJs77v",
        "outputId": "960a5834-ba8c-44c9-966b-164d25f7d042"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "ds.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "8ymgj_1dtf5i"
      },
      "outputs": [],
      "source": [
        "# Encode categorical features\n",
        "label_encoder = LabelEncoder()\n",
        "def le(df):\n",
        "    for col in df.columns:\n",
        "        if df[col].dtype == 'object':\n",
        "            df[col] = label_encoder.fit_transform(df[col])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "LIg9LzjntjmX",
        "outputId": "9d91240b-f0de-485f-d124-6412fe41188d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   duration  protocol_type  service  flag  src_bytes  dst_bytes  land  \\\n",
              "0         0              1       19     9        491          0     0   \n",
              "1         0              2       41     9        146          0     0   \n",
              "2         0              1       46     5          0          0     0   \n",
              "3         0              1       22     9        232       8153     0   \n",
              "4         0              1       22     9        199        420     0   \n",
              "\n",
              "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0               0       0    0  ...                  25   \n",
              "1               0       0    0  ...                   1   \n",
              "2               0       0    0  ...                  26   \n",
              "3               0       0    0  ...                 255   \n",
              "4               0       0    0  ...                 255   \n",
              "\n",
              "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                    0.17                    0.03   \n",
              "1                    0.00                    0.60   \n",
              "2                    0.10                    0.05   \n",
              "3                    1.00                    0.00   \n",
              "4                    1.00                    0.00   \n",
              "\n",
              "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                         0.17                         0.00   \n",
              "1                         0.88                         0.00   \n",
              "2                         0.00                         0.00   \n",
              "3                         0.03                         0.04   \n",
              "4                         0.00                         0.00   \n",
              "\n",
              "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                  0.00                      0.00                  0.05   \n",
              "1                  0.00                      0.00                  0.00   \n",
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              "3                  0.03                      0.01                  0.00   \n",
              "4                  0.00                      0.00                  0.00   \n",
              "\n",
              "   dst_host_srv_rerror_rate  class  \n",
              "0                      0.00      1  \n",
              "1                      0.00      1  \n",
              "2                      0.00      0  \n",
              "3                      0.01      1  \n",
              "4                      0.00      1  \n",
              "\n",
              "[5 rows x 42 columns]"
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              "      <td>26</td>\n",
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              "      <td>232</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 42 columns</p>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "<style>\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-e6d02aaf-adbb-4f14-b704-557943ee805e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "le(ds)\n",
        "(ds.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "XwR2XGs0tnPU"
      },
      "outputs": [],
      "source": [
        "ds.drop(['num_outbound_cmds'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "cG6oeHo2tszZ"
      },
      "outputs": [],
      "source": [
        "# Feature Selection\n",
        "X = ds.drop(['class'], axis=1)\n",
        "y = ds['class']\n",
        "rfc = RandomForestClassifier()\n",
        "rfe = RFE(rfc, n_features_to_select=10)\n",
        "rfe = rfe.fit(X, y)\n",
        "feature_map = [(i, v) for i, v in itertools.zip_longest(rfe.get_support(), X.columns)]\n",
        "selected_features = [v for i, v in feature_map if i]\n",
        "top_features = pd.DataFrame({'Features': selected_features})\n",
        "top_features.index = top_features.index + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HWimW1xttsU",
        "outputId": "3f245a58-1da4-4585-caad-2cedcbd4cd4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  Features\n",
            "1            protocol_type\n",
            "2                  service\n",
            "3                     flag\n",
            "4                src_bytes\n",
            "5                dst_bytes\n",
            "6                    count\n",
            "7            same_srv_rate\n",
            "8            diff_srv_rate\n",
            "9       dst_host_srv_count\n",
            "10  dst_host_same_srv_rate\n"
          ]
        }
      ],
      "source": [
        "print(top_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "v8Uua_eftwDp"
      },
      "outputs": [],
      "source": [
        "X = X[selected_features]\n",
        "scale = StandardScaler()\n",
        "X = scale.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# ANN train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n",
        "\n",
        "# LSTM train-test split\n",
        "X_trainL, X_testL, y_trainL, y_testL = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "X_trainL, X_valL, y_trainL, y_valL = train_test_split(X_trainL, y_trainL, test_size=0.25, random_state=42)"
      ],
      "metadata": {
        "id": "NS_WzqlkNSHW"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ANN- Binary Classificaton"
      ],
      "metadata": {
        "id": "Uczl1Ht8LWI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ANN model\n",
        "model_ann = Sequential()\n",
        "model_ann.add(Dense(128, input_dim=X_train.shape[1]))\n",
        "model_ann.add(ReLU())\n",
        "model_ann.add(BatchNormalization())\n",
        "model_ann.add(Dense(128))\n",
        "model_ann.add(ReLU())\n",
        "model_ann.add(BatchNormalization())\n",
        "model_ann.add(Dropout(0.2))\n",
        "model_ann.add(Dense(64))\n",
        "model_ann.add(ReLU())\n",
        "model_ann.add(BatchNormalization())\n",
        "model_ann.add(Dropout(0.2))\n",
        "model_ann.add(Dense(32))\n",
        "model_ann.add(ReLU())\n",
        "model_ann.add(BatchNormalization())\n",
        "model_ann.add(Dropout(0.2))\n",
        "model_ann.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "s4AhBNRiySbx"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training and compilation\n",
        "model_ann.compile(optimizer=Adam(learning_rate=0.001), loss=BinaryCrossentropy(), metrics=['accuracy'])\n",
        "history_ann = model_ann.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_val, y_val))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RbzJrD4ROLtz",
        "outputId": "f70060ee-f75c-4395-d491-46f2c4700528"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "473/473 [==============================] - 5s 5ms/step - loss: 0.1546 - accuracy: 0.9466 - val_loss: 0.1072 - val_accuracy: 0.9647\n",
            "Epoch 2/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.1230 - accuracy: 0.9588 - val_loss: 0.1003 - val_accuracy: 0.9633\n",
            "Epoch 3/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.1210 - accuracy: 0.9608 - val_loss: 0.0969 - val_accuracy: 0.9661\n",
            "Epoch 4/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.1141 - accuracy: 0.9624 - val_loss: 0.0997 - val_accuracy: 0.9675\n",
            "Epoch 5/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.1086 - accuracy: 0.9642 - val_loss: 0.0934 - val_accuracy: 0.9669\n",
            "Epoch 6/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.1054 - accuracy: 0.9636 - val_loss: 0.0908 - val_accuracy: 0.9665\n",
            "Epoch 7/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.1041 - accuracy: 0.9649 - val_loss: 0.0913 - val_accuracy: 0.9651\n",
            "Epoch 8/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0979 - accuracy: 0.9661 - val_loss: 0.0956 - val_accuracy: 0.9686\n",
            "Epoch 9/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0977 - accuracy: 0.9655 - val_loss: 0.1010 - val_accuracy: 0.9663\n",
            "Epoch 10/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0909 - accuracy: 0.9678 - val_loss: 0.0784 - val_accuracy: 0.9680\n",
            "Epoch 11/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0872 - accuracy: 0.9698 - val_loss: 0.0873 - val_accuracy: 0.9760\n",
            "Epoch 12/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0826 - accuracy: 0.9694 - val_loss: 0.0653 - val_accuracy: 0.9784\n",
            "Epoch 13/100\n",
            "473/473 [==============================] - 3s 5ms/step - loss: 0.0774 - accuracy: 0.9735 - val_loss: 0.0742 - val_accuracy: 0.9798\n",
            "Epoch 14/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0761 - accuracy: 0.9740 - val_loss: 0.0888 - val_accuracy: 0.9744\n",
            "Epoch 15/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0734 - accuracy: 0.9745 - val_loss: 0.0658 - val_accuracy: 0.9772\n",
            "Epoch 16/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0713 - accuracy: 0.9774 - val_loss: 0.0835 - val_accuracy: 0.9762\n",
            "Epoch 17/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0707 - accuracy: 0.9761 - val_loss: 0.0698 - val_accuracy: 0.9766\n",
            "Epoch 18/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0696 - accuracy: 0.9762 - val_loss: 0.0755 - val_accuracy: 0.9754\n",
            "Epoch 19/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0674 - accuracy: 0.9776 - val_loss: 0.0784 - val_accuracy: 0.9780\n",
            "Epoch 20/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0647 - accuracy: 0.9799 - val_loss: 0.0913 - val_accuracy: 0.9734\n",
            "Epoch 21/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0676 - accuracy: 0.9790 - val_loss: 0.0787 - val_accuracy: 0.9726\n",
            "Epoch 22/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0651 - accuracy: 0.9788 - val_loss: 0.0981 - val_accuracy: 0.9780\n",
            "Epoch 23/100\n",
            "473/473 [==============================] - 5s 11ms/step - loss: 0.0624 - accuracy: 0.9792 - val_loss: 0.0743 - val_accuracy: 0.9762\n",
            "Epoch 24/100\n",
            "473/473 [==============================] - 4s 9ms/step - loss: 0.0656 - accuracy: 0.9795 - val_loss: 0.0838 - val_accuracy: 0.9776\n",
            "Epoch 25/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0627 - accuracy: 0.9792 - val_loss: 0.0683 - val_accuracy: 0.9788\n",
            "Epoch 26/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0614 - accuracy: 0.9800 - val_loss: 0.0916 - val_accuracy: 0.9758\n",
            "Epoch 27/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0629 - accuracy: 0.9797 - val_loss: 0.0610 - val_accuracy: 0.9786\n",
            "Epoch 28/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0592 - accuracy: 0.9805 - val_loss: 0.0627 - val_accuracy: 0.9829\n",
            "Epoch 29/100\n",
            "473/473 [==============================] - 5s 10ms/step - loss: 0.0609 - accuracy: 0.9801 - val_loss: 0.0661 - val_accuracy: 0.9772\n",
            "Epoch 30/100\n",
            "473/473 [==============================] - 6s 12ms/step - loss: 0.0579 - accuracy: 0.9815 - val_loss: 0.0690 - val_accuracy: 0.9784\n",
            "Epoch 31/100\n",
            "473/473 [==============================] - 6s 13ms/step - loss: 0.0561 - accuracy: 0.9803 - val_loss: 0.0644 - val_accuracy: 0.9806\n",
            "Epoch 32/100\n",
            "473/473 [==============================] - 7s 16ms/step - loss: 0.0610 - accuracy: 0.9798 - val_loss: 0.0948 - val_accuracy: 0.9738\n",
            "Epoch 33/100\n",
            "473/473 [==============================] - 4s 9ms/step - loss: 0.0574 - accuracy: 0.9811 - val_loss: 0.0810 - val_accuracy: 0.9825\n",
            "Epoch 34/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0574 - accuracy: 0.9817 - val_loss: 0.0560 - val_accuracy: 0.9835\n",
            "Epoch 35/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0584 - accuracy: 0.9800 - val_loss: 0.0766 - val_accuracy: 0.9766\n",
            "Epoch 36/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0573 - accuracy: 0.9804 - val_loss: 0.0682 - val_accuracy: 0.9732\n",
            "Epoch 37/100\n",
            "473/473 [==============================] - 3s 5ms/step - loss: 0.0545 - accuracy: 0.9812 - val_loss: 0.0831 - val_accuracy: 0.9754\n",
            "Epoch 38/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0540 - accuracy: 0.9823 - val_loss: 0.0723 - val_accuracy: 0.9766\n",
            "Epoch 39/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0540 - accuracy: 0.9814 - val_loss: 0.0975 - val_accuracy: 0.9728\n",
            "Epoch 40/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0537 - accuracy: 0.9823 - val_loss: 0.0767 - val_accuracy: 0.9780\n",
            "Epoch 41/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0547 - accuracy: 0.9819 - val_loss: 0.0750 - val_accuracy: 0.9843\n",
            "Epoch 42/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0527 - accuracy: 0.9817 - val_loss: 0.0766 - val_accuracy: 0.9774\n",
            "Epoch 43/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0537 - accuracy: 0.9821 - val_loss: 0.0981 - val_accuracy: 0.9728\n",
            "Epoch 44/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0550 - accuracy: 0.9811 - val_loss: 0.0657 - val_accuracy: 0.9784\n",
            "Epoch 45/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0515 - accuracy: 0.9831 - val_loss: 0.0675 - val_accuracy: 0.9794\n",
            "Epoch 46/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0508 - accuracy: 0.9833 - val_loss: 0.0815 - val_accuracy: 0.9837\n",
            "Epoch 47/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0519 - accuracy: 0.9826 - val_loss: 0.0744 - val_accuracy: 0.9768\n",
            "Epoch 48/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0529 - accuracy: 0.9825 - val_loss: 0.0823 - val_accuracy: 0.9724\n",
            "Epoch 49/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0512 - accuracy: 0.9823 - val_loss: 0.0767 - val_accuracy: 0.9837\n",
            "Epoch 50/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0491 - accuracy: 0.9833 - val_loss: 0.0698 - val_accuracy: 0.9804\n",
            "Epoch 51/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0493 - accuracy: 0.9821 - val_loss: 0.0527 - val_accuracy: 0.9827\n",
            "Epoch 52/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0510 - accuracy: 0.9829 - val_loss: 0.0682 - val_accuracy: 0.9813\n",
            "Epoch 53/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0497 - accuracy: 0.9835 - val_loss: 0.0651 - val_accuracy: 0.9758\n",
            "Epoch 54/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0464 - accuracy: 0.9847 - val_loss: 0.0499 - val_accuracy: 0.9843\n",
            "Epoch 55/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0483 - accuracy: 0.9845 - val_loss: 0.0488 - val_accuracy: 0.9845\n",
            "Epoch 56/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0479 - accuracy: 0.9831 - val_loss: 0.0690 - val_accuracy: 0.9792\n",
            "Epoch 57/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0476 - accuracy: 0.9843 - val_loss: 0.0653 - val_accuracy: 0.9808\n",
            "Epoch 58/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0488 - accuracy: 0.9840 - val_loss: 0.0525 - val_accuracy: 0.9831\n",
            "Epoch 59/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0478 - accuracy: 0.9845 - val_loss: 0.0955 - val_accuracy: 0.9774\n",
            "Epoch 60/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0503 - accuracy: 0.9836 - val_loss: 0.0476 - val_accuracy: 0.9833\n",
            "Epoch 61/100\n",
            "473/473 [==============================] - 4s 8ms/step - loss: 0.0503 - accuracy: 0.9829 - val_loss: 0.0527 - val_accuracy: 0.9831\n",
            "Epoch 62/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0479 - accuracy: 0.9837 - val_loss: 0.0578 - val_accuracy: 0.9847\n",
            "Epoch 63/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0481 - accuracy: 0.9831 - val_loss: 0.0631 - val_accuracy: 0.9841\n",
            "Epoch 64/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0467 - accuracy: 0.9849 - val_loss: 0.0730 - val_accuracy: 0.9823\n",
            "Epoch 65/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0460 - accuracy: 0.9844 - val_loss: 0.0719 - val_accuracy: 0.9788\n",
            "Epoch 66/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0501 - accuracy: 0.9838 - val_loss: 0.0798 - val_accuracy: 0.9802\n",
            "Epoch 67/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0472 - accuracy: 0.9839 - val_loss: 0.0886 - val_accuracy: 0.9806\n",
            "Epoch 68/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0460 - accuracy: 0.9843 - val_loss: 0.0745 - val_accuracy: 0.9835\n",
            "Epoch 69/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0456 - accuracy: 0.9838 - val_loss: 0.0708 - val_accuracy: 0.9839\n",
            "Epoch 70/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0461 - accuracy: 0.9840 - val_loss: 0.0659 - val_accuracy: 0.9800\n",
            "Epoch 71/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0500 - accuracy: 0.9837 - val_loss: 0.0745 - val_accuracy: 0.9841\n",
            "Epoch 72/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0452 - accuracy: 0.9838 - val_loss: 0.0898 - val_accuracy: 0.9847\n",
            "Epoch 73/100\n",
            "473/473 [==============================] - 4s 9ms/step - loss: 0.0449 - accuracy: 0.9857 - val_loss: 0.0723 - val_accuracy: 0.9825\n",
            "Epoch 74/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0430 - accuracy: 0.9852 - val_loss: 0.0696 - val_accuracy: 0.9821\n",
            "Epoch 75/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0450 - accuracy: 0.9850 - val_loss: 0.0887 - val_accuracy: 0.9819\n",
            "Epoch 76/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0445 - accuracy: 0.9850 - val_loss: 0.0915 - val_accuracy: 0.9750\n",
            "Epoch 77/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0462 - accuracy: 0.9843 - val_loss: 0.0743 - val_accuracy: 0.9827\n",
            "Epoch 78/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0464 - accuracy: 0.9843 - val_loss: 0.0839 - val_accuracy: 0.9802\n",
            "Epoch 79/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0432 - accuracy: 0.9851 - val_loss: 0.0858 - val_accuracy: 0.9829\n",
            "Epoch 80/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0445 - accuracy: 0.9848 - val_loss: 0.0853 - val_accuracy: 0.9847\n",
            "Epoch 81/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0415 - accuracy: 0.9857 - val_loss: 0.0627 - val_accuracy: 0.9808\n",
            "Epoch 82/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0454 - accuracy: 0.9842 - val_loss: 0.0687 - val_accuracy: 0.9819\n",
            "Epoch 83/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0441 - accuracy: 0.9848 - val_loss: 0.0622 - val_accuracy: 0.9825\n",
            "Epoch 84/100\n",
            "473/473 [==============================] - 4s 8ms/step - loss: 0.0445 - accuracy: 0.9850 - val_loss: 0.0731 - val_accuracy: 0.9829\n",
            "Epoch 85/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0444 - accuracy: 0.9854 - val_loss: 0.0647 - val_accuracy: 0.9847\n",
            "Epoch 86/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0429 - accuracy: 0.9855 - val_loss: 0.0736 - val_accuracy: 0.9819\n",
            "Epoch 87/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0431 - accuracy: 0.9848 - val_loss: 0.0639 - val_accuracy: 0.9851\n",
            "Epoch 88/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0428 - accuracy: 0.9868 - val_loss: 0.0632 - val_accuracy: 0.9839\n",
            "Epoch 89/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0417 - accuracy: 0.9855 - val_loss: 0.0708 - val_accuracy: 0.9841\n",
            "Epoch 90/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0428 - accuracy: 0.9862 - val_loss: 0.0511 - val_accuracy: 0.9855\n",
            "Epoch 91/100\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0454 - accuracy: 0.9852 - val_loss: 0.0524 - val_accuracy: 0.9859\n",
            "Epoch 92/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0429 - accuracy: 0.9847 - val_loss: 0.0889 - val_accuracy: 0.9817\n",
            "Epoch 93/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0439 - accuracy: 0.9857 - val_loss: 0.0646 - val_accuracy: 0.9841\n",
            "Epoch 94/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0426 - accuracy: 0.9856 - val_loss: 0.0871 - val_accuracy: 0.9734\n",
            "Epoch 95/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0418 - accuracy: 0.9859 - val_loss: 0.0810 - val_accuracy: 0.9851\n",
            "Epoch 96/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0430 - accuracy: 0.9858 - val_loss: 0.0600 - val_accuracy: 0.9825\n",
            "Epoch 97/100\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0426 - accuracy: 0.9854 - val_loss: 0.0550 - val_accuracy: 0.9847\n",
            "Epoch 98/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0436 - accuracy: 0.9847 - val_loss: 0.0628 - val_accuracy: 0.9859\n",
            "Epoch 99/100\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0422 - accuracy: 0.9857 - val_loss: 0.0567 - val_accuracy: 0.9853\n",
            "Epoch 100/100\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0412 - accuracy: 0.9860 - val_loss: 0.0713 - val_accuracy: 0.9853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#classification report and\n",
        "y_pred_ann = model_ann.predict(X_test)\n",
        "y_pred_ann = (y_pred_ann > 0.5)\n",
        "accuracy_ann = accuracy_score(y_test, y_pred_ann)\n",
        "print(\"Test Accuracy of ANN Model:\", accuracy_ann * 100)\n",
        "print(classification_report(y_test, y_pred_ann))\n",
        "cm_ann = confusion_matrix(y_test, y_pred_ann)\n",
        "sns.heatmap(cm_ann, annot=True, fmt=\"d\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "48cH4OoYOOji",
        "outputId": "8ffcd14f-1907-438c-b568-55eae33ec047"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "158/158 [==============================] - 0s 2ms/step\n",
            "Test Accuracy of ANN Model: 98.39253820202421\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.98      2365\n",
            "           1       0.99      0.98      0.98      2674\n",
            "\n",
            "    accuracy                           0.98      5039\n",
            "   macro avg       0.98      0.98      0.98      5039\n",
            "weighted avg       0.98      0.98      0.98      5039\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Calculate ROC and AUC\n",
        "fpr_ann, tpr_ann, thresholds_ann = roc_curve(y_test, y_pred_ann)\n",
        "auc_ann = roc_auc_score(y_test, y_pred_ann)\n",
        "\n",
        "# Plot ROC and AUC\n",
        "plt.figure()\n",
        "plt.plot(fpr_ann, tpr_ann, label=\"ANN (AUC = %0.2f)\" % auc_ann)\n",
        "plt.plot([0, 1], [0, 1], \"k--\")\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC and AUC for ANN Model\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "BLrBDudqPcnn",
        "outputId": "b953e307-0925-4578-aac4-7fce7e64503a"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the ANN model\n",
        "ann_model_path = '/content/drive/My Drive/MyModels/ANN_model.h5'\n",
        "os.makedirs(os.path.dirname(ann_model_path), exist_ok=True)\n",
        "model_ann.save(ann_model_path)\n",
        "print(\"ANN model saved successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LF6WnkjOSCT",
        "outputId": "0d0c6536-cd93-4472-acae-370d46b55e84"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ANN model saved successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LSTM"
      ],
      "metadata": {
        "id": "cjOGJ9ouLkmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape the input data for LSTM\n",
        "X_trainL = np.reshape(X_trainL, (X_trainL.shape[0], X_trainL.shape[1], 1))\n",
        "X_valL = np.reshape(X_valL, (X_valL.shape[0], X_valL.shape[1], 1))\n",
        "X_testL = np.reshape(X_testL, (X_testL.shape[0], X_testL.shape[1], 1))"
      ],
      "metadata": {
        "id": "GxUwplybyakc"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM model\n",
        "model_lstm = Sequential([\n",
        "    LSTM(units=256, return_sequences=True, input_shape=(X_trainL.shape[1], X_trainL.shape[2])),\n",
        "    Dropout(0.2),\n",
        "    LSTM(units=128, return_sequences=True),\n",
        "    Dropout(0.2),\n",
        "    LSTM(units=64),\n",
        "    Dropout(0.2),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "wg3VEXrxTDn_"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training and compilation\n",
        "model_lstm.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "history_lstm=model_lstm.fit(X_trainL, y_trainL, epochs= 10, batch_size=128, validation_data=(X_valL, y_valL))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WclXaAW2SUGI",
        "outputId": "a8e443a8-7a41-4973-c93c-778570a56694"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "119/119 [==============================] - 29s 186ms/step - loss: 0.3599 - accuracy: 0.8658 - val_loss: 0.2009 - val_accuracy: 0.9421\n",
            "Epoch 2/10\n",
            "119/119 [==============================] - 22s 189ms/step - loss: 0.1861 - accuracy: 0.9364 - val_loss: 0.1783 - val_accuracy: 0.9327\n",
            "Epoch 3/10\n",
            "119/119 [==============================] - 20s 169ms/step - loss: 0.1489 - accuracy: 0.9469 - val_loss: 0.1330 - val_accuracy: 0.9514\n",
            "Epoch 4/10\n",
            "119/119 [==============================] - 21s 173ms/step - loss: 0.1316 - accuracy: 0.9539 - val_loss: 0.1336 - val_accuracy: 0.9464\n",
            "Epoch 5/10\n",
            "119/119 [==============================] - 25s 214ms/step - loss: 0.1255 - accuracy: 0.9541 - val_loss: 0.1235 - val_accuracy: 0.9498\n",
            "Epoch 6/10\n",
            "119/119 [==============================] - 20s 168ms/step - loss: 0.1174 - accuracy: 0.9579 - val_loss: 0.1264 - val_accuracy: 0.9464\n",
            "Epoch 7/10\n",
            "119/119 [==============================] - 21s 174ms/step - loss: 0.1164 - accuracy: 0.9571 - val_loss: 0.1188 - val_accuracy: 0.9553\n",
            "Epoch 8/10\n",
            "119/119 [==============================] - 21s 178ms/step - loss: 0.1162 - accuracy: 0.9553 - val_loss: 0.1124 - val_accuracy: 0.9524\n",
            "Epoch 9/10\n",
            "119/119 [==============================] - 21s 174ms/step - loss: 0.1049 - accuracy: 0.9640 - val_loss: 0.1012 - val_accuracy: 0.9657\n",
            "Epoch 10/10\n",
            "119/119 [==============================] - 27s 231ms/step - loss: 0.1004 - accuracy: 0.9657 - val_loss: 0.0956 - val_accuracy: 0.9673\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Classification Report and Confusion  Matrix\n",
        "y_pred_lstm=model_lstm.predict(X_testL)\n",
        "y_pred_lstm=(y_pred_lstm> 0.5)\n",
        "accuracy_lstm=accuracy_score(y_testL, y_pred_lstm)\n",
        "print(\"Test Accuracy of LSTM Model:\", accuracy_lstm * 100)\n",
        "print(classification_report(y_testL, y_pred_lstm))\n",
        "cm_lstm= confusion_matrix(y_testL, y_pred_lstm)\n",
        "sns.heatmap(cm_lstm, annot= True, fmt=\"d\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "6ptGYVVCRDob",
        "outputId": "32b13063-61dd-4581-a6b1-69516dd1fb46"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "158/158 [==============================] - 5s 26ms/step\n",
            "Test Accuracy of LSTM Model: 97.16213534431435\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.96      0.97      2365\n",
            "           1       0.96      0.98      0.97      2674\n",
            "\n",
            "    accuracy                           0.97      5039\n",
            "   macro avg       0.97      0.97      0.97      5039\n",
            "weighted avg       0.97      0.97      0.97      5039\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate ROC and AUC for LSTM model\n",
        "fpr_lstm, tpr_lstm, thresholds_lstm = roc_curve(y_testL, y_pred_lstm)\n",
        "auc_lstm = roc_auc_score(y_testL, y_pred_lstm)\n",
        "\n",
        "# Plot ROC and AUC for LSTM model\n",
        "plt.figure()\n",
        "plt.plot(fpr_lstm, tpr_lstm, label=\"LSTM (AUC = %0.2f)\" % auc_lstm)\n",
        "plt.plot([0, 1], [0, 1], \"k--\")\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC and AUC for LSTM Model\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "VkBb6jFpPlQM",
        "outputId": "2b69c508-d2ef-4570-e982-a84bc1d9114d"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the LSTM model\n",
        "lstm_model_path = '/content/drive/My Drive/MyModels/LSTM_model.h5'\n",
        "os.makedirs(os.path.dirname(lstm_model_path), exist_ok=True)\n",
        "model_lstm.save(lstm_model_path)\n",
        "print(\"LSTM model saved successfully.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l0pUdVXBPlFD",
        "outputId": "d4cd7dd6-93e0-4f27-a8be-4ea96be48b39"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LSTM model saved successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ensemble"
      ],
      "metadata": {
        "id": "-qmtdpXWLp9p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class EnsembleModel:\n",
        "    def __init__(self, ann_model_path, lstm_model_path):\n",
        "        self.model_ann = load_model(ann_model_path)\n",
        "        self.model_lstm = load_model(lstm_model_path)\n",
        "\n",
        "    def predict(self, X):\n",
        "        X_lstm = np.reshape(X, (X.shape[0], X.shape[1], 1))\n",
        "        y_pred_prob_ann = self.model_ann.predict(X)\n",
        "        y_pred_prob_lstm = self.model_lstm.predict(X_lstm)\n",
        "        y_pred_prob_ensemble = (y_pred_prob_ann + y_pred_prob_lstm) / 2\n",
        "        y_pred_ensemble = (y_pred_prob_ensemble > 0.5).astype(int)\n",
        "        return y_pred_ensemble"
      ],
      "metadata": {
        "id": "1pvRdrU9ybvp"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the ensemble model class\n",
        "ensemble_model = EnsembleModel(ann_model_path, lstm_model_path)\n",
        "ensemble_model_path = '/content/drive/My Drive/MyModels/ensemble_model.pkl'\n",
        "os.makedirs(os.path.dirname(ensemble_model_path), exist_ok=True)\n",
        "with open(ensemble_model_path, 'wb') as f:\n",
        "    joblib.dump(ensemble_model, f)"
      ],
      "metadata": {
        "id": "71rS7roPUZI2"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the ensemble model for 10 epochs\n",
        "ensemble_model.model_ann.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_val, y_val))\n",
        "ensemble_model.model_lstm.fit(X_trainL, y_trainL, epochs=5, batch_size=128, validation_data=(X_valL, y_valL))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GH20jonPkSoT",
        "outputId": "aca07245-1bbb-4f73-8269-e298ea2b05b2"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "473/473 [==============================] - 4s 5ms/step - loss: 0.0423 - accuracy: 0.9857 - val_loss: 0.0681 - val_accuracy: 0.9819\n",
            "Epoch 2/10\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0432 - accuracy: 0.9851 - val_loss: 0.0577 - val_accuracy: 0.9813\n",
            "Epoch 3/10\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0407 - accuracy: 0.9864 - val_loss: 0.1001 - val_accuracy: 0.9839\n",
            "Epoch 4/10\n",
            "473/473 [==============================] - 4s 8ms/step - loss: 0.0422 - accuracy: 0.9857 - val_loss: 0.0907 - val_accuracy: 0.9813\n",
            "Epoch 5/10\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0403 - accuracy: 0.9864 - val_loss: 0.0632 - val_accuracy: 0.9845\n",
            "Epoch 6/10\n",
            "473/473 [==============================] - 2s 4ms/step - loss: 0.0398 - accuracy: 0.9862 - val_loss: 0.0634 - val_accuracy: 0.9845\n",
            "Epoch 7/10\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0402 - accuracy: 0.9863 - val_loss: 0.0937 - val_accuracy: 0.9847\n",
            "Epoch 8/10\n",
            "473/473 [==============================] - 2s 5ms/step - loss: 0.0396 - accuracy: 0.9868 - val_loss: 0.0739 - val_accuracy: 0.9825\n",
            "Epoch 9/10\n",
            "473/473 [==============================] - 3s 6ms/step - loss: 0.0434 - accuracy: 0.9854 - val_loss: 0.0768 - val_accuracy: 0.9768\n",
            "Epoch 10/10\n",
            "473/473 [==============================] - 3s 7ms/step - loss: 0.0417 - accuracy: 0.9856 - val_loss: 0.0533 - val_accuracy: 0.9847\n",
            "Epoch 1/10\n",
            "119/119 [==============================] - 29s 212ms/step - loss: 0.0969 - accuracy: 0.9676 - val_loss: 0.0927 - val_accuracy: 0.9680\n",
            "Epoch 2/10\n",
            "119/119 [==============================] - 21s 174ms/step - loss: 0.0956 - accuracy: 0.9668 - val_loss: 0.0924 - val_accuracy: 0.9686\n",
            "Epoch 3/10\n",
            "119/119 [==============================] - 21s 174ms/step - loss: 0.1016 - accuracy: 0.9637 - val_loss: 0.0959 - val_accuracy: 0.9659\n",
            "Epoch 4/10\n",
            "119/119 [==============================] - 22s 184ms/step - loss: 0.0955 - accuracy: 0.9676 - val_loss: 0.0907 - val_accuracy: 0.9702\n",
            "Epoch 5/10\n",
            "119/119 [==============================] - 20s 167ms/step - loss: 0.0934 - accuracy: 0.9677 - val_loss: 0.1056 - val_accuracy: 0.9619\n",
            "Epoch 6/10\n",
            "119/119 [==============================] - 23s 196ms/step - loss: 0.0981 - accuracy: 0.9656 - val_loss: 0.1131 - val_accuracy: 0.9567\n",
            "Epoch 7/10\n",
            "119/119 [==============================] - 20s 168ms/step - loss: 0.0986 - accuracy: 0.9645 - val_loss: 0.0840 - val_accuracy: 0.9732\n",
            "Epoch 8/10\n",
            "119/119 [==============================] - 21s 176ms/step - loss: 0.0876 - accuracy: 0.9705 - val_loss: 0.0890 - val_accuracy: 0.9698\n",
            "Epoch 9/10\n",
            "119/119 [==============================] - 22s 189ms/step - loss: 0.0845 - accuracy: 0.9714 - val_loss: 0.0898 - val_accuracy: 0.9742\n",
            "Epoch 10/10\n",
            "119/119 [==============================] - 20s 169ms/step - loss: 0.0916 - accuracy: 0.9682 - val_loss: 0.0834 - val_accuracy: 0.9700\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7c44a05af070>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict on the test set\n",
        "y_pred_ensemble = ensemble_model.predict(X_test)\n",
        "\n",
        "# Calculate the test accuracy\n",
        "accuracy_ensemble = accuracy_score(y_test, y_pred_ensemble)\n",
        "print(\"Test Accuracy of Ensemble Model:\", accuracy_ensemble * 100)\n",
        "\n",
        "# Print the classification report\n",
        "print(classification_report(y_test, y_pred_ensemble))\n",
        "\n",
        "# Plot the confusion matrix\n",
        "cm_ensemble = confusion_matrix(y_test, y_pred_ensemble)\n",
        "sns.heatmap(cm_ensemble, annot=True, fmt=\"d\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "b3eYxj0FV217",
        "outputId": "328c40ed-1399-478c-83db-05e82bbc1348"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "158/158 [==============================] - 0s 2ms/step\n",
            "158/158 [==============================] - 5s 22ms/step\n",
            "Test Accuracy of Ensemble Model: 98.27346695772971\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98      2365\n",
            "           1       0.99      0.98      0.98      2674\n",
            "\n",
            "    accuracy                           0.98      5039\n",
            "   macro avg       0.98      0.98      0.98      5039\n",
            "weighted avg       0.98      0.98      0.98      5039\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#AUC- ROC Curve\n",
        "# Plot the ROC and AUC for the ensemble model\n",
        "plt.figure()\n",
        "plt.plot(fpr_ann, tpr_ann, label=\"ANN (AUC = %0.2f)\" % auc_ann, color='blue')\n",
        "plt.plot(fpr_lstm, tpr_lstm, label=\"LSTM (AUC = %0.2f)\" % auc_lstm, color='green')\n",
        "plt.plot(fpr_ensemble, tpr_ensemble, label=\"Ensemble (AUC = %0.2f)\" % auc_ensemble, color='purple')\n",
        "plt.plot([0, 1], [0, 1], \"k--\")\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "plt.title(\"ROC and AUC for Ensemble Model\")\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "p0qyS7fSkiYL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "7098e9de-b2a6-4821-828a-b2675b7b6940"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qum6mwYn-eAz"
      },
      "execution_count": 82,
      "outputs": []
    }
  ]
}