mlnids.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "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\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('Train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=pd.read_csv('Test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</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>...</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",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>anomaly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp  ftp_data   SF        491          0     0   \n",
       "1         0           udp     other   SF        146          0     0   \n",
       "2         0           tcp   private   S0          0          0     0   \n",
       "3         0           tcp      http   SF        232       8153     0   \n",
       "4         0           tcp      http   SF        199        420     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
       "0               0       0    0  ...                  25   \n",
       "1               0       0    0  ...                   1   \n",
       "2               0       0    0  ...                  26   \n",
       "3               0       0    0  ...                 255   \n",
       "4               0       0    0  ...                 255   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                    0.17                    0.03   \n",
       "1                    0.00                    0.60   \n",
       "2                    0.10                    0.05   \n",
       "3                    1.00                    0.00   \n",
       "4                    1.00                    0.00   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.17                         0.00   \n",
       "1                         0.88                         0.00   \n",
       "2                         0.00                         0.00   \n",
       "3                         0.03                         0.04   \n",
       "4                         0.00                         0.00   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                  0.00                      0.00                  0.05   \n",
       "1                  0.00                      0.00                  0.00   \n",
       "2                  1.00                      1.00                  0.00   \n",
       "3                  0.03                      0.01                  0.00   \n",
       "4                  0.00                      0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_rerror_rate    class  \n",
       "0                      0.00   normal  \n",
       "1                      0.00   normal  \n",
       "2                      0.00  anomaly  \n",
       "3                      0.01   normal  \n",
       "4                      0.00   normal  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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": 52,
   "metadata": {},
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "train.describe(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25192, 42)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "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": 57,
   "metadata": {},
   "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='class', ylabel='count'>"
      ]
     },
     "execution_count": 58,
     "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": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution Training set:\n",
      "class\n",
      "normal     13449\n",
      "anomaly    11743\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Class distribution Training set:')\n",
    "print(train['class'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "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": 61,
   "metadata": {},
   "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": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</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>...</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",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type  service  flag  src_bytes  dst_bytes  land  \\\n",
       "0         0              1       19     9        491          0     0   \n",
       "1         0              2       41     9        146          0     0   \n",
       "2         0              1       46     5          0          0     0   \n",
       "3         0              1       22     9        232       8153     0   \n",
       "4         0              1       22     9        199        420     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
       "0               0       0    0  ...                  25   \n",
       "1               0       0    0  ...                   1   \n",
       "2               0       0    0  ...                  26   \n",
       "3               0       0    0  ...                 255   \n",
       "4               0       0    0  ...                 255   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                    0.17                    0.03   \n",
       "1                    0.00                    0.60   \n",
       "2                    0.10                    0.05   \n",
       "3                    1.00                    0.00   \n",
       "4                    1.00                    0.00   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.17                         0.00   \n",
       "1                         0.88                         0.00   \n",
       "2                         0.00                         0.00   \n",
       "3                         0.03                         0.04   \n",
       "4                         0.00                         0.00   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                  0.00                      0.00                  0.05   \n",
       "1                  0.00                      0.00                  0.00   \n",
       "2                  1.00                      1.00                  0.00   \n",
       "3                  0.03                      0.01                  0.00   \n",
       "4                  0.00                      0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_rerror_rate  class  \n",
       "0                      0.00      1  \n",
       "1                      0.00      1  \n",
       "2                      0.00      0  \n",
       "3                      0.01      1  \n",
       "4                      0.00      1  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.drop(['class'], axis=1)\n",
    "Y_train = train['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "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": 64,
     "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": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = StandardScaler()\n",
    "X_train = scale.fit_transform(X_train)\n",
    "test = scale.fit_transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17634, 10)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7558, 10)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17634,)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7558,)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  0.3138895034790039\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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  0.004001617431640625\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": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 75,
     "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": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.9288873766587274\n",
      "Test Score: 0.9233924318602805\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": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: optuna in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (3.6.1)\n",
      "Requirement already satisfied: alembic>=1.5.0 in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from optuna) (1.13.2)\n",
      "Requirement already satisfied: colorlog in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from optuna) (6.8.2)\n",
      "Requirement already satisfied: numpy in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from optuna) (2.0.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from optuna) (24.1)\n",
      "Requirement already satisfied: sqlalchemy>=1.3.0 in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from optuna) (2.0.31)\n",
      "Requirement already satisfied: tqdm in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from optuna) (4.66.4)\n",
      "Requirement already satisfied: PyYAML in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from optuna) (6.0.1)\n",
      "Requirement already satisfied: Mako in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from alembic>=1.5.0->optuna) (1.3.5)\n",
      "Requirement already satisfied: typing-extensions>=4 in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from alembic>=1.5.0->optuna) (4.12.2)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from sqlalchemy>=1.3.0->optuna) (3.0.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from colorlog->optuna) (0.4.6)\n",
      "Requirement already satisfied: MarkupSafe>=0.9.2 in c:\\users\\sambhat\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from Mako->alembic>=1.5.0->optuna) (2.1.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import optuna\n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "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": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FrozenTrial(number=0, state=1, values=[0.9822704419158508], datetime_start=datetime.datetime(2024, 7, 20, 17, 18, 39, 573238), datetime_complete=datetime.datetime(2024, 7, 20, 17, 18, 41, 118316), params={'KNN_n_neighbors': 11}, 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": [
    "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": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.9828172847907451\n",
      "Test Score: 0.9822704419158508\n"
     ]
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  0.02756357192993164\n"
     ]
    }
   ],
   "source": [
    "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": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  0.0020036697387695312\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": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "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": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FrozenTrial(number=9, state=1, values=[0.9956337655464409], datetime_start=datetime.datetime(2024, 7, 20, 17, 18, 44, 59331), datetime_complete=datetime.datetime(2024, 7, 20, 17, 18, 44, 87072), params={'dt_max_depth': 13, 'dt_max_features': 9}, 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=9, value=None)\n"
     ]
    }
   ],
   "source": [
    "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": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.9997164568447318\n",
      "Test Score: 0.9947075946017465\n"
     ]
    }
   ],
   "source": [
    "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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═════════════════════╤═══════════════╤══════════════╕\n",
      "│ Model               │   Train Score │   Test Score │\n",
      "╞═════════════════════╪═══════════════╪══════════════╡\n",
      "│ KNN                 │      0.982817 │     0.98227  │\n",
      "├─────────────────────┼───────────────┼──────────────┤\n",
      "│ Logistic Regression │      0.928887 │     0.923392 │\n",
      "├─────────────────────┼───────────────┼──────────────┤\n",
      "│ Decision Tree       │      0.999716 │     0.994708 │\n",
      "╘═════════════════════╧═══════════════╧══════════════╛\n"
     ]
    }
   ],
   "source": [
    "data = [[\"KNN\", KNN_train, KNN_test], \n",
    "        [\"Logistic Regression\", lg_train, lg_test],\n",
    "        [\"Decision Tree\", dt_train, dt_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": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "models = {}\n",
    "models['KNeighborsClassifier']= knn\n",
    "models['LogisticRegression']= lr\n",
    "models['DecisionTreeClassifier']= dtc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "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)\n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "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",
      " 91.36 % +- 0.56\n",
      "\n",
      "Mean recall: \n",
      " 95.72 % +- 0.69\n",
      "\n",
      "************** DecisionTreeClassifier Model Validation **************\n",
      "Mean precision: \n",
      " 99.54 % +- 0.18\n",
      "\n",
      "Mean recall: \n",
      " 99.52 % +- 0.21\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": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sambhat\\AppData\\Local\\Temp\\ipykernel_16732\\1073761507.py:4: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n",
      "  scores=pd.DataFrame(scores).swapaxes(\"index\", \"columns\")*100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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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": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = {}\n",
    "models['KNeighborsClassifier']= knn\n",
    "models['LogisticRegression']= lr\n",
    "models['DecisionTreeClassifier']= dtc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions complete.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*************** KNeighborsClassifier 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",
      "**************** LogisticRegression Model Testing ****************\n",
      "[[3129  369]\n",
      " [ 210 3850]]\n",
      "----------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      normal       0.94      0.89      0.92      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",
      "************** DecisionTreeClassifier Model Testing **************\n",
      "[[3485   13]\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      1.00      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"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report, f1_score\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": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 96,
     "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": [
    "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)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}