Intrusion_Detection.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lightgbm in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (4.4.0)\n",
            "Requirement already satisfied: xgboost in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (1.6.2)\n",
            "Requirement already satisfied: tabulate in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (0.9.0)\n",
            "Requirement already satisfied: optuna in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (3.6.1)\n",
            "Requirement already satisfied: numpy>=1.17.0 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from lightgbm) (1.21.6)\n",
            "Requirement already satisfied: scipy in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from lightgbm) (1.7.3)\n",
            "Requirement already satisfied: sqlalchemy>=1.3.0 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (2.0.30)\n",
            "Requirement already satisfied: tqdm in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (4.66.4)\n",
            "Requirement already satisfied: colorlog in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (6.8.2)\n",
            "Requirement already satisfied: packaging>=20.0 in c:\\users\\abhis\\appdata\\roaming\\python\\python37\\site-packages (from optuna) (24.0)\n",
            "Requirement already satisfied: alembic>=1.5.0 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (1.12.1)\n",
            "Requirement already satisfied: PyYAML in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (6.0.1)\n",
            "Requirement already satisfied: importlib-metadata in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (6.7.0)\n",
            "Requirement already satisfied: typing-extensions>=4 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (4.7.1)\n",
            "Requirement already satisfied: importlib-resources in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (5.12.0)\n",
            "Requirement already satisfied: Mako in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (1.2.4)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from sqlalchemy>=1.3.0->optuna) (3.0.3)\n",
            "Requirement already satisfied: colorama in c:\\users\\abhis\\appdata\\roaming\\python\\python37\\site-packages (from colorlog->optuna) (0.4.6)\n",
            "Requirement already satisfied: zipp>=0.5 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from importlib-metadata->alembic>=1.5.0->optuna) (3.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from Mako->alembic>=1.5.0->optuna) (2.1.5)\n"
          ]
        }
      ],
      "source": [
        "!pip install lightgbm xgboost tabulate optuna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ZyqhSW6_lqED"
      },
      "outputs": [],
      "source": [
        "\n",
        "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",
        "import warnings\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.tree  import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier, GradientBoostingClassifier\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.feature_selection import RFE\n",
        "import itertools\n",
        "from xgboost import XGBClassifier\n",
        "from tabulate import tabulate\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import recall_score\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "0X7TVxSsoeEp"
      },
      "outputs": [],
      "source": [
        "train=pd.read_csv('./Train_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "IVZKboAzqOIB"
      },
      "outputs": [],
      "source": [
        "test=pd.read_csv('./Test_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ve4kwNdEtTgD",
        "outputId": "5d0d8af8-09dc-4e46-d869-9472578e98b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "ED2y_zFNtW6y",
        "outputId": "33e7efb8-3cb3-4e7a-b8ce-d68c30fe5e33"
      },
      "outputs": [
        {
          "data": {
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              "<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",
              "    </tr>\n",
              "    <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>"
            ],
            "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]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "GfQr_paCtbkI",
        "outputId": "6aae1c9d-34bf-4e06-fce2-85ad9d73249b"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.describe(include='object')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ls-_hXe1tl3X",
        "outputId": "bc8f9a5b-67ee-4f56-dcbb-389716d9b671"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "-6i0kvUxtve0"
      },
      "outputs": [],
      "source": [
        "total = train.shape[0]\n",
        "missing_columns = [col for col in train.columns if train[col].isnull().sum() > 0]\n",
        "for col in missing_columns:\n",
        "    null_count = train[col].isnull().sum()\n",
        "    per = (null_count/total) * 100\n",
        "    print(f\"{col}: {null_count} ({round(per, 3)}%)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ckgcGL3tt454",
        "outputId": "10b1b38e-2720-460c-9e8e-a3b6551c1a30"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of duplicate rows: 0\n"
          ]
        }
      ],
      "source": [
        "print(f\"Number of duplicate rows: {train.duplicated().sum()}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "h2ABI7twuFdm",
        "outputId": "f4e83bed-36da-4159-dedf-99849d2b485a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='class', ylabel='count'>"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.countplot(x=train['class'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lf-PtMKCuK8x",
        "outputId": "d67d03dd-c4e6-4d02-ade8-12915e1e501a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Class Distribution Training Set:\n",
            "normal     13449\n",
            "anomaly    11743\n",
            "Name: class, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('Class Distribution Training Set:')\n",
        "print(train['class'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Pb_VtfxQu2pM"
      },
      "outputs": [],
      "source": [
        "def le(df):\n",
        "    for col in df.columns:\n",
        "        if df[col].dtype == 'object':\n",
        "                label_encoder = LabelEncoder()\n",
        "                df[col] = label_encoder.fit_transform(df[col])\n",
        "\n",
        "le(train)\n",
        "le(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "qQHhym9mvCw_"
      },
      "outputs": [],
      "source": [
        "train.drop(['num_outbound_cmds'], axis=1, inplace=True)\n",
        "test.drop(['num_outbound_cmds'], axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "P_fwZayCvH1M"
      },
      "outputs": [],
      "source": [
        "X_train = train.drop(['class'], axis=1)\n",
        "Y_train = train['class']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-OOeJ7VvLas",
        "outputId": "e247a61a-2ccb-40ca-b79d-9a66028c7455"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['protocol_type',\n",
              " 'service',\n",
              " 'flag',\n",
              " 'src_bytes',\n",
              " 'dst_bytes',\n",
              " 'count',\n",
              " 'same_srv_rate',\n",
              " 'diff_srv_rate',\n",
              " 'dst_host_srv_count',\n",
              " 'dst_host_same_srv_rate']"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rfc = RandomForestClassifier()\n",
        "\n",
        "rfe = RFE(rfc, n_features_to_select=10)\n",
        "rfe = rfe.fit(X_train, Y_train)\n",
        "\n",
        "feature_map = [(i, v) for i, v in itertools.zip_longest(rfe.get_support(), X_train.columns)]\n",
        "selected_features = [v for i, v in feature_map if i==True]\n",
        "\n",
        "selected_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ByJFnDjhvPTQ"
      },
      "outputs": [],
      "source": [
        "X_train = X_train[selected_features]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "VO487mOsvjl3"
      },
      "outputs": [],
      "source": [
        "scale = StandardScaler()\n",
        "X_train = scale.fit_transform(X_train)\n",
        "test = scale.fit_transform(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "3oEJGQ7nvlrp"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, train_size=0.70, random_state=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SajFyKswPPOO"
      },
      "source": [
        "# SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "mMyHVxFNMppn",
        "outputId": "0e2d7e2d-37e8-4153-c425-7b7c1b9491e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(kernel='linear', random_state=0)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "svc = SVC(kernel=\"linear\",random_state=0)\n",
        "svc.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "RJeVokk9MpsP"
      },
      "outputs": [],
      "source": [
        "svc_y_pred = svc.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8WKag60MpvB",
        "outputId": "b6a656ee-8a92-44ba-8966-e4e70a5ea42c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9323895210373114"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "accuracy_score(y_test,svc_y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMI_aZklMpxS",
        "outputId": "08a560b5-05e2-43bd-adca-8d370d478185"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Score: 0.9356357037541114\n",
            "Test Score: 0.9323895210373114\n"
          ]
        }
      ],
      "source": [
        "svc_train, svc_test = svc.score(x_train , y_train), svc.score(x_test , y_test)\n",
        "\n",
        "print(f\"Training Score: {svc_train}\")\n",
        "print(f\"Test Score: {svc_test}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvSBcPN4Mp0Y",
        "outputId": "04cbf405-235e-4a8c-c264-96c47f268d87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "F1-score 0.9322310075600991\n"
          ]
        }
      ],
      "source": [
        "f1_score = metrics.f1_score(y_test,svc_y_pred,average='weighted')\n",
        "print(\"F1-score\", f1_score)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YalbTRC-PlCY"
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "kK78Fl_3OKyr",
        "outputId": "8f6dfb27-539e-446d-a8af-91e52478546e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50)"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier(n_estimators=50)\n",
        "clf.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "uKA6egtVOK1y"
      },
      "outputs": [],
      "source": [
        "clf_y_pred = clf.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gitScMcOOK5Y",
        "outputId": "4a58bb3c-27d9-40dc-ee83-0f05584e233a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9964276263561789\n"
          ]
        }
      ],
      "source": [
        "accuracy = accuracy_score(y_test,clf_y_pred)\n",
        "print(\"Accuracy:\", accuracy)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_dXMKh-OK89",
        "outputId": "0f049f64-67e8-4d84-ef18-cc802527473d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Score: 1.0\n",
            "Test Score: 0.9964276263561789\n"
          ]
        }
      ],
      "source": [
        "ran_train, ran_test = clf.score(x_train , y_train), clf.score(x_test , y_test)\n",
        "\n",
        "print(f\"Training Score: {ran_train}\")\n",
        "print(f\"Test Score: {ran_test}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuu_ZlYvMp5M",
        "outputId": "53daf637-bfa3-4b02-80f6-a206a4b757eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "F1-score 0.9964275909508982\n"
          ]
        }
      ],
      "source": [
        "f1_score = metrics.f1_score(y_test,clf_y_pred,average='weighted')\n",
        "print(\"F1-score\", f1_score)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jOJMy5B9Prfq"
      },
      "source": [
        "# Gradient Boost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "RKv8aibnOlha",
        "outputId": "10ff28ed-b0f2-4d05-e9af-24c8f12ece9c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GradientBoostingClassifier()"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "grd = GradientBoostingClassifier()\n",
        "grd.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "CZAHBKHJOlkt"
      },
      "outputs": [],
      "source": [
        "y_pred = grd.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-NoFLsEOlne",
        "outputId": "073d5ee0-09a7-4758-d2af-71121eda5835"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of Gradient Boost is:  99.27229425774014 %\n"
          ]
        }
      ],
      "source": [
        "print('Accuracy of Gradient Boost is: ', grd.score(x_test,y_test) * 100,'%')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IT9SQ2bpOlpg",
        "outputId": "f89be6b6-e897-4f5e-8d7b-7395f5f5172b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Score: 0.9947828059430646\n",
            "Test Score: 0.9927229425774015\n"
          ]
        }
      ],
      "source": [
        "grd_train, grd_test = grd.score(x_train , y_train), grd.score(x_test , y_test)\n",
        "\n",
        "print(f\"Training Score: {grd_train}\")\n",
        "print(f\"Test Score: {grd_test}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9XQKx3WPxG2"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Ka_VFXcNwDDC"
      },
      "outputs": [],
      "source": [
        "import time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3L9Si6owHUj",
        "outputId": "547f52f6-721b-4968-8967-f6e0f4eb7afa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training time:  0.057062387466430664\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "clfl = LogisticRegression(max_iter = 1200000)\n",
        "start_time = time.time()\n",
        "clfl.fit(x_train, y_train.values.ravel())\n",
        "end_time = time.time()\n",
        "print(\"Training time: \", end_time-start_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZjMkWsIwKli",
        "outputId": "50c0bbb6-eee1-4504-a9b8-e5acf12c7fc7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Testing time:  0.002354860305786133\n"
          ]
        }
      ],
      "source": [
        "start_time = time.time()\n",
        "y_test_pred = clfl.predict(x_train)\n",
        "end_time = time.time()\n",
        "print(\"Testing time: \", end_time-start_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "GYDU9GCdwOw0",
        "outputId": "78c3d2e6-1fa6-479f-85ea-44c4105575d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42)"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lg_model = LogisticRegression(random_state = 42)\n",
        "lg_model.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8s8mkAwywRps",
        "outputId": "b718d208-fc67-49a4-eeca-e1f1d133c3ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Score: 0.9287739593966202\n",
            "Test Score: 0.9231278115903678\n"
          ]
        }
      ],
      "source": [
        "lg_train, lg_test = lg_model.score(x_train , y_train), lg_model.score(x_test , y_test)\n",
        "\n",
        "print(f\"Training Score: {lg_train}\")\n",
        "print(f\"Test Score: {lg_test}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDrQ5jlIP8CO",
        "outputId": "44ad41c7-66ed-439d-bc95-213446af6a25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of logistic Regression is:  92.31278115903679 %\n"
          ]
        }
      ],
      "source": [
        "print('Accuracy of logistic Regression is: ', lg_model.score(x_test,y_test) * 100,'%')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2tYO6ajwUz6",
        "outputId": "d15d7ab7-7056-4850-cb99-1d87049aaeeb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: optuna in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (3.6.1)\n",
            "Requirement already satisfied: numpy in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (1.21.6)\n",
            "Requirement already satisfied: packaging>=20.0 in c:\\users\\abhis\\appdata\\roaming\\python\\python37\\site-packages (from optuna) (24.0)\n",
            "Requirement already satisfied: PyYAML in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (6.0.1)\n",
            "Requirement already satisfied: alembic>=1.5.0 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (1.12.1)\n",
            "Requirement already satisfied: sqlalchemy>=1.3.0 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (2.0.30)\n",
            "Requirement already satisfied: colorlog in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (6.8.2)\n",
            "Requirement already satisfied: tqdm in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from optuna) (4.66.4)\n",
            "Requirement already satisfied: importlib-resources in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (5.12.0)\n",
            "Requirement already satisfied: importlib-metadata in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (6.7.0)\n",
            "Requirement already satisfied: typing-extensions>=4 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (4.7.1)\n",
            "Requirement already satisfied: Mako in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from alembic>=1.5.0->optuna) (1.2.4)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from sqlalchemy>=1.3.0->optuna) (3.0.3)\n",
            "Requirement already satisfied: colorama in c:\\users\\abhis\\appdata\\roaming\\python\\python37\\site-packages (from colorlog->optuna) (0.4.6)\n",
            "Requirement already satisfied: zipp>=0.5 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from importlib-metadata->alembic>=1.5.0->optuna) (3.15.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.9.2 in c:\\users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages (from Mako->alembic>=1.5.0->optuna) (2.1.5)\n"
          ]
        }
      ],
      "source": [
        "!pip install optuna"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "htjHNT7swhtv"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\abhis\\anaconda3\\envs\\new_environment\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        }
      ],
      "source": [
        "import optuna\n",
        "optuna.logging.set_verbosity(optuna.logging.WARNING)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeKl1XrnQSBe"
      },
      "source": [
        "# KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "lhb2tw4owuK9"
      },
      "outputs": [],
      "source": [
        "\n",
        "def objective(trial):\n",
        "    n_neighbors = trial.suggest_int('KNN_n_neighbors', 2, 16, log=False)\n",
        "    classifier_obj = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
        "    classifier_obj.fit(x_train, y_train)\n",
        "    accuracy = classifier_obj.score(x_test, y_test)\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vWNK8tSwwYv",
        "outputId": "123ac82b-0769-4066-83b4-a4b7110cdeb2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "FrozenTrial(number=0, state=TrialState.COMPLETE, values=[0.9797565493516803], datetime_start=datetime.datetime(2024, 6, 16, 16, 0, 8, 810403), datetime_complete=datetime.datetime(2024, 6, 16, 16, 0, 9, 535712), params={'KNN_n_neighbors': 15}, user_attrs={}, system_attrs={}, intermediate_values={}, distributions={'KNN_n_neighbors': IntDistribution(high=16, log=False, low=2, step=1)}, trial_id=0, value=None)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "study_KNN = optuna.create_study(direction='maximize')\n",
        "study_KNN.optimize(objective, n_trials=1)\n",
        "print(study_KNN.best_trial)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6dnp1fZwz6s",
        "outputId": "d692223c-9288-45e7-8fcb-64f9e28523e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Score: 0.9807190654417602\n",
            "Test Score: 0.9797565493516803\n"
          ]
        }
      ],
      "source": [
        "\n",
        "KNN_model = KNeighborsClassifier(n_neighbors=study_KNN.best_trial.params['KNN_n_neighbors'])\n",
        "KNN_model.fit(x_train, y_train)\n",
        "\n",
        "KNN_train, KNN_test = KNN_model.score(x_train, y_train), KNN_model.score(x_test, y_test)\n",
        "\n",
        "print(f\"Train Score: {KNN_train}\")\n",
        "print(f\"Test Score: {KNN_test}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRY7sA2fQVfS"
      },
      "source": [
        "# Decesion Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mi95brTYw4Vo",
        "outputId": "b86a3950-2d85-4695-b194-84816019dd76"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training time:  0.015631675720214844\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "clfd = DecisionTreeClassifier(criterion =\"entropy\", max_depth = 4)\n",
        "start_time = time.time()\n",
        "clfd.fit(x_train, y_train.values.ravel())\n",
        "end_time = time.time()\n",
        "print(\"Training time: \", end_time-start_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ep6k89_Cw9-g",
        "outputId": "a0f614e4-96aa-4b98-ba63-f8afc2259666"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Testing time:  0.0029904842376708984\n"
          ]
        }
      ],
      "source": [
        "start_time = time.time()\n",
        "y_test_pred = clfd.predict(x_train)\n",
        "end_time = time.time()\n",
        "print(\"Testing time: \", end_time-start_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "ArdBwJ4OxAE6"
      },
      "outputs": [],
      "source": [
        "def objective(trial):\n",
        "    dt_max_depth = trial.suggest_int('dt_max_depth', 2, 32, log=False)\n",
        "    dt_max_features = trial.suggest_int('dt_max_features', 2, 10, log=False)\n",
        "    classifier_obj = DecisionTreeClassifier(max_features = dt_max_features, max_depth = dt_max_depth)\n",
        "    classifier_obj.fit(x_train, y_train)\n",
        "    accuracy = classifier_obj.score(x_test, y_test)\n",
        "    return accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJczR7DvxEY_",
        "outputId": "047ece85-2a93-4da1-9ba2-929c4e864c10"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "FrozenTrial(number=17, state=TrialState.COMPLETE, values=[0.9960306959513099], datetime_start=datetime.datetime(2024, 6, 16, 16, 0, 12, 268490), datetime_complete=datetime.datetime(2024, 6, 16, 16, 0, 12, 301934), params={'dt_max_depth': 15, 'dt_max_features': 8}, user_attrs={}, system_attrs={}, intermediate_values={}, distributions={'dt_max_depth': IntDistribution(high=32, log=False, low=2, step=1), 'dt_max_features': IntDistribution(high=10, log=False, low=2, step=1)}, trial_id=17, value=None)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "study_dt = optuna.create_study(direction='maximize')\n",
        "study_dt.optimize(objective, n_trials=30)\n",
        "print(study_dt.best_trial)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z79ghBdkxG1T",
        "outputId": "3af45179-e0cb-474e-f5ba-0bdfebb2f71d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train Score: 0.9999432913689463\n",
            "Test Score: 0.9945752844667901\n"
          ]
        }
      ],
      "source": [
        "#DTree\n",
        "dt = DecisionTreeClassifier(max_features = study_dt.best_trial.params['dt_max_features'], max_depth = study_dt.best_trial.params['dt_max_depth'])\n",
        "dt.fit(x_train, y_train)\n",
        "\n",
        "dt_train, dt_test = dt.score(x_train, y_train), dt.score(x_test, y_test)\n",
        "\n",
        "print(f\"Train Score: {dt_train}\")\n",
        "print(f\"Test Score: {dt_test}\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Plotting different Models and their accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LM-QzE1BxeTI",
        "outputId": "d6a9883c-2e9c-4ccc-e59a-ae3bc65640c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "╒═════════════════════╤═══════════════╤══════════════╕\n",
            "│ Model               │   Train Score │   Test Score │\n",
            "╞═════════════════════╪═══════════════╪══════════════╡\n",
            "│ KNN                 │      0.980719 │     0.979757 │\n",
            "├─────────────────────┼───────────────┼──────────────┤\n",
            "│ Logistic Regression │      0.928774 │     0.923128 │\n",
            "├─────────────────────┼───────────────┼──────────────┤\n",
            "│ Decision Tree       │      0.999943 │     0.994575 │\n",
            "├─────────────────────┼───────────────┼──────────────┤\n",
            "│ SVC                 │      0.935636 │     0.93239  │\n",
            "├─────────────────────┼───────────────┼──────────────┤\n",
            "│ Random Forest       │      1        │     0.996428 │\n",
            "├─────────────────────┼───────────────┼──────────────┤\n",
            "│ Gradient            │      0.994783 │     0.992723 │\n",
            "╘═════════════════════╧═══════════════╧══════════════╛\n"
          ]
        }
      ],
      "source": [
        "data = [[\"KNN\", KNN_train, KNN_test],\n",
        "        [\"Logistic Regression\", lg_train, lg_test],\n",
        "        [\"Decision Tree\", dt_train, dt_test],\n",
        "        [\"SVC\", svc_train, svc_test ],\n",
        "        [\"Random Forest\", ran_train, ran_test],\n",
        "        [\"Gradient\", grd_train, grd_test]]\n",
        "\n",
        "col_names = [\"Model\", \"Train Score\", \"Test Score\"]\n",
        "print(tabulate(data, headers=col_names, tablefmt=\"fancy_grid\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "gaMJm8Exxh4N"
      },
      "outputs": [],
      "source": [
        "\n",
        "SEED = 42\n",
        "\n",
        "# Decision Tree Model\n",
        "dtc = DecisionTreeClassifier()\n",
        "\n",
        "# KNN\n",
        "knn = KNeighborsClassifier()\n",
        "\n",
        "# LOGISTIC REGRESSION MODEL\n",
        "\n",
        "lr = LogisticRegression()\n",
        "\n",
        "svc = SVC()\n",
        "grd=GradientBoostingClassifier()\n",
        "ran=RandomForestClassifier(n_estimators=50)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "AdxAGDJ9yE9J"
      },
      "outputs": [],
      "source": [
        "\n",
        "models = {}\n",
        "models['KNeighborsClassifier']= knn\n",
        "models['LogisticRegression']= lr\n",
        "models['DecisionTreeClassifier']= dtc\n",
        "models['SVC']=svc\n",
        "models['GradientBostingClassifier']=grd\n",
        "models['RandomForest']=ran"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "2B6aNSqtyHME"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = {}\n",
        "for name in models:\n",
        "  scores[name]={}\n",
        "  for scorer in ['precision','recall']:\n",
        "    scores[name][scorer] = cross_val_score(models[name], x_train, y_train, cv=10, scoring=scorer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "62ti0uvoyQf2",
        "outputId": "84a37244-60f4-45f1-94c3-ca5f6e1488f7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------- KNeighborsClassifier Model Validation ---------------\n",
            "Mean precision: \n",
            " 98.45 % +- 0.48\n",
            "\n",
            "Mean recall: \n",
            " 98.24 % +- 0.54\n",
            "\n",
            "---------------- LogisticRegression Model Validation ----------------\n",
            "Mean precision: \n",
            " 91.35 % +- 0.57\n",
            "\n",
            "Mean recall: \n",
            " 95.72 % +- 0.67\n",
            "\n",
            "-------------- DecisionTreeClassifier Model Validation --------------\n",
            "Mean precision: \n",
            " 99.51 % +- 0.2\n",
            "\n",
            "Mean recall: \n",
            " 99.55 % +- 0.2\n",
            "\n",
            "------------------------ SVC Model Validation ------------------------\n",
            "Mean precision: \n",
            " 95.74 % +- 0.59\n",
            "\n",
            "Mean recall: \n",
            " 98.34 % +- 0.43\n",
            "\n",
            "------------- GradientBostingClassifier Model Validation -------------\n",
            "Mean precision: \n",
            " 99.14 % +- 0.31\n",
            "\n",
            "Mean recall: \n",
            " 99.52 % +- 0.21\n",
            "\n",
            "------------------- RandomForest Model Validation -------------------\n",
            "Mean precision: \n",
            " 99.59 % +- 0.17\n",
            "\n",
            "Mean recall: \n",
            " 99.82 % +- 0.13\n",
            "\n"
          ]
        }
      ],
      "source": [
        "def line(name):\n",
        "  return '-'*(25-len(name)//2)\n",
        "\n",
        "for name in models:\n",
        "  print(line(name), name, 'Model Validation', line(name))\n",
        "\n",
        "  for scorer in ['precision','recall']:\n",
        "    mean = round(np.mean(scores[name][scorer])*100,2)\n",
        "    stdev = round(np.std(scores[name][scorer])*100,2)\n",
        "    print (\"Mean {}:\".format(scorer),\"\\n\", mean,\"%\", \"+-\",stdev)\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "CugfkzSSyauC",
        "outputId": "3b58d0be-fae8-45aa-9524-933bea8a9401"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 2400x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for name in models:\n",
        "    for scorer in ['precision','recall']:\n",
        "        scores[name][scorer] = scores[name][scorer].mean()\n",
        "scores=pd.DataFrame(scores).swapaxes(\"index\", \"columns\")*100\n",
        "scores.plot(kind = \"bar\",  ylim=[80,100], figsize=(24,6), rot=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "Md0KynExyim9"
      },
      "outputs": [],
      "source": [
        "models = {}\n",
        "models['KNN']= knn\n",
        "models['LR']= lr\n",
        "models['DC']= dtc\n",
        "models['SVC']=svc\n",
        "models['GRD']=grd\n",
        "models['Rf']=ran"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ur5VbZYjylhn",
        "outputId": "c6b1c003-ada2-487c-f066-d21dc98cf287"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predictions complete.\n"
          ]
        }
      ],
      "source": [
        "preds={}\n",
        "for name in models:\n",
        "    models[name].fit(x_train, y_train)\n",
        "    preds[name] = models[name].predict(x_test)\n",
        "print(\"Predictions complete.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKxg8gAHyoy_",
        "outputId": "ab14ee94-bfba-4f62-86fe-f13b92bf3b44"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "------------------------ KNN Model Testing ------------------------\n",
            "[[3435   63]\n",
            " [  65 3995]]\n",
            "------------------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      normal       0.98      0.98      0.98      3498\n",
            "     anamoly       0.98      0.98      0.98      4060\n",
            "\n",
            "    accuracy                           0.98      7558\n",
            "   macro avg       0.98      0.98      0.98      7558\n",
            "weighted avg       0.98      0.98      0.98      7558\n",
            "\n",
            "------------------------ LR Model Testing ------------------------\n",
            "[[3127  371]\n",
            " [ 210 3850]]\n",
            "------------------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      normal       0.94      0.89      0.91      3498\n",
            "     anamoly       0.91      0.95      0.93      4060\n",
            "\n",
            "    accuracy                           0.92      7558\n",
            "   macro avg       0.92      0.92      0.92      7558\n",
            "weighted avg       0.92      0.92      0.92      7558\n",
            "\n",
            "------------------------ DC Model Testing ------------------------\n",
            "[[3483   15]\n",
            " [  27 4033]]\n",
            "------------------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      normal       0.99      1.00      0.99      3498\n",
            "     anamoly       1.00      0.99      0.99      4060\n",
            "\n",
            "    accuracy                           0.99      7558\n",
            "   macro avg       0.99      0.99      0.99      7558\n",
            "weighted avg       0.99      0.99      0.99      7558\n",
            "\n",
            "------------------------ SVC Model Testing ------------------------\n",
            "[[3338  160]\n",
            " [  60 4000]]\n",
            "------------------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      normal       0.98      0.95      0.97      3498\n",
            "     anamoly       0.96      0.99      0.97      4060\n",
            "\n",
            "    accuracy                           0.97      7558\n",
            "   macro avg       0.97      0.97      0.97      7558\n",
            "weighted avg       0.97      0.97      0.97      7558\n",
            "\n",
            "------------------------ GRD Model Testing ------------------------\n",
            "[[3473   25]\n",
            " [  30 4030]]\n",
            "------------------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      normal       0.99      0.99      0.99      3498\n",
            "     anamoly       0.99      0.99      0.99      4060\n",
            "\n",
            "    accuracy                           0.99      7558\n",
            "   macro avg       0.99      0.99      0.99      7558\n",
            "weighted avg       0.99      0.99      0.99      7558\n",
            "\n",
            "------------------------ Rf Model Testing ------------------------\n",
            "[[3483   15]\n",
            " [  13 4047]]\n",
            "------------------------\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      normal       1.00      1.00      1.00      3498\n",
            "     anamoly       1.00      1.00      1.00      4060\n",
            "\n",
            "    accuracy                           1.00      7558\n",
            "   macro avg       1.00      1.00      1.00      7558\n",
            "weighted avg       1.00      1.00      1.00      7558\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def line(name,sym=\"-\"):\n",
        "    return sym*(25-len(name)//2)\n",
        "target_names=[\"normal\",\"anamoly\"]\n",
        "for name in models:\n",
        "    print(line(name), name, 'Model Testing', line(name))\n",
        "    print(confusion_matrix(y_test, preds[name]))\n",
        "    print(line(name,'-'))\n",
        "    print(classification_report(y_test, preds[name], target_names=target_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "ho4cWij1yxgC",
        "outputId": "4c838b0b-f4fe-4701-de9f-e2bfd3049791"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "f1s = {}\n",
        "for name in models:\n",
        "    f1s[name]=f1_score(y_test, preds[name])\n",
        "f1s=pd.DataFrame(f1s.values(),index=f1s.keys(),columns=[\"F1-score\"])*100\n",
        "f1s.plot(kind = \"bar\",  ylim=[80,100], figsize=(10,6), rot=0)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLtUfG0a2Xr7",
        "outputId": "72353be4-2e23-4d15-f760-6ad70b85f9c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "KNN    98.423257\n",
              "LR     92.983939\n",
              "DC     99.481993\n",
              "SVC    97.323601\n",
              "GRD    99.322243\n",
              "Rf     99.655257\n",
              "Name: F1-score, dtype: float64"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "f1s['F1-score']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Deep learning Approach"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {},
      "outputs": [],
      "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",
        "import warnings\n",
        "from sklearn import tree\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier, GradientBoostingClassifier\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from lightgbm import LGBMClassifier\n",
        "from sklearn.feature_selection import RFE\n",
        "import itertools\n",
        "from xgboost import XGBClassifier\n",
        "from tabulate import tabulate\n",
        "from sklearn.metrics import accuracy_score, f1_score, recall_score, confusion_matrix, classification_report\n",
        "from sklearn import metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "train = pd.read_csv('./Train_data.csv')\n",
        "test = pd.read_csv('./Test_data.csv')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Data preprocessing (assuming this is defined earlier in the notebook)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "def le(df):\n",
        "    for col in df.columns:\n",
        "        if df[col].dtype == 'object':\n",
        "            label_encoder = LabelEncoder()\n",
        "            df[col] = label_encoder.fit_transform(df[col])\n",
        "\n",
        "le(train)\n",
        "le(test)\n",
        "train.drop(['num_outbound_cmds'], axis=1, inplace=True)\n",
        "test.drop(['num_outbound_cmds'], axis=1, inplace=True)\n",
        "\n",
        "X_train = train.drop(['class'], axis=1)\n",
        "Y_train = train['class']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Feature Selection using RFE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "rfc = RandomForestClassifier()\n",
        "rfe = RFE(rfc, n_features_to_select=10)\n",
        "rfe = rfe.fit(X_train, Y_train)\n",
        "\n",
        "selected_features = [v for i, v in itertools.zip_longest(rfe.get_support(), X_train.columns) if i==True]\n",
        "X_train = X_train[selected_features]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Standardize features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {},
      "outputs": [],
      "source": [
        "scale = StandardScaler()\n",
        "X_train = scale.fit_transform(X_train)\n",
        "test = scale.fit_transform(test)\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, train_size=0.70, random_state=2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "num_classes = len(np.unique(y_train))\n",
        "y_train_cat = tf.keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes)\n",
        "\n",
        "# Define the neural network model\n",
        "model = Sequential()\n",
        "model.add(Dense(64, input_dim=x_train.shape[1], activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(16, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 64)                704       \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 16)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 2)                 34        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,346\n",
            "Trainable params: 3,346\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Compile and train the model "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "441/441 [==============================] - 3s 5ms/step - loss: 0.3480 - accuracy: 0.8552 - val_loss: 0.1515 - val_accuracy: 0.9495\n",
            "Epoch 2/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.2065 - accuracy: 0.9312 - val_loss: 0.1334 - val_accuracy: 0.9563\n",
            "Epoch 3/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1737 - accuracy: 0.9415 - val_loss: 0.1193 - val_accuracy: 0.9643\n",
            "Epoch 4/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1710 - accuracy: 0.9445 - val_loss: 0.1120 - val_accuracy: 0.9612\n",
            "Epoch 5/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1589 - accuracy: 0.9489 - val_loss: 0.1115 - val_accuracy: 0.9643\n",
            "Epoch 6/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1535 - accuracy: 0.9489 - val_loss: 0.1033 - val_accuracy: 0.9671\n",
            "Epoch 7/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1451 - accuracy: 0.9529 - val_loss: 0.1002 - val_accuracy: 0.9702\n",
            "Epoch 8/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1427 - accuracy: 0.9553 - val_loss: 0.1008 - val_accuracy: 0.9677\n",
            "Epoch 9/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1383 - accuracy: 0.9562 - val_loss: 0.0934 - val_accuracy: 0.9697\n",
            "Epoch 10/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1348 - accuracy: 0.9573 - val_loss: 0.0943 - val_accuracy: 0.9688\n",
            "Epoch 11/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1339 - accuracy: 0.9593 - val_loss: 0.0908 - val_accuracy: 0.9694\n",
            "Epoch 12/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1300 - accuracy: 0.9576 - val_loss: 0.0916 - val_accuracy: 0.9685\n",
            "Epoch 13/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1273 - accuracy: 0.9599 - val_loss: 0.0920 - val_accuracy: 0.9711\n",
            "Epoch 14/50\n",
            "441/441 [==============================] - 2s 6ms/step - loss: 0.1240 - accuracy: 0.9612 - val_loss: 0.0926 - val_accuracy: 0.9674\n",
            "Epoch 15/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1234 - accuracy: 0.9586 - val_loss: 0.0893 - val_accuracy: 0.9674\n",
            "Epoch 16/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1179 - accuracy: 0.9610 - val_loss: 0.0885 - val_accuracy: 0.9694\n",
            "Epoch 17/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1214 - accuracy: 0.9609 - val_loss: 0.0886 - val_accuracy: 0.9702\n",
            "Epoch 18/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1196 - accuracy: 0.9587 - val_loss: 0.0895 - val_accuracy: 0.9680\n",
            "Epoch 19/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1193 - accuracy: 0.9612 - val_loss: 0.0912 - val_accuracy: 0.9674\n",
            "Epoch 20/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1196 - accuracy: 0.9592 - val_loss: 0.0937 - val_accuracy: 0.9660\n",
            "Epoch 21/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1191 - accuracy: 0.9608 - val_loss: 0.0911 - val_accuracy: 0.9702\n",
            "Epoch 22/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1144 - accuracy: 0.9600 - val_loss: 0.0899 - val_accuracy: 0.9663\n",
            "Epoch 23/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1114 - accuracy: 0.9638 - val_loss: 0.0874 - val_accuracy: 0.9674\n",
            "Epoch 24/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1140 - accuracy: 0.9640 - val_loss: 0.0882 - val_accuracy: 0.9674\n",
            "Epoch 25/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1125 - accuracy: 0.9617 - val_loss: 0.0833 - val_accuracy: 0.9680\n",
            "Epoch 26/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1172 - accuracy: 0.9600 - val_loss: 0.0881 - val_accuracy: 0.9697\n",
            "Epoch 27/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1160 - accuracy: 0.9601 - val_loss: 0.0869 - val_accuracy: 0.9665\n",
            "Epoch 28/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1145 - accuracy: 0.9624 - val_loss: 0.0848 - val_accuracy: 0.9663\n",
            "Epoch 29/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1105 - accuracy: 0.9636 - val_loss: 0.0865 - val_accuracy: 0.9660\n",
            "Epoch 30/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1099 - accuracy: 0.9626 - val_loss: 0.0845 - val_accuracy: 0.9657\n",
            "Epoch 31/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1117 - accuracy: 0.9619 - val_loss: 0.0917 - val_accuracy: 0.9668\n",
            "Epoch 32/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1157 - accuracy: 0.9602 - val_loss: 0.0847 - val_accuracy: 0.9671\n",
            "Epoch 33/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1089 - accuracy: 0.9634 - val_loss: 0.0844 - val_accuracy: 0.9688\n",
            "Epoch 34/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1106 - accuracy: 0.9631 - val_loss: 0.0839 - val_accuracy: 0.9694\n",
            "Epoch 35/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1093 - accuracy: 0.9623 - val_loss: 0.0807 - val_accuracy: 0.9691\n",
            "Epoch 36/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1086 - accuracy: 0.9621 - val_loss: 0.0844 - val_accuracy: 0.9654\n",
            "Epoch 37/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1068 - accuracy: 0.9621 - val_loss: 0.0823 - val_accuracy: 0.9682\n",
            "Epoch 38/50\n",
            "441/441 [==============================] - 2s 4ms/step - loss: 0.1058 - accuracy: 0.9635 - val_loss: 0.0820 - val_accuracy: 0.9702\n",
            "Epoch 39/50\n",
            "441/441 [==============================] - 2s 4ms/step - loss: 0.1069 - accuracy: 0.9631 - val_loss: 0.0800 - val_accuracy: 0.9680\n",
            "Epoch 40/50\n",
            "441/441 [==============================] - 2s 4ms/step - loss: 0.1061 - accuracy: 0.9637 - val_loss: 0.0835 - val_accuracy: 0.9680\n",
            "Epoch 41/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1086 - accuracy: 0.9625 - val_loss: 0.0818 - val_accuracy: 0.9691\n",
            "Epoch 42/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1107 - accuracy: 0.9634 - val_loss: 0.0819 - val_accuracy: 0.9705\n",
            "Epoch 43/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.0992 - accuracy: 0.9651 - val_loss: 0.0799 - val_accuracy: 0.9691\n",
            "Epoch 44/50\n",
            "441/441 [==============================] - 2s 4ms/step - loss: 0.1027 - accuracy: 0.9648 - val_loss: 0.0798 - val_accuracy: 0.9733\n",
            "Epoch 45/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1060 - accuracy: 0.9646 - val_loss: 0.0781 - val_accuracy: 0.9705\n",
            "Epoch 46/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1051 - accuracy: 0.9628 - val_loss: 0.0820 - val_accuracy: 0.9711\n",
            "Epoch 47/50\n",
            "441/441 [==============================] - 2s 5ms/step - loss: 0.1083 - accuracy: 0.9628 - val_loss: 0.0829 - val_accuracy: 0.9682\n",
            "Epoch 48/50\n",
            "441/441 [==============================] - 2s 4ms/step - loss: 0.1040 - accuracy: 0.9629 - val_loss: 0.0765 - val_accuracy: 0.9714\n",
            "Epoch 49/50\n",
            "441/441 [==============================] - 2s 4ms/step - loss: 0.1021 - accuracy: 0.9642 - val_loss: 0.0808 - val_accuracy: 0.9691\n",
            "Epoch 50/50\n",
            "441/441 [==============================] - 2s 4ms/step - loss: 0.1025 - accuracy: 0.9651 - val_loss: 0.0786 - val_accuracy: 0.9685\n"
          ]
        }
      ],
      "source": [
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
        "# Train the model\n",
        "history = model.fit(x_train, y_train_cat, epochs=50, batch_size=32, validation_split=0.2, verbose=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "jePcKV9mzYGq"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Deep Learning Model Accuracy: 97.31%\n",
            "237/237 [==============================] - 0s 1ms/step\n",
            "Confusion Matrix:\n",
            "[[3362  136]\n",
            " [  67 3993]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.96      0.97      3498\n",
            "           1       0.97      0.98      0.98      4060\n",
            "\n",
            "    accuracy                           0.97      7558\n",
            "   macro avg       0.97      0.97      0.97      7558\n",
            "weighted avg       0.97      0.97      0.97      7558\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "scores = model.evaluate(x_test, y_test_cat, verbose=0)\n",
        "print(\"Deep Learning Model Accuracy: %.2f%%\" % (scores[1] * 100))\n",
        "\n",
        "# Predictions and classification report\n",
        "y_pred_cat = model.predict(x_test)\n",
        "y_pred = np.argmax(y_pred_cat, axis=1)\n",
        "\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Plot training & validation accuracy values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize=(12, 4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "f1s = {}\n",
        "for name in models:\n",
        "    f1s[name]=f1_score(y_test, preds[name])\n",
        "f1s['NN']=f1_score(y_test, y_pred)\n",
        "f1s=pd.DataFrame(f1s.values(),index=f1s.keys(),columns=[\"F1-score\"])*100\n",
        "f1s.plot(kind = \"bar\",  ylim=[80,100], figsize=(10,6), rot=0)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "KNN    98.423257\n",
              "LR     92.983939\n",
              "DC     99.481993\n",
              "SVC    97.323601\n",
              "GRD    99.322243\n",
              "Rf     99.655257\n",
              "NN     97.521065\n",
              "Name: F1-score, dtype: float64"
            ]
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "f1s['F1-score']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}