intrusion_detection(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Important Imports and Uploading Dataset**"
      ],
      "metadata": {
        "id": "Ah-cdchz0-pD"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5f9RGnHsziP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "693c5488-dff7-45fc-fafa-6bb6d7734453"
      },
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"intrusion detection system\n",
        "Automatically generated by Colaboratory.\n",
        "Original file is located at\n",
        "    https://colab.research.google.com/drive/1kyt50JZi3xZa8yRFvHBtmNPxHqaBSsHn\n",
        "\"\"\"\n",
        "\n",
        "#n -import important packages (this might change as we move forward with the project)\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import time\n",
        "\n",
        "#N - updated: libraries for Evaluate and measure the accuracy of the model\n",
        "from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score, confusion_matrix\n",
        "\n",
        "#n -libraries for the files in google drive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from google.colab import drive\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "#load dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/kddcup99_csv.csv')\n",
        "df.columns\n",
        "df.info() #n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 494020 entries, 0 to 494019\n",
            "Data columns (total 42 columns):\n",
            " #   Column                       Non-Null Count   Dtype  \n",
            "---  ------                       --------------   -----  \n",
            " 0   duration                     494020 non-null  int64  \n",
            " 1   protocol_type                494020 non-null  object \n",
            " 2   service                      494020 non-null  object \n",
            " 3   flag                         494020 non-null  object \n",
            " 4   src_bytes                    494020 non-null  int64  \n",
            " 5   dst_bytes                    494020 non-null  int64  \n",
            " 6   land                         494020 non-null  int64  \n",
            " 7   wrong_fragment               494020 non-null  int64  \n",
            " 8   urgent                       494020 non-null  int64  \n",
            " 9   hot                          494020 non-null  int64  \n",
            " 10  num_failed_logins            494020 non-null  int64  \n",
            " 11  logged_in                    494020 non-null  int64  \n",
            " 12  lnum_compromised             494020 non-null  int64  \n",
            " 13  lroot_shell                  494020 non-null  int64  \n",
            " 14  lsu_attempted                494020 non-null  int64  \n",
            " 15  lnum_root                    494020 non-null  int64  \n",
            " 16  lnum_file_creations          494020 non-null  int64  \n",
            " 17  lnum_shells                  494020 non-null  int64  \n",
            " 18  lnum_access_files            494020 non-null  int64  \n",
            " 19  lnum_outbound_cmds           494020 non-null  int64  \n",
            " 20  is_host_login                494020 non-null  int64  \n",
            " 21  is_guest_login               494020 non-null  int64  \n",
            " 22  count                        494020 non-null  int64  \n",
            " 23  srv_count                    494020 non-null  int64  \n",
            " 24  serror_rate                  494020 non-null  float64\n",
            " 25  srv_serror_rate              494020 non-null  float64\n",
            " 26  rerror_rate                  494020 non-null  float64\n",
            " 27  srv_rerror_rate              494020 non-null  float64\n",
            " 28  same_srv_rate                494020 non-null  float64\n",
            " 29  diff_srv_rate                494020 non-null  float64\n",
            " 30  srv_diff_host_rate           494020 non-null  float64\n",
            " 31  dst_host_count               494020 non-null  int64  \n",
            " 32  dst_host_srv_count           494020 non-null  int64  \n",
            " 33  dst_host_same_srv_rate       494020 non-null  float64\n",
            " 34  dst_host_diff_srv_rate       494020 non-null  float64\n",
            " 35  dst_host_same_src_port_rate  494020 non-null  float64\n",
            " 36  dst_host_srv_diff_host_rate  494020 non-null  float64\n",
            " 37  dst_host_serror_rate         494020 non-null  float64\n",
            " 38  dst_host_srv_serror_rate     494020 non-null  float64\n",
            " 39  dst_host_rerror_rate         494020 non-null  float64\n",
            " 40  dst_host_srv_rerror_rate     494020 non-null  float64\n",
            " 41  label                        494020 non-null  object \n",
            "dtypes: float64(15), int64(23), object(4)\n",
            "memory usage: 158.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Preprocessing**"
      ],
      "metadata": {
        "id": "QZSRxLZ71MEf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#n -check for any missing values\n",
        "print(df.isnull().sum())\n",
        "duplicates = df.duplicated()\n",
        "print('Number of duplicate entries:', duplicates.sum())\n",
        "\n",
        "#Remove duplicate rows\n",
        "df = df.drop_duplicates()\n",
        "print('Number of duplicate entries after removing:', df.duplicated().sum())\n",
        "df.label.value_counts()\n",
        "\n",
        "#AlAnoud AlJebreen -Convert categorical data into numerical data\n",
        "df = pd.get_dummies(df, columns=['protocol_type', 'service', 'flag', 'label'])\n",
        "\n",
        "#NS - Normalization of dataset\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "df_normalized = scaler.fit_transform(df.drop('label_normal', axis=1))\n",
        "df_normalized = pd.DataFrame(df_normalized, columns=df.drop('label_normal', axis=1).columns)\n",
        "df_normalized = pd.concat([df_normalized, df['label_normal']], axis=1)\n",
        "\n",
        "df.to_csv('newkddcup99.csv', index=False)\n",
        "\n",
        "#NS\n",
        "corr = df.corr()\n",
        "  \n",
        "plt.figure(figsize =(15, 12))\n",
        "  \n",
        "sns.heatmap(corr)\n",
        "  \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UUM-uC11TKbE",
        "outputId": "76c38d91-fff2-4f17-8220-cf2a1a2f8ab4"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "lnum_compromised               0\n",
            "lroot_shell                    0\n",
            "lsu_attempted                  0\n",
            "lnum_root                      0\n",
            "lnum_file_creations            0\n",
            "lnum_shells                    0\n",
            "lnum_access_files              0\n",
            "lnum_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",
            "label                          0\n",
            "dtype: int64\n",
            "Number of duplicate entries: 348435\n",
            "Number of duplicate entries after removing: 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "df.drop('is_guest_login', axis = 1, inplace = True)\n",
        "#NS - This variable is highly correlated with rerror_rate and should be ignored for analysis.\n",
        "df.drop('srv_rerror_rate', axis = 1, inplace = True)\n",
        "#NS - This variable is highly correlated with srv_serror_rate and should be ignored for analysis.\n",
        "df.drop('dst_host_srv_serror_rate', axis = 1, inplace = True)\n",
        "#NS - This variable is highly correlated with rerror_rate and should be ignored for analysis.\n",
        "df.drop('dst_host_serror_rate', axis = 1, inplace = True)\n",
        "#NS - This variable is highly correlated with srv_rerror_rate and should be ignored for analysis.\n",
        "df.drop('dst_host_rerror_rate', axis = 1, inplace = True)\n",
        "#NS - This variable is highly correlated with rerror_rate and should be ignored for analysis.\n",
        "df.drop('dst_host_srv_rerror_rate', axis = 1, inplace = True)\n",
        "#NS - This variable is highly correlated with srv_rerror_rate and should be ignored for analysis.\n",
        "df.drop('dst_host_same_srv_rate', axis = 1, inplace = True)\n",
        "df \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "LnvOzKbGTPd0",
        "outputId": "53a52cc7-489b-41eb-86c2-c5e7b375f4a3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        duration  src_bytes  dst_bytes  land  wrong_fragment  urgent  hot  \\\n",
              "0              0        181       5450     0               0       0    0   \n",
              "1              0        239        486     0               0       0    0   \n",
              "2              0        235       1337     0               0       0    0   \n",
              "3              0        219       1337     0               0       0    0   \n",
              "4              0        217       2032     0               0       0    0   \n",
              "...          ...        ...        ...   ...             ...     ...  ...   \n",
              "494015         0        310       1881     0               0       0    0   \n",
              "494016         0        282       2286     0               0       0    0   \n",
              "494017         0        203       1200     0               0       0    0   \n",
              "494018         0        291       1200     0               0       0    0   \n",
              "494019         0        219       1234     0               0       0    0   \n",
              "\n",
              "        num_failed_logins  logged_in  lnum_compromised  ...  label_phf  \\\n",
              "0                       0          1                 0  ...          0   \n",
              "1                       0          1                 0  ...          0   \n",
              "2                       0          1                 0  ...          0   \n",
              "3                       0          1                 0  ...          0   \n",
              "4                       0          1                 0  ...          0   \n",
              "...                   ...        ...               ...  ...        ...   \n",
              "494015                  0          1                 0  ...          0   \n",
              "494016                  0          1                 0  ...          0   \n",
              "494017                  0          1                 0  ...          0   \n",
              "494018                  0          1                 0  ...          0   \n",
              "494019                  0          1                 0  ...          0   \n",
              "\n",
              "        label_pod  label_portsweep  label_rootkit  label_satan  label_smurf  \\\n",
              "0               0                0              0            0            0   \n",
              "1               0                0              0            0            0   \n",
              "2               0                0              0            0            0   \n",
              "3               0                0              0            0            0   \n",
              "4               0                0              0            0            0   \n",
              "...           ...              ...            ...          ...          ...   \n",
              "494015          0                0              0            0            0   \n",
              "494016          0                0              0            0            0   \n",
              "494017          0                0              0            0            0   \n",
              "494018          0                0              0            0            0   \n",
              "494019          0                0              0            0            0   \n",
              "\n",
              "        label_spy  label_teardrop  label_warezclient  label_warezmaster  \n",
              "0               0               0                  0                  0  \n",
              "1               0               0                  0                  0  \n",
              "2               0               0                  0                  0  \n",
              "3               0               0                  0                  0  \n",
              "4               0               0                  0                  0  \n",
              "...           ...             ...                ...                ...  \n",
              "494015          0               0                  0                  0  \n",
              "494016          0               0                  0                  0  \n",
              "494017          0               0                  0                  0  \n",
              "494018          0               0                  0                  0  \n",
              "494019          0               0                  0                  0  \n",
              "\n",
              "[145585 rows x 134 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8743713-7133-40c3-a3a9-c6d77e9788d6\">\n",
              "    <div 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>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>lnum_compromised</th>\n",
              "      <th>...</th>\n",
              "      <th>label_phf</th>\n",
              "      <th>label_pod</th>\n",
              "      <th>label_portsweep</th>\n",
              "      <th>label_rootkit</th>\n",
              "      <th>label_satan</th>\n",
              "      <th>label_smurf</th>\n",
              "      <th>label_spy</th>\n",
              "      <th>label_teardrop</th>\n",
              "      <th>label_warezclient</th>\n",
              "      <th>label_warezmaster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>181</td>\n",
              "      <td>5450</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>239</td>\n",
              "      <td>486</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>235</td>\n",
              "      <td>1337</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>219</td>\n",
              "      <td>1337</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>217</td>\n",
              "      <td>2032</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494015</th>\n",
              "      <td>0</td>\n",
              "      <td>310</td>\n",
              "      <td>1881</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494016</th>\n",
              "      <td>0</td>\n",
              "      <td>282</td>\n",
              "      <td>2286</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494017</th>\n",
              "      <td>0</td>\n",
              "      <td>203</td>\n",
              "      <td>1200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494018</th>\n",
              "      <td>0</td>\n",
              "      <td>291</td>\n",
              "      <td>1200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>494019</th>\n",
              "      <td>0</td>\n",
              "      <td>219</td>\n",
              "      <td>1234</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>...</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>145585 rows × 134 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8743713-7133-40c3-a3a9-c6d77e9788d6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      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",
              "    [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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8743713-7133-40c3-a3a9-c6d77e9788d6 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-f8743713-7133-40c3-a3a9-c6d77e9788d6');\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",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Feature Selection**"
      ],
      "metadata": {
        "id": "YK1sbX9Q1S7x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#AlAnoud AlJebreen -Feature Selection PCA\n",
        "from sklearn.decomposition import PCA\n",
        "x = df.drop('label_normal', axis=1)\n",
        "y = df['label_normal']\n",
        "pca = PCA(n_components=3)\n",
        "x_pca = pca.fit_transform(x)\n",
        "\n",
        "#PCA plot\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.scatter(x_pca[:,0],x_pca[:,1], c=y, cmap='plasma')\n",
        "plt.xlabel('First Principal Component')\n",
        "plt.ylabel('Second Principal Component')\n",
        "plt.title('PCA')\n",
        "plt.show()\n",
        "\n",
        "#AlAnoud AlJebreen -Feature Selection RFE \n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.linear_model import LinearRegression\n",
        "x = df.drop('label_normal', axis=1)\n",
        "y = df['label_normal']\n",
        "model = LinearRegression()\n",
        "rfe = RFE(model, n_features_to_select=3)\n",
        "rfe.fit(x,y) \n",
        "\n",
        "#RFE plot\n",
        "plt.figure(figsize=(8,6))\n",
        "mask = rfe.support_\n",
        "plt.matshow(mask.reshape(1,-1), cmap='gray_r')\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Selected Features')\n",
        "plt.title('RFE')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "id": "vdjHQRrGTT_l",
        "outputId": "e7b80f7e-1c3b-4470-f4dd-db9ade072446"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x200 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Training and Testing Set Split**"
      ],
      "metadata": {
        "id": "8qUtHa6Y2RDj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#SK - Split the dataset into training and testing sets (40% for test data and 60% for train data)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size=0.4, random_state=42)\n",
        "#print(X_train.shape, X_test.shape, Y_train.shape, Y_test.shape)\n",
        "print(X_train.shape, X_test.shape, Y_train.shape, Y_test.shape) #print the shapes of the training and test sets \n",
        "\n",
        "#SK - Save the training and testing datasets into separate CSV files\n",
        "X_train.to_csv('train_data.csv', index=False)\n",
        "X_test.to_csv('test_data.csv', index=False)\n",
        "Y_train.to_csv('train_labels.csv', index=False)\n",
        "Y_test.to_csv('test_labels.csv', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TQpEVOwTZSd",
        "outputId": "b2ff658c-8af0-4f62-8069-57c4c922b2bb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(87351, 133) (58234, 133) (87351,) (58234,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Decision Tree Classifier**\n"
      ],
      "metadata": {
        "id": "imqYOynh2LTF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#SK - First Model: Decision Tree classifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtc = DecisionTreeClassifier()\n",
        "\n",
        "#SK - Train the model using the training data\n",
        "start = time.time()\n",
        "dtc.fit(X_train, Y_train)\n",
        "print(\"Processing time for Training using Decision Tree Classifier: %s seconds \" % (time.time() - start))\n",
        "\n",
        "#SK - Make predictions on the test data\n",
        "start = time.time()\n",
        "Y_pred = dtc.predict(X_test)\n",
        "print(\"Processing time for Testing using Decision Tree Classifier: %s seconds \" % (time.time() - start)) \n",
        "\n",
        "#NS - Perform cross-validation and get the mean accuracy score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(dtc, X_train, Y_train, cv=10)\n",
        "print(\"Cross-validation scores:\", scores)\n",
        "print(\"Mean cross-validation score:\", scores.mean())\n",
        "\n",
        "#SK - Calculate the accuracy, recall, precision.\n",
        "#NS -  Updated - calculate f1-score, confusion matrix\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "recall= recall_score(Y_test, Y_pred )\n",
        "precision= precision_score(Y_test, Y_pred )\n",
        "f1score = f1_score(Y_test, Y_pred)\n",
        "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
        "\n",
        "#SK - Print the results\n",
        "#NS - Updated the decimal required for better comparison\n",
        "print(\"Decision Tree Classifier:\")\n",
        "print(\"The accuracy of the model is : {:.4f}%\".format(accuracy*100))\n",
        "print(\"Recall = {:.4f} \" .format(recall*100))\n",
        "print(\"Precison = {:.4f} \".format(precision*100))\n",
        "print(\"F1-score: \", f1score)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLEnLc5rTs9r",
        "outputId": "f61fe52f-9e4c-49e4-b16d-7122de92db1a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing time for Training using Decision Tree Classifier: 3.3307273387908936 seconds \n",
            "Processing time for Testing using Decision Tree Classifier: 0.09370994567871094 seconds \n",
            "Cross-validation scores: [1.         0.99988552 1.         0.99965655 0.99977104 0.99988552\n",
            " 0.99988552 1.         0.99988552 0.99919863]\n",
            "Mean cross-validation score: 0.9998168288494561\n",
            "Decision Tree Classifier:\n",
            "The accuracy of the model is : 99.9742%\n",
            "Recall = 99.9659 \n",
            "Precison = 99.9915 \n",
            "F1-score:  0.9997866439086837\n",
            "Confusion Matrix:\n",
            " [[23074     3]\n",
            " [   12 35145]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Random Forest Classifier**"
      ],
      "metadata": {
        "id": "6oEVhyzY2a6Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#NS - Second Model: Random Forest\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#NS - Create a random forest classifier with 100 trees\n",
        "rfc = RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "#NS - Train the model using the training data\n",
        "start = time.time()\n",
        "rfc.fit(X_train, Y_train)\n",
        "print(\"Processing time for Training using Random Forest Classifier: %s seconds \" % (time.time() - start))\n",
        "\n",
        "#NS - Make predictions on the test data\n",
        "start = time.time()\n",
        "Y_pred = rfc.predict(X_test)\n",
        "print(\"Processing time for Testing using Random Forest Classifier: %s seconds \" % (time.time() - start))\n",
        "\n",
        "#NS - Perform cross-validation and get the mean accuracy score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores = cross_val_score(dtc, X_train, Y_train, cv=10)\n",
        "print(\"Cross-validation scores:\", scores)\n",
        "print(\"Mean cross-validation score:\", scores.mean())\n",
        "\n",
        "#NS - Calculate the accuracy, f1-score,recall, precision and confusion matrix\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "recall= recall_score(Y_test, Y_pred )\n",
        "precision= precision_score(Y_test, Y_pred )\n",
        "f1score = f1_score(Y_test, Y_pred)\n",
        "conf_matrix = confusion_matrix(Y_test, Y_pred)\n",
        "\n",
        "#NS - Print the results\n",
        "print(\"Random Forest Classifier:\")\n",
        "print(\"The accuracy of the model is : {:.4f}%\".format(accuracy*100))\n",
        "print(\"Recall = {:.4f} \" .format(recall*100))\n",
        "print(\"Precison = {:.4f} \".format(precision*100))\n",
        "print(\"F1-score: \", f1score)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3a378ZN_Tz_p",
        "outputId": "1bab6176-8257-45bf-9782-5ed14cba6faa"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing time for Training using Random Forest Classifier: 8.360581159591675 seconds \n",
            "Processing time for Testing using Random Forest Classifier: 0.7756664752960205 seconds \n",
            "Cross-validation scores: [1.         0.99988552 1.         0.99965655 0.99965655 0.99977104\n",
            " 0.99988552 1.         0.99988552 0.99919863]\n",
            "Mean cross-validation score: 0.9997939324556382\n",
            "Random Forest Classifier:\n",
            "The accuracy of the model is : 99.9828%\n",
            "Recall = 99.9943 \n",
            "Precison = 99.9772 \n",
            "F1-score:  0.9998577929465302\n",
            "Confusion Matrix:\n",
            " [[23069     8]\n",
            " [    2 35155]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Gaussian Naive Bayes Classifier**"
      ],
      "metadata": {
        "id": "jCN5amUs2gHE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#NS Third Model - Gaussian Naive Bayes\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "gbc = GaussianNB()\n",
        "\n",
        "#NS - Train the model using the training data\n",
        "start = time.time()\n",
        "gbc.fit(X_train, Y_train)\n",
        "print(\"Processing time for Training using Gaussian Naive Bayes Classifier: %s seconds \" % (time.time() - start))\n",
        "\n",
        "#NS - Make predictions on the test data\n",
        "start = time.time()\n",
        "Y_pred_gbc = gbc.predict(X_test)\n",
        "print(\"Processing time for Testing using Gaussian Naive Bayes Classifier: %s seconds \" % (time.time() - start))\n",
        "\n",
        "#NS - Perform cross-validation and get the mean accuracy score\n",
        "scores = cross_val_score(dtc, X_train, Y_train, cv=10)\n",
        "print(\"Cross-validation scores:\", scores)\n",
        "print(\"Mean cross-validation score:\", scores.mean())\n",
        "\n",
        "#NS - Calculate the accuracy, f1-score,recall, precision and confusion matrix\n",
        "accuracy = accuracy_score(Y_test, Y_pred_gbc)\n",
        "recall= recall_score(Y_test, Y_pred_gbc )\n",
        "precision= precision_score(Y_test, Y_pred_gbc )\n",
        "f1score = f1_score(Y_test, Y_pred_gbc)\n",
        "conf_matrix = confusion_matrix(Y_test, Y_pred_gbc)\n",
        "\n",
        "#NS - Print the results\n",
        "print(\"Gaussian Naive Bayes Classifier:\")\n",
        "print(\"The accuracy of the model is : {:.4f}%\".format(accuracy*100))\n",
        "print(\"Recall = {:.4f} \" .format(recall*100))\n",
        "print(\"Precison = {:.4f} \".format(precision*100))\n",
        "print(\"F1-score: \", f1score)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d79y9OxVT1Qx",
        "outputId": "4312b748-6d2f-4522-8768-eb50b3ed7453"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing time for Training using Gaussian Naive Bayes Classifier: 0.4157528877258301 seconds \n",
            "Processing time for Testing using Gaussian Naive Bayes Classifier: 0.2593040466308594 seconds \n",
            "Cross-validation scores: [0.99988553 0.99977104 1.         0.99942759 0.99988552 0.99988552\n",
            " 0.99988552 1.         0.99988552 0.99931311]\n",
            "Mean cross-validation score: 0.9997939337661004\n",
            "Gaussian Naive Bayes Classifier:\n",
            "The accuracy of the model is : 74.9184%\n",
            "Recall = 99.8009 \n",
            "Precison = 70.7071 \n",
            "F1-score:  0.8277188016041519\n",
            "Confusion Matrix:\n",
            " [[ 8541 14536]\n",
            " [   70 35087]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **XGBoost Classifier**"
      ],
      "metadata": {
        "id": "V-sCU7lX2pT4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#NS Fourth Model - XGBoost Classifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "xgb = XGBClassifier()\n",
        "\n",
        "#NS - Train the model using the training data\n",
        "start = time.time()\n",
        "xgb.fit(X_train, Y_train)\n",
        "print(\"Processing time for Training using XGBoost Classifier: %s seconds \" % (time.time() - start))\n",
        "\n",
        "#NS - Make predictions on the test data\n",
        "start = time.time()\n",
        "Y_pred_xgb = xgb.predict(X_test)\n",
        "print(\"Processing time for Testing using XGBoost Classifier: %s seconds \" % (time.time() - start))\n",
        "\n",
        "#NS - Perform cross-validation and get the mean accuracy score\n",
        "scores = cross_val_score(dtc, X_train, Y_train, cv=10)\n",
        "print(\"Cross-validation scores:\", scores)\n",
        "print(\"Mean cross-validation score:\", scores.mean())\n",
        "\n",
        "#NS - Calculate the accuracy, f1-score,recall and precision\n",
        "accuracy = accuracy_score(Y_test, Y_pred_xgb)\n",
        "recall= recall_score(Y_test, Y_pred_xgb )\n",
        "precision= precision_score(Y_test, Y_pred_xgb )\n",
        "f1score = f1_score(Y_test, Y_pred_xgb)\n",
        "conf_matrix = confusion_matrix(Y_test, Y_pred_xgb)\n",
        "\n",
        "#NS - Print the results\n",
        "print(\"XG Boost Classifier:\")\n",
        "print(\"The accuracy of the model is : {:.4f}%\".format(accuracy*100))\n",
        "print(\"Recall = {:.4f} \" .format(recall*100))\n",
        "print(\"Precison = {:.4f} \".format(precision*100))\n",
        "print(\"F1-score: \", f1score)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "df338rvIzXRe",
        "outputId": "f7943f08-1475-48cf-a9af-46ca7e81d732"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing time for Training using XGBoost Classifier: 66.21139740943909 seconds \n",
            "Processing time for Testing using XGBoost Classifier: 0.22092604637145996 seconds \n",
            "Cross-validation scores: [1.         1.         1.         0.99965655 0.99988552 0.99988552\n",
            " 0.99988552 1.         0.99977104 0.99931311]\n",
            "Mean cross-validation score: 0.9998397252432742\n",
            "XG Boost Classifier:\n",
            "The accuracy of the model is : 99.9725%\n",
            "Recall = 99.9772 \n",
            "Precison = 99.9772 \n",
            "F1-score:  0.9997724492988594\n",
            "Confusion Matrix:\n",
            " [[23069     8]\n",
            " [    8 35149]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualization of result metrics**"
      ],
      "metadata": {
        "id": "7tII_0J32w52"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "#NS - Define metrics for all models\n",
        "metrics = {\n",
        "    'Decision Tree': [0.7656, 0.6203, 1.00, 0.6304],\n",
        "    'Random Forest': [0.9998, 0.9997, 0.9999, 0.9998],\n",
        "    'Gaussian Naive Bayes': [0.8277, 0.7071, 0.9980, 0.7492],\n",
        "    'XGBoost': [0.9997, 0.9997, 0.9997, 0.9997]\n",
        "}\n",
        "\n",
        "#NS - Create a bar plot \n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "bar_width = 0.2\n",
        "opacity = 0.8\n",
        "index = np.arange(len(metrics))\n",
        "\n",
        "rects1 = ax.bar(index, metrics['Decision Tree'], bar_width, alpha=opacity, color='b', label='Decision Tree')\n",
        "rects2 = ax.bar(index + bar_width, metrics['Random Forest'], bar_width, alpha=opacity, color='g', label='Random Forest')\n",
        "rects3 = ax.bar(index + 2*bar_width, metrics['Gaussian Naive Bayes'], bar_width, alpha=opacity, color='r', label='Gaussian Naive Bayes')\n",
        "rects4 = ax.bar(index + 3*bar_width, metrics['XGBoost'], bar_width, alpha=opacity, color='c', label='XGBoost')\n",
        "\n",
        "ax.set_xlabel('Metrics')\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_title('Comparison of ML Models')\n",
        "ax.set_xticks(index + bar_width)\n",
        "ax.set_xticklabels(('F1-score', 'Precision', 'Recall', 'Accuracy'))\n",
        "ax.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "82_MYt3m0G6r",
        "outputId": "b3f89a35-f94d-44c9-b1da-d5e2dc549549"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}