IDS-Best-Algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a547bf73-71af-45fc-95d6-460f63b671ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required modules\n",
    "import os\n",
    "from collections import defaultdict\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sea\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c2e7fda7-1017-47f3-bc21-a74035e37855",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8028124-4f29-42a5-bd6b-4862952dda50",
   "metadata": {},
   "source": [
    "# Reading and processing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7aaacfb1-c5ab-4ae6-a03e-90b6ab827dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "back,buffer_overflow,ftp_write,guess_passwd,imap,ipsweep,land,loadmodule,multihop,neptune,nmap,normal,perl,phf,pod,portsweep,rootkit,satan,smurf,spy,teardrop,warezclient,warezmaster.\n",
      "duration: continuous.\n",
      "protocol_type: symbolic.\n",
      "service: symbolic.\n",
      "flag: symbolic.\n",
      "src_bytes: continuous.\n",
      "dst_bytes: continuous.\n",
      "land: symbolic.\n",
      "wrong_fragment: continuous.\n",
      "urgent: continuous.\n",
      "hot: continuous.\n",
      "num_failed_logins: continuous.\n",
      "logged_in: symbolic.\n",
      "num_compromised: continuous.\n",
      "root_shell: continuous.\n",
      "su_attempted: continuous.\n",
      "num_root: continuous.\n",
      "num_file_creations: continuous.\n",
      "num_shells: continuous.\n",
      "num_access_files: continuous.\n",
      "num_outbound_cmds: continuous.\n",
      "is_host_login: symbolic.\n",
      "is_guest_login: symbolic.\n",
      "count: continuous.\n",
      "srv_count: continuous.\n",
      "serror_rate: continuous.\n",
      "srv_serror_rate: continuous.\n",
      "rerror_rate: continuous.\n",
      "srv_rerror_rate: continuous.\n",
      "same_srv_rate: continuous.\n",
      "diff_srv_rate: continuous.\n",
      "srv_diff_host_rate: continuous.\n",
      "dst_host_count: continuous.\n",
      "dst_host_srv_count: continuous.\n",
      "dst_host_same_srv_rate: continuous.\n",
      "dst_host_diff_srv_rate: continuous.\n",
      "dst_host_same_src_port_rate: continuous.\n",
      "dst_host_srv_diff_host_rate: continuous.\n",
      "dst_host_serror_rate: continuous.\n",
      "dst_host_srv_serror_rate: continuous.\n",
      "dst_host_rerror_rate: continuous.\n",
      "dst_host_srv_rerror_rate: continuous.\n"
     ]
    }
   ],
   "source": [
    "# Read names\n",
    "with open('kddcup.names', 'r') as names:\n",
    "    print(names.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffcd0fa6-8280-4a5c-8890-1fc99e754f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=\"\"\"duration,\n",
    "protocol_type,\n",
    "service,\n",
    "flag,\n",
    "src_bytes,\n",
    "dst_bytes,\n",
    "land,\n",
    "wrong_fragment,\n",
    "urgent,\n",
    "hot,\n",
    "num_failed_logins,\n",
    "logged_in,\n",
    "num_compromised,\n",
    "root_shell,\n",
    "su_attempted,\n",
    "num_root,\n",
    "num_file_creations,\n",
    "num_shells,\n",
    "num_access_files,\n",
    "num_outbound_cmds,\n",
    "is_host_login,\n",
    "is_guest_login,\n",
    "count,\n",
    "srv_count,\n",
    "serror_rate,\n",
    "srv_serror_rate,\n",
    "rerror_rate,\n",
    "srv_rerror_rate,\n",
    "same_srv_rate,\n",
    "diff_srv_rate,\n",
    "srv_diff_host_rate,\n",
    "dst_host_count,\n",
    "dst_host_srv_count,\n",
    "dst_host_same_srv_rate,\n",
    "dst_host_diff_srv_rate,\n",
    "dst_host_same_src_port_rate,\n",
    "dst_host_srv_diff_host_rate,\n",
    "dst_host_serror_rate,\n",
    "dst_host_srv_serror_rate,\n",
    "dst_host_rerror_rate,\n",
    "dst_host_srv_rerror_rate\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa5adf77-4739-4619-b8a6-27e8dd20e5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n"
     ]
    }
   ],
   "source": [
    "# Split our columns\n",
    "columns= []\n",
    "for c in cols.split(','):\n",
    "    if(c.strip()):\n",
    "        columns.append(c.strip())\n",
    "columns.append('target')\n",
    "print(len(columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94aa2dc5-e615-4d41-aede-224b91152435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "back dos\n",
      "buffer_overflow u2r\n",
      "ftp_write r2l\n",
      "guess_passwd r2l\n",
      "imap r2l\n",
      "ipsweep probe\n",
      "land dos\n",
      "loadmodule u2r\n",
      "multihop r2l\n",
      "neptune dos\n",
      "nmap probe\n",
      "perl u2r\n",
      "phf r2l\n",
      "pod dos\n",
      "portsweep probe\n",
      "rootkit u2r\n",
      "satan probe\n",
      "smurf dos\n",
      "spy r2l\n",
      "teardrop dos\n",
      "warezclient r2l\n",
      "warezmaster r2l\n"
     ]
    }
   ],
   "source": [
    "# Read attack types\n",
    "with open('training_attack_types', 'r') as types:\n",
    "    print(types.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "804c6991-d39d-4551-b4f3-23e991d9edb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "attack_types = {\n",
    "    'normal': 'normal',\n",
    "    'back': 'dos',\n",
    "    'buffer_overflow': 'u2r',\n",
    "    'ftp_write': 'r2l',\n",
    "    'guess_passwd': 'r2l',\n",
    "    'imap': 'r2l',\n",
    "    'ipsweep': 'probe',\n",
    "    'land': 'dos',\n",
    "    'loadmodule': 'u2r',\n",
    "    'multihop': 'r2l',\n",
    "    'neptune': 'dos',\n",
    "    'nmap': 'probe',\n",
    "    'perl': 'u2r',\n",
    "    'phf': 'r2l',\n",
    "    'pod': 'dos',\n",
    "    'portsweep': 'probe',\n",
    "    'rootkit': 'u2r',\n",
    "    'satan': 'probe',\n",
    "    'smurf': 'dos',\n",
    "    'spy': 'r2l',\n",
    "    'teardrop': 'dos',\n",
    "    'warezclient': 'r2l',\n",
    "    'warezmaster': 'r2l'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3758576-e6a9-46ec-bb29-c924cd035ce2",
   "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>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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>181</td>\n",
       "      <td>5450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</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>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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    http   SF        181       5450     0   \n",
       "1         0           tcp    http   SF        239        486     0   \n",
       "2         0           tcp    http   SF        235       1337     0   \n",
       "3         0           tcp    http   SF        219       1337     0   \n",
       "4         0           tcp    http   SF        217       2032     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
       "0               0       0    0  ...                   9   \n",
       "1               0       0    0  ...                  19   \n",
       "2               0       0    0  ...                  29   \n",
       "3               0       0    0  ...                  39   \n",
       "4               0       0    0  ...                  49   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                     1.0                     0.0   \n",
       "1                     1.0                     0.0   \n",
       "2                     1.0                     0.0   \n",
       "3                     1.0                     0.0   \n",
       "4                     1.0                     0.0   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.11                          0.0   \n",
       "1                         0.05                          0.0   \n",
       "2                         0.03                          0.0   \n",
       "3                         0.03                          0.0   \n",
       "4                         0.02                          0.0   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                   0.0                       0.0                   0.0   \n",
       "1                   0.0                       0.0                   0.0   \n",
       "2                   0.0                       0.0                   0.0   \n",
       "3                   0.0                       0.0                   0.0   \n",
       "4                   0.0                       0.0                   0.0   \n",
       "\n",
       "   dst_host_srv_rerror_rate   target  \n",
       "0                       0.0  normal.  \n",
       "1                       0.0  normal.  \n",
       "2                       0.0  normal.  \n",
       "3                       0.0  normal.  \n",
       "4                       0.0  normal.  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading Dataset\n",
    "df = pd.read_csv('kddcup.data_10_percent.gz', names= columns)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fabc97ab-26a4-45a5-893f-44243b4d499a",
   "metadata": {},
   "source": [
    "### Differentiating between nominal, binary, and numeric features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00a6efae-b6a3-4cb5-b991-8f94cd7346d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> nominal columns\n",
      "\n",
      " ['protocol_type', 'service', 'flag'] \n",
      "\n",
      ">> binary columns\n",
      "\n",
      " ['land', 'logged_in', 'root_shell', 'su_attempted', 'is_host_login', 'is_guest_login'] \n",
      "\n",
      ">>> numeric columns\n",
      "\n",
      " ['duration', 'src_bytes', 'dst_bytes', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'num_compromised', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate']\n"
     ]
    }
   ],
   "source": [
    "# root_shell is marked as a continuous feature in the kddcup.names \n",
    "# file, but it is supposed to be a binary feature according to the \n",
    "# dataset documentation\n",
    "\n",
    "col_names = np.array(columns)\n",
    "\n",
    "nominal_idx = [1, 2, 3]\n",
    "binary_idx = [6, 11, 13, 14, 20, 21]\n",
    "numeric_idx = list(set(range(41)).difference(nominal_idx).difference(binary_idx))\n",
    "\n",
    "nominal_cols = col_names[nominal_idx].tolist()\n",
    "binary_cols = col_names[binary_idx].tolist()\n",
    "numeric_cols = col_names[numeric_idx].tolist()\n",
    "\n",
    "print('> nominal columns\\n\\n', nominal_cols, '\\n')\n",
    "print('>> binary columns\\n\\n', binary_cols, '\\n' )\n",
    "print('>>> numeric columns\\n\\n', numeric_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ebd45ad-bc12-4c19-95bc-f09b0b8ab890",
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's go to attack types for our record\n",
    "# training_attack_types.txt maps each of the 22 different attacks to 1 of 4 categories\n",
    "\n",
    "category = defaultdict(list)\n",
    "category['benign'].append('normal')\n",
    "\n",
    "with open('datasets/training_attack_types.txt', 'r') as f:\n",
    "    for line in f.readlines():\n",
    "        attack, cat = line.strip().split(' ')\n",
    "        category[cat].append(attack)\n",
    "\n",
    "attack_mapping = dict((v,k) for k in category for v in category[k])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "996a5a47-525f-4388-a62d-021b7172e94f",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55039511-4fb5-4b5b-b762-1d6c5c44a17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['normal.', 'buffer_overflow.', 'loadmodule.', 'perl.', 'neptune.',\n",
       "       'smurf.', 'guess_passwd.', 'pod.', 'teardrop.', 'portsweep.',\n",
       "       'ipsweep.', 'land.', 'ftp_write.', 'back.', 'imap.', 'satan.',\n",
       "       'phf.', 'nmap.', 'multihop.', 'warezmaster.', 'warezclient.',\n",
       "       'spy.', 'rootkit.'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "14c0084c-350d-40b9-9b45-51b176eaf09b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eaf522d1-dc0c-4f7a-b250-05ccd3790eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smurf.              280790\n",
       "neptune.            107201\n",
       "normal.              97278\n",
       "back.                 2203\n",
       "satan.                1589\n",
       "ipsweep.              1247\n",
       "portsweep.            1040\n",
       "warezclient.          1020\n",
       "teardrop.              979\n",
       "pod.                   264\n",
       "nmap.                  231\n",
       "guess_passwd.           53\n",
       "buffer_overflow.        30\n",
       "land.                   21\n",
       "warezmaster.            20\n",
       "imap.                   12\n",
       "rootkit.                10\n",
       "loadmodule.              9\n",
       "ftp_write.               8\n",
       "multihop.                7\n",
       "phf.                     4\n",
       "perl.                    3\n",
       "spy.                     2\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the number for each attack and normal records\n",
    "df['target'].value_counts().sort_values(ascending= False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9011206b-c9c4-4b39-930b-223fabd42c3d",
   "metadata": {},
   "source": [
    "## we can see\n",
    "normal : 97278(normal) from 494021(records) --> 396743 (attacks)\n",
    "the number of attack records is much bigger than **normal** records. This **imbalance** data can lead our model to fail in the classification task, but we will fixed soon.. :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89641f41-2bc3-4b49-ba61-ebaae36297b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494021 entries, 0 to 494020\n",
      "Data columns (total 42 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   duration                     494021 non-null  int64  \n",
      " 1   protocol_type                494021 non-null  object \n",
      " 2   service                      494021 non-null  object \n",
      " 3   flag                         494021 non-null  object \n",
      " 4   src_bytes                    494021 non-null  int64  \n",
      " 5   dst_bytes                    494021 non-null  int64  \n",
      " 6   land                         494021 non-null  int64  \n",
      " 7   wrong_fragment               494021 non-null  int64  \n",
      " 8   urgent                       494021 non-null  int64  \n",
      " 9   hot                          494021 non-null  int64  \n",
      " 10  num_failed_logins            494021 non-null  int64  \n",
      " 11  logged_in                    494021 non-null  int64  \n",
      " 12  num_compromised              494021 non-null  int64  \n",
      " 13  root_shell                   494021 non-null  int64  \n",
      " 14  su_attempted                 494021 non-null  int64  \n",
      " 15  num_root                     494021 non-null  int64  \n",
      " 16  num_file_creations           494021 non-null  int64  \n",
      " 17  num_shells                   494021 non-null  int64  \n",
      " 18  num_access_files             494021 non-null  int64  \n",
      " 19  num_outbound_cmds            494021 non-null  int64  \n",
      " 20  is_host_login                494021 non-null  int64  \n",
      " 21  is_guest_login               494021 non-null  int64  \n",
      " 22  count                        494021 non-null  int64  \n",
      " 23  srv_count                    494021 non-null  int64  \n",
      " 24  serror_rate                  494021 non-null  float64\n",
      " 25  srv_serror_rate              494021 non-null  float64\n",
      " 26  rerror_rate                  494021 non-null  float64\n",
      " 27  srv_rerror_rate              494021 non-null  float64\n",
      " 28  same_srv_rate                494021 non-null  float64\n",
      " 29  diff_srv_rate                494021 non-null  float64\n",
      " 30  srv_diff_host_rate           494021 non-null  float64\n",
      " 31  dst_host_count               494021 non-null  int64  \n",
      " 32  dst_host_srv_count           494021 non-null  int64  \n",
      " 33  dst_host_same_srv_rate       494021 non-null  float64\n",
      " 34  dst_host_diff_srv_rate       494021 non-null  float64\n",
      " 35  dst_host_same_src_port_rate  494021 non-null  float64\n",
      " 36  dst_host_srv_diff_host_rate  494021 non-null  float64\n",
      " 37  dst_host_serror_rate         494021 non-null  float64\n",
      " 38  dst_host_srv_serror_rate     494021 non-null  float64\n",
      " 39  dst_host_rerror_rate         494021 non-null  float64\n",
      " 40  dst_host_srv_rerror_rate     494021 non-null  float64\n",
      " 41  target                       494021 non-null  object \n",
      "dtypes: float64(15), int64(23), object(4)\n",
      "memory usage: 158.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58e2560-f82d-4083-812b-85ba20f08d40",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ae341bf-984e-4a73-aaae-899065f7bd85",
   "metadata": {},
   "source": [
    "Set a column for attack type rely on target column, \n",
    "and set **only five** types we previous added by attacks_types`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "184e6eab-90bc-40e5-87bc-a0ccf49ec793",
   "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>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_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>target</th>\n",
       "      <th>Attack_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>181</td>\n",
       "      <td>5450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</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>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</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>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp    http   SF        181       5450     0   \n",
       "1         0           tcp    http   SF        239        486     0   \n",
       "2         0           tcp    http   SF        235       1337     0   \n",
       "3         0           tcp    http   SF        219       1337     0   \n",
       "4         0           tcp    http   SF        217       2032     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  dst_host_same_srv_rate  \\\n",
       "0               0       0    0  ...                     1.0   \n",
       "1               0       0    0  ...                     1.0   \n",
       "2               0       0    0  ...                     1.0   \n",
       "3               0       0    0  ...                     1.0   \n",
       "4               0       0    0  ...                     1.0   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                     0.0                         0.11   \n",
       "1                     0.0                         0.05   \n",
       "2                     0.0                         0.03   \n",
       "3                     0.0                         0.03   \n",
       "4                     0.0                         0.02   \n",
       "\n",
       "   dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "0                          0.0                   0.0   \n",
       "1                          0.0                   0.0   \n",
       "2                          0.0                   0.0   \n",
       "3                          0.0                   0.0   \n",
       "4                          0.0                   0.0   \n",
       "\n",
       "   dst_host_srv_serror_rate  dst_host_rerror_rate  dst_host_srv_rerror_rate  \\\n",
       "0                       0.0                   0.0                       0.0   \n",
       "1                       0.0                   0.0                       0.0   \n",
       "2                       0.0                   0.0                       0.0   \n",
       "3                       0.0                   0.0                       0.0   \n",
       "4                       0.0                   0.0                       0.0   \n",
       "\n",
       "    target  Attack_type  \n",
       "0  normal.       normal  \n",
       "1  normal.       normal  \n",
       "2  normal.       normal  \n",
       "3  normal.       normal  \n",
       "4  normal.       normal  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Attack_type'] = df.target.apply(lambda r:attack_types[r[:-1]])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad97c7af-178a-4b33-b6ce-38e2855aa4ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Attack_type'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f352737-ec43-4364-af0d-31bf89b32283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['normal', 'u2r', 'dos', 'r2l', 'probe'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Attack_type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff46009-db64-4862-94da-3f11f0a4ec49",
   "metadata": {},
   "source": [
    "**normal**: Just a normal -_-"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a718f8-6c48-4575-bcde-8f41073318cb",
   "metadata": {},
   "source": [
    "**(DoS)**: is an attack meant to shut down a machine or network,\n",
    "making it inaccessible to its intended users"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b14ebc0e-3ccd-4db1-bbde-450eec314421",
   "metadata": {},
   "source": [
    "**(U2R)**: is an attack where hackers exploit some vulnerabilities to\n",
    "gain root access to the system"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c9ffd3-fdf7-45df-8c16-b172455d749c",
   "metadata": {},
   "source": [
    "**(R2L)**: occurs when the attacker finds vulnerable points in a\n",
    "computer or network security software to gain access to the machine or the system"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d62db0-8fb9-42fd-9a39-8a0cfa68af94",
   "metadata": {},
   "source": [
    "**probe** is made to gather information on the targeted\n",
    "system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "00472153-edb8-40e6-b78a-41aed11dffc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dos       391458\n",
       "normal     97278\n",
       "probe       4107\n",
       "r2l         1126\n",
       "u2r           52\n",
       "Name: Attack_type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Attack_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "27f169c0-687c-4b72-b2a8-bea5211bbd61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494021, 43)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29f8f48c-7657-4210-880d-337b713028dd",
   "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",
       "target                         0\n",
       "Attack_type                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4967537-a7e6-4d66-be1f-12a0c7aeb904",
   "metadata": {},
   "source": [
    "### zero Nulls :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bca5e23-d3e0-4078-a2dc-1ab4134444ef",
   "metadata": {},
   "source": [
    "### Finding categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10f4fb00-ba06-47bd-a37f-d693d49c5d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['service', 'protocol_type', 'flag', 'target', 'Attack_type']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = df._get_numeric_data().columns\n",
    "\n",
    "cat_cols = list(set(df.columns) - set(num_cols))\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680e7c90-f2f3-447d-a9ff-6b1c4f9454c5",
   "metadata": {},
   "source": [
    "### remove target and Attack type for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46b43394-7327-44f4-b9fc-374b01bb5fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols.remove('target')\n",
    "cat_cols.remove('Attack_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b4f91e53-950c-4548-b357-5d9333230ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['service', 'protocol_type', 'flag']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e8420c-cde3-4616-ae34-e121272d9412",
   "metadata": {},
   "source": [
    "### Show our Categorical Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fa62fc4a-01d1-418f-9ffb-200cc37618cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a function for visualing a bar_char\n",
    "def bar_char(features):\n",
    "    df[features].value_counts().plot(kind= 'bar', color= 'teal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7d335a1b-962a-4710-833f-b7f6ba2b24be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_char('protocol_type')\n",
    "plt.xticks(rotation= 360);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4622003a-328c-43d2-85db-c39a7649168e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize= (15,3))\n",
    "bar_char('service')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "48d7737c-8af3-4815-a422-f2c5e3dd45d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_char('flag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5e794db-7c77-408e-8eea-f04cb370293d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at looged colums 'binary'\n",
    "bar_char('logged_in')\n",
    "plt.xticks(rotation= 360);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6d4019-3bd3-49ef-ac80-6f26117f49ea",
   "metadata": {},
   "source": [
    "logged_in (1 if successfully logged in; 0 otherwise)\n",
    "We **notice** that just 70000 packets are successfully logged in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "efc84e40-0fdc-4c8e-b4e7-e72890ae1ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at Attack_type features distribution\n",
    "bar_char('target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bbde0bd3-a53d-4211-bf10-0bd0458d0a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_char('Attack_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e28bbd-2c4b-4de5-986a-81626d478dd5",
   "metadata": {},
   "source": [
    "## Looking at Correlation between columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "13f8b215-ceca-4119-a36f-282e150615e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drop columns with NaN\n",
    "df = df.dropna('columns')\n",
    "\n",
    "# keep columns where there are more than 1 unique values\n",
    "df = df[[col for col in df if df[col].nunique() > 1]]\n",
    "\n",
    "# set correlation\n",
    "corr = df.corr()\n",
    "\n",
    "# set figure size\n",
    "plt.figure(figsize=(15, 12))\n",
    "\n",
    "# Visualiz it with heatmap \n",
    "sea.heatmap(corr)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2bb78fd-05c7-4242-9ebf-39e5e00222d1",
   "metadata": {},
   "source": [
    "### Look at some correlated columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "911d96ab-aac4-447d-a3c8-770d0d6ad4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9938277978737942"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num_root'].corr(df['num_compromised'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "150e1d50-82cc-4e2e-9c09-a8cfb881f0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9436670688882602"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srv_count'].corr(df['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bfa34c1c-bc8b-48cd-a582-1c983ef51221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.998361507272552"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srv_serror_rate'].corr(df['serror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "30f5669d-2ccc-44d9-9a73-fe674300307e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9947309539818258"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['srv_rerror_rate'].corr(df['rerror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ad378513-f1d5-453f-a141-b2a9c8d6de39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736854572954081"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_same_srv_rate'].corr(df['dst_host_srv_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c35eb4d9-9254-42c3-ac07-5301525a4d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9981559173372515"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_srv_serror_rate'].corr(df['dst_host_serror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6dfcb3e9-9741-4ce9-adb2-f88002d3be80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9848038371110593"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_srv_rerror_rate'].corr(df['dst_host_rerror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7489d578-4966-49d2-b81f-4cf15773addd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.927808034269101"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_same_srv_rate'].corr(df['same_srv_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "edf46ea6-fead-4494-9e1c-24fb5314215c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8989546630324231"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_srv_count'].corr(df['same_srv_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6bd62d47-249e-4831-862c-4de8bc5ecdcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9449263676783239"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_same_src_port_rate'].corr(df['srv_count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4ec88ee5-d52e-4092-83f8-122d083a7e7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.998672968010501"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_serror_rate'].corr(df['serror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2349da4d-9fe2-4f93-9801-e724a060041e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.997835300373953"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_serror_rate'].corr(df['srv_serror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6fceb678-8a64-4ee8-83da-28573c81ec35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9869947924955924"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_rerror_rate'].corr(df['rerror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e71466e7-a5f8-4cf5-b799-dfbaa8f43153",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9821663427308904"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_rerror_rate'].corr(df['srv_rerror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "36cfb06f-9b44-457f-bd5d-b0c744b59bec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9851995540751091"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_srv_rerror_rate'].corr(df['rerror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f5ebc328-9aac-45b1-b2c0-c62458e57213",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9865705438845588"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_srv_rerror_rate'].corr(df['srv_rerror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4601c5d6-3085-4358-b635-588c9075422c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9978492485679238"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_srv_serror_rate'].corr(df['serror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d83271a5-362e-4cce-a659-e51e960ce4f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9993041091850208"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dst_host_srv_serror_rate'].corr(df['srv_serror_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e8f1886a-710d-4d58-ada3-abb0b7b6b643",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The variable is highly correlated should be ignored for analysis.\n",
    "#corr: 0.99382\n",
    "df.drop('num_root', axis= 1, inplace= True)\n",
    "\n",
    "#corr: 0.99836\n",
    "df.drop('srv_serror_rate', axis= 1, inplace= True)\n",
    "\n",
    "#corr: 0.99473\n",
    "df.drop('srv_rerror_rate', axis= 1, inplace= True)\n",
    "\n",
    "#corr: 0.99815\n",
    "df.drop('dst_host_srv_serror_rate', axis= 1, inplace= True)\n",
    "\n",
    "#corr: 0.99867\n",
    "df.drop('dst_host_serror_rate', axis= 1, inplace= True)\n",
    "\n",
    "#corr: 0.98699\n",
    "df.drop('dst_host_rerror_rate', axis= 1, inplace= True)\n",
    "\n",
    "#corr: 0.98519\n",
    "df.drop('dst_host_srv_rerror_rate', axis= 1, inplace= True)\n",
    "\n",
    "#corr: 0.97368\n",
    "df.drop('dst_host_same_srv_rate', axis= 1, inplace= True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b70a9c8f-3821-4bc0-b307-46a85c1ed562",
   "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>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>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</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>target</th>\n",
       "      <th>Attack_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>181</td>\n",
       "      <td>5450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</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>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</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>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp    http   SF        181       5450     0   \n",
       "1         0           tcp    http   SF        239        486     0   \n",
       "2         0           tcp    http   SF        235       1337     0   \n",
       "3         0           tcp    http   SF        219       1337     0   \n",
       "4         0           tcp    http   SF        217       2032     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  same_srv_rate  diff_srv_rate  \\\n",
       "0               0       0    0  ...            1.0            0.0   \n",
       "1               0       0    0  ...            1.0            0.0   \n",
       "2               0       0    0  ...            1.0            0.0   \n",
       "3               0       0    0  ...            1.0            0.0   \n",
       "4               0       0    0  ...            1.0            0.0   \n",
       "\n",
       "   srv_diff_host_rate  dst_host_count  dst_host_srv_count  \\\n",
       "0                 0.0               9                   9   \n",
       "1                 0.0              19                  19   \n",
       "2                 0.0              29                  29   \n",
       "3                 0.0              39                  39   \n",
       "4                 0.0              49                  49   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                     0.0                         0.11   \n",
       "1                     0.0                         0.05   \n",
       "2                     0.0                         0.03   \n",
       "3                     0.0                         0.03   \n",
       "4                     0.0                         0.02   \n",
       "\n",
       "   dst_host_srv_diff_host_rate   target  Attack_type  \n",
       "0                          0.0  normal.       normal  \n",
       "1                          0.0  normal.       normal  \n",
       "2                          0.0  normal.       normal  \n",
       "3                          0.0  normal.       normal  \n",
       "4                          0.0  normal.       normal  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0c26354b-e5d0-4db7-be68-03b2e576566b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "urgent                              0.005510\n",
       "land                                0.006673\n",
       "su_attempted                        0.007793\n",
       "root_shell                          0.010551\n",
       "num_shells                          0.011020\n",
       "num_failed_logins                   0.015520\n",
       "num_access_files                    0.036482\n",
       "is_guest_login                      0.037211\n",
       "dst_host_srv_diff_host_rate         0.042133\n",
       "diff_srv_rate                       0.082205\n",
       "num_file_creations                  0.096416\n",
       "dst_host_diff_srv_rate              0.109259\n",
       "wrong_fragment                      0.134805\n",
       "srv_diff_host_rate                  0.142397\n",
       "rerror_rate                         0.231623\n",
       "logged_in                           0.355345\n",
       "serror_rate                         0.380717\n",
       "same_srv_rate                       0.388189\n",
       "dst_host_same_src_port_rate         0.481309\n",
       "hot                                 0.782103\n",
       "num_compromised                     1.798326\n",
       "dst_host_count                     64.745380\n",
       "dst_host_srv_count                106.040437\n",
       "count                             213.147412\n",
       "srv_count                         246.322817\n",
       "duration                          707.746472\n",
       "dst_bytes                       33040.001252\n",
       "src_bytes                      988218.101045\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate standard dev for dataframe\n",
    "df_std= df.std()\n",
    "df_std= df_std.sort_values(ascending= True)\n",
    "df_std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e396622-b2db-431c-b47d-b3913d4a28ff",
   "metadata": {},
   "source": [
    "## Feature Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0394019f-1318-40ff-95a8-02a2cd9cf0c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "udp      20354\n",
       "tcp     190065\n",
       "icmp    283602\n",
       "Name: protocol_type, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at protocol types\n",
    "df['protocol_type'].value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3e0f9b44-3e4e-401b-9741-2cffb81edb73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set feature mapping for protocols\n",
    "pro_map = {'udp': 0, 'tcp': 1, 'icmp': 2}\n",
    "df['protocol_type'] = df['protocol_type'].map(pro_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4db22a5c-2f71-4a70-94cd-f3b836bc8c52",
   "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",
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       "\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>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</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>target</th>\n",
       "      <th>Attack_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>181</td>\n",
       "      <td>5450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0              1    http   SF        181       5450     0   \n",
       "1         0              1    http   SF        239        486     0   \n",
       "2         0              1    http   SF        235       1337     0   \n",
       "3         0              1    http   SF        219       1337     0   \n",
       "4         0              1    http   SF        217       2032     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  same_srv_rate  diff_srv_rate  \\\n",
       "0               0       0    0  ...            1.0            0.0   \n",
       "1               0       0    0  ...            1.0            0.0   \n",
       "2               0       0    0  ...            1.0            0.0   \n",
       "3               0       0    0  ...            1.0            0.0   \n",
       "4               0       0    0  ...            1.0            0.0   \n",
       "\n",
       "   srv_diff_host_rate  dst_host_count  dst_host_srv_count  \\\n",
       "0                 0.0               9                   9   \n",
       "1                 0.0              19                  19   \n",
       "2                 0.0              29                  29   \n",
       "3                 0.0              39                  39   \n",
       "4                 0.0              49                  49   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                     0.0                         0.11   \n",
       "1                     0.0                         0.05   \n",
       "2                     0.0                         0.03   \n",
       "3                     0.0                         0.03   \n",
       "4                     0.0                         0.02   \n",
       "\n",
       "   dst_host_srv_diff_host_rate   target  Attack_type  \n",
       "0                          0.0  normal.       normal  \n",
       "1                          0.0  normal.       normal  \n",
       "2                          0.0  normal.       normal  \n",
       "3                          0.0  normal.       normal  \n",
       "4                          0.0  normal.       normal  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "454a9e24-9383-43cf-a50a-e10a2cf2762b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['protocol_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d9c75b05-7ed4-49dd-90ce-9d3810e91332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OTH            8\n",
       "S3            10\n",
       "RSTOS0        11\n",
       "S2            24\n",
       "S1            57\n",
       "SH           107\n",
       "RSTO         579\n",
       "RSTR         903\n",
       "REJ        26875\n",
       "S0         87007\n",
       "SF        378440\n",
       "Name: flag, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at falgs \n",
    "df['flag'].value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c59fbe48-ca5f-4f32-b386-ac4d2f077938",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set feature mapping for flags\n",
    "flag_map = {'OTH': 0, 'S3': 1, 'RSTOS0': 2, 'S2': 3, 'S1': 4, 'SH': 5,\n",
    "           'RSTO': 6, 'RSTR': 7, 'REJ': 8, 'S0': 9, 'SF': 10}\n",
    "df['flag'] = df['flag'].map(flag_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e6dd9456-8507-43d7-ad0e-79fd4fcf392f",
   "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>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>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</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>target</th>\n",
       "      <th>Attack_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>10</td>\n",
       "      <td>181</td>\n",
       "      <td>5450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>10</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>10</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>10</td>\n",
       "      <td>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>http</td>\n",
       "      <td>10</td>\n",
       "      <td>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "      <td>normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  protocol_type service  flag  src_bytes  dst_bytes  land  \\\n",
       "0         0              1    http    10        181       5450     0   \n",
       "1         0              1    http    10        239        486     0   \n",
       "2         0              1    http    10        235       1337     0   \n",
       "3         0              1    http    10        219       1337     0   \n",
       "4         0              1    http    10        217       2032     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  ...  same_srv_rate  diff_srv_rate  \\\n",
       "0               0       0    0  ...            1.0            0.0   \n",
       "1               0       0    0  ...            1.0            0.0   \n",
       "2               0       0    0  ...            1.0            0.0   \n",
       "3               0       0    0  ...            1.0            0.0   \n",
       "4               0       0    0  ...            1.0            0.0   \n",
       "\n",
       "   srv_diff_host_rate  dst_host_count  dst_host_srv_count  \\\n",
       "0                 0.0               9                   9   \n",
       "1                 0.0              19                  19   \n",
       "2                 0.0              29                  29   \n",
       "3                 0.0              39                  39   \n",
       "4                 0.0              49                  49   \n",
       "\n",
       "   dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "0                     0.0                         0.11   \n",
       "1                     0.0                         0.05   \n",
       "2                     0.0                         0.03   \n",
       "3                     0.0                         0.03   \n",
       "4                     0.0                         0.02   \n",
       "\n",
       "   dst_host_srv_diff_host_rate   target  Attack_type  \n",
       "0                          0.0  normal.       normal  \n",
       "1                          0.0  normal.       normal  \n",
       "2                          0.0  normal.       normal  \n",
       "3                          0.0  normal.       normal  \n",
       "4                          0.0  normal.       normal  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4522d9f5-f07d-4182-8975-8ba1112907cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  4,  8,  3,  9,  1,  6,  7,  2,  0,  5], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flag'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "54eacdf7-9389-45c9-98f5-268386333dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can ignore column of 'service' for now he is not usefull right in our case\n",
    "df.drop('service', axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3ad9d7f6-ed88-4949-8983-676861d0912a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494021, 32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a2e2b0a1-f363-4ee9-9fcf-c8b41025c5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration                         int64\n",
       "protocol_type                    int64\n",
       "flag                             int64\n",
       "src_bytes                        int64\n",
       "dst_bytes                        int64\n",
       "land                             int64\n",
       "wrong_fragment                   int64\n",
       "urgent                           int64\n",
       "hot                              int64\n",
       "num_failed_logins                int64\n",
       "logged_in                        int64\n",
       "num_compromised                  int64\n",
       "root_shell                       int64\n",
       "su_attempted                     int64\n",
       "num_file_creations               int64\n",
       "num_shells                       int64\n",
       "num_access_files                 int64\n",
       "is_guest_login                   int64\n",
       "count                            int64\n",
       "srv_count                        int64\n",
       "serror_rate                    float64\n",
       "rerror_rate                    float64\n",
       "same_srv_rate                  float64\n",
       "diff_srv_rate                  float64\n",
       "srv_diff_host_rate             float64\n",
       "dst_host_count                   int64\n",
       "dst_host_srv_count               int64\n",
       "dst_host_diff_srv_rate         float64\n",
       "dst_host_same_src_port_rate    float64\n",
       "dst_host_srv_diff_host_rate    float64\n",
       "target                          object\n",
       "Attack_type                     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09aee19c-434a-4cb9-a4bf-e71918e8396f",
   "metadata": {},
   "source": [
    "### allright data types now Fine !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ce12da-bd29-49ec-b9a6-56771e153faa",
   "metadata": {},
   "source": [
    "# Modeling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8c02ec7b-cd91-4d68-b761-fd50a5c62332",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import required modules\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "549752af-88bb-4144-adc7-eb23f09f30c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(494021, 31)\n",
      "(330994, 30) (163027, 30)\n",
      "(330994, 1) (163027, 1)\n"
     ]
    }
   ],
   "source": [
    "# Handle our dataset\n",
    "df= df.drop(['target',], axis= 1)\n",
    "print(df.shape)\n",
    "\n",
    "# Target variable and train set\n",
    "y = df[['Attack_type']]\n",
    "X = df.drop(['Attack_type', ], axis= 1)\n",
    "\n",
    "# Standard Scalar\n",
    "sc = MinMaxScaler()\n",
    "X = sc.fit_transform(X)\n",
    "\n",
    "# Split to test and train data\n",
    "X_train, X_test, y_train, y_test= train_test_split(X, y, test_size= 0.33, random_state= 42)\n",
    "print(X_train.shape, X_test.shape)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4cbbbf-0c97-47cb-a991-3c9b01673444",
   "metadata": {},
   "source": [
    "#### Now we need to fixed imbalance classes berfore apply our algorithms to rich out the best.\n",
    "https://imbalanced-learn.org/stable/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d305643-a79b-4278-a222-84aaf582f268",
   "metadata": {},
   "source": [
    "# Dealing with class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "751f0a95-07f9-4ea8-8292-596bdd43ec0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attack_type\n",
       "dos            0.791930\n",
       "normal         0.197311\n",
       "probe          0.008269\n",
       "r2l            0.002374\n",
       "u2r            0.000117\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts().apply(lambda x: x/float(len(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "175e1b7c-bc94-45eb-b4d9-59701f46246e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attack_type\n",
       "dos            0.792619\n",
       "normal         0.196714\n",
       "probe          0.008335\n",
       "r2l            0.002233\n",
       "u2r            0.000100\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts().apply(lambda x: x/float(len(y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ca3a4e49-60eb-4c00-b961-4386567695e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attack_type\n",
       "dos            262352\n",
       "normal          65111\n",
       "probe            2759\n",
       "r2l               739\n",
       "u2r                33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d3b303e-a60f-4f85-a3a4-33e915ad18fd",
   "metadata": {},
   "source": [
    "We can see   **dos**: from 391458 to 262352\n",
    "            **normal**: from 97278 to 65111"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfac86c6-8314-42b5-a1e2-2c5fb8c7c5cc",
   "metadata": {},
   "source": [
    "#### Implement Oversampling\n",
    "refers to intelligently generating synthetic data points for\n",
    "minority classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4d6f3e39-6450-4c75-9ba8-4937d1afd53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attack_type\n",
       "dos            262352\n",
       "normal         262352\n",
       "probe          262352\n",
       "r2l            262352\n",
       "u2r            262352\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smote = SMOTE(sampling_strategy= 'auto', random_state= 0)\n",
    "X_train_sm, y_train_sm = smote.fit_resample(X_train, y_train)\n",
    "y_train_sm.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2256c4cc-38f8-41ec-9b4d-b5feeccd8316",
   "metadata": {},
   "source": [
    "#### Implement Undersampling \n",
    "refers to sampling the overrepresented class(es) to reduce the\n",
    "number of samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fd42dbb5-7296-4856-98ef-b6df2dbaa7e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attack_type\n",
       "dos            66198\n",
       "normal         66198\n",
       "probe          66198\n",
       "r2l            66198\n",
       "u2r            66198\n",
       "dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "mean_class_size = int(y_train.value_counts().sum()/5)\n",
    "\n",
    "ratio = {'dos': mean_class_size,\n",
    "         'normal': mean_class_size,\n",
    "         'probe': mean_class_size,\n",
    "         'r2l': mean_class_size,\n",
    "         'u2r': mean_class_size}\n",
    "rus = RandomUnderSampler(sampling_strategy= ratio, random_state= 0, replacement= True)\n",
    "X_train_rus, y_train_rus = rus.fit_resample(X_train_sm, y_train_sm)\n",
    "y_train_rus.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b8deaa-8c09-4c0e-9aa7-b9c0e3199036",
   "metadata": {},
   "source": [
    "## Apply Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "469a7807-9824-44dc-ae11-3fe3bc05bfa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "modGNB = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ad843553-b5df-4788-a64a-4b4ef477e561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time training model\n",
    "s_time= time.time()\n",
    "modGNB.fit(X_train_rus, y_train_rus.values.ravel())# Using to Return a contiguous flattened values from y_train.\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "12be3806-76e1-4e38-9d7b-b53370bab5e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  0.9606235027313232\n"
     ]
    }
   ],
   "source": [
    "print('Training time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4cc73c04-2981-432e-b4fe-7145792fcd01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time testing model\n",
    "s_time= time.time()\n",
    "pred_modGNB = modGNB.predict(X_test)\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "83245cb1-fd4d-4750-b457-d8994867740d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  0.3005814552307129\n"
     ]
    }
   ],
   "source": [
    "print('Testing time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "32c29a18-7c19-41a1-bac0-58bddc45d647",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score is:  0.7937641620592767\n",
      "Test score is:  0.8802529642329184\n"
     ]
    }
   ],
   "source": [
    "# evaluate a score for both train and test\n",
    "print('Train score is: ', modGNB.score(X_train_rus, y_train_rus))\n",
    "print('Test score is: ', modGNB.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b83c6f7-b4bd-4c38-88dc-0f439c1429d5",
   "metadata": {},
   "source": [
    "## Apply Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "937598c7-ee5c-4316-b926-269aafabfee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "modDT = DecisionTreeClassifier(criterion= 'entropy', max_depth= 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c0d1bab0-a63f-4b69-b1d4-19c779c31a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time training model\n",
    "s_time= time.time()\n",
    "modDT.fit(X_train_rus, y_train_rus.values.ravel())# Using to Return a contiguous flattened values from y_train.\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e0b022dc-94d0-4009-a394-28b0c9fb656a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  2.077519416809082\n"
     ]
    }
   ],
   "source": [
    "print('Training time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3c433c4c-b494-4ef5-82f8-a8a584c37a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time testing model\n",
    "s_time= time.time()\n",
    "pred_modDT = modDT.predict(X_test)\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "4d08d9e3-9e29-4814-9836-02ef8a8d1542",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  0.023993492126464844\n"
     ]
    }
   ],
   "source": [
    "print('Testing time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d36d725c-62eb-4707-8a5f-4c5e48bf22cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score is:  0.9215474787757938\n",
      "Test score is:  0.9560440908561159\n"
     ]
    }
   ],
   "source": [
    "# evaluate a score for both train and test\n",
    "print('Train score is: ', modDT.score(X_train_rus, y_train_rus))\n",
    "print('Test score is: ', modDT.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb35325-02e3-4ac5-8790-0caf1eec8e15",
   "metadata": {},
   "source": [
    "## Apply Random Rorest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "827e25b2-5f0d-473c-a76b-dcbe119f941c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "modRF= RandomForestClassifier(n_estimators= 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ea6160f3-2cfe-4b28-b023-4ae5d0ba5442",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time training model\n",
    "s_time= time.time()\n",
    "modRF.fit(X_train_rus, y_train_rus.values.ravel())# Using to Return a contiguous flattened values from y_train.\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "20f5ec53-660b-44ca-8c08-e2273571efae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  10.982484102249146\n"
     ]
    }
   ],
   "source": [
    "print('Training time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5f6c7608-2061-4232-bac5-5224b24125a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time testing model\n",
    "s_time= time.time()\n",
    "pred_modRF = modRF.predict(X_test)\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "e18157b5-3cb2-4688-93ee-4a85adb06bbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  0.4870898723602295\n"
     ]
    }
   ],
   "source": [
    "print('Testing time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "59741f9f-c20f-4bf6-9041-ae0f50f75d82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score is:  0.9999848938034381\n",
      "Test score is:  0.9994663460653757\n"
     ]
    }
   ],
   "source": [
    "# evaluate a score for both train and test\n",
    "print('Train score is: ', modRF.score(X_train_rus, y_train_rus))\n",
    "print('Test score is: ', modRF.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f5bb73-d03c-4a12-a597-2a2c223851d6",
   "metadata": {},
   "source": [
    "## Apply Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "86ee3d9b-51ba-4685-9740-0f104c3ecd36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "modSVM = SVC(gamma= 'scale')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "054a75bf-ae6e-48e8-bd38-400219da279b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time training model\n",
    "s_time= time.time()\n",
    "modSVM.fit(X_train_rus, y_train_rus.values.ravel())# Using to Return a contiguous flattened values from y_train.\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7b16e5ea-f960-45d6-848b-858dc4fe2d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  685.3784263134003\n"
     ]
    }
   ],
   "source": [
    "print('Training time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "67ee738d-e742-419c-b860-2dd270a28bc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time testing model\n",
    "s_time= time.time()\n",
    "pred_modSVM = modSVM.predict(X_test)\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "62b184b4-db27-460e-803d-c1c2909b1be1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  563.9377031326294\n"
     ]
    }
   ],
   "source": [
    "print('Testing time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "39656d2f-1445-409b-9fa3-04057e6d6b9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score is:  0.9739629596060304\n",
      "Test score is:  0.9917498328497734\n"
     ]
    }
   ],
   "source": [
    "# evaluate a score for both train and test\n",
    "print('Train score is: ', modSVM.score(X_train_rus, y_train_rus))\n",
    "print('Test score is: ', modSVM.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f411ec-58a3-44a5-9d4b-5ce1a4d0d47d",
   "metadata": {},
   "source": [
    "Here we can notice SVM algorithm is late :("
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e20ff101-9756-45c0-86ae-feb83813f21b",
   "metadata": {},
   "source": [
    "## Apply Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "abfc36a4-752c-4614-afa9-8d6317c782e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "modLR = LogisticRegression(max_iter= 1200000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ce3bfe90-fb34-42db-841f-5916d97d5773",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time training model\n",
    "s_time= time.time()\n",
    "modLR.fit(X_train_rus, y_train_rus.values.ravel())# Using to Return a contiguous flattened values from y_train.\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6a2b3a04-491b-4b6f-8a24-ed01e7bcc99b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  105.90997266769409\n"
     ]
    }
   ],
   "source": [
    "print('Training time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fc9a9cec-3253-4a77-a495-b614c4761bde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time testing model\n",
    "s_time= time.time()\n",
    "pred_modLR = modLR.predict(X_test)\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "389e46af-6c4b-44cf-b32d-b4a9089fb65d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  0.029862403869628906\n"
     ]
    }
   ],
   "source": [
    "print('Testing time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d6bd0132-8c3a-4aff-b7bf-52e1cad0c6ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score is:  0.9422913078944983\n",
      "Test score is:  0.9796536769982886\n"
     ]
    }
   ],
   "source": [
    "# evaluate a score for both train and test\n",
    "print('Train score is: ', modLR.score(X_train_rus, y_train_rus))\n",
    "print('Test score is: ', modLR.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bb64a7-cd3c-4566-b95c-941113c21011",
   "metadata": {},
   "source": [
    "## Apply Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a59d02e8-2b8f-48f8-b6ce-f1da7e67f2b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "modGB = GradientBoostingClassifier(random_state= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1e9d8a6a-7b37-4d43-9b3e-a1b9ffa2826c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time training model\n",
    "s_time= time.time()\n",
    "modGB.fit(X_train_rus, y_train_rus.values.ravel())# Using to Return a contiguous flattened values from y_train.\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "8d16ea3b-839c-474d-b8be-1509145690db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time:  514.6790626049042\n"
     ]
    }
   ],
   "source": [
    "print('Training time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9bdfc81b-223e-4e07-8a02-983654863f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate our time testing model\n",
    "s_time= time.time()\n",
    "pred_modGB = modGB.predict(X_test)\n",
    "e_time= time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c5ae1121-82ae-4ae6-b351-969182f4dbc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time:  1.2446503639221191\n"
     ]
    }
   ],
   "source": [
    "print('Testing time: ', e_time-s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "364aed51-447f-4ac6-ad2a-ced7a18401dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score is:  0.9973322456871809\n",
      "Test score is:  0.9973930698595939\n"
     ]
    }
   ],
   "source": [
    "# evaluate a score for both train and test\n",
    "print('Train score is: ', modGB.score(X_train_rus, y_train_rus))\n",
    "print('Test score is: ', modGB.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f11247c-788a-44cc-b822-4cb27d125031",
   "metadata": {},
   "source": [
    "## Apply Artificial Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b4053c4e-6ec1-44fc-975f-8392d6158c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "72505ea4-ee5d-430d-afc6-039d738aa941",
   "metadata": {},
   "outputs": [],
   "source": [
    "def func():\n",
    "    model = Sequential()\n",
    "    \n",
    "    # here 30 is output dimension\n",
    "    model.add(Dense(30, input_dim= 30, activation= 'relu', kernel_initializer='random_uniform'))\n",
    "    \n",
    "    #in next layer we do not specify the input_dim as the model is sequential so output of previous layer is input to next layer\n",
    "    \n",
    "    model.add(Dense(1, activation='sigmoid', kernel_initializer= 'random_uniform'))\n",
    "    \n",
    "    # 5 classes: normal, dos, probe, r2l, u2r\n",
    "    model.add(Dense(5, activation= 'softmax'))\n",
    "    \n",
    "    # loss is categorical_crossentropy which specifies that we have multiple classes\n",
    "    model.compile(loss= 'categorical_crossentropy', optimizer= 'adam', metrics= ['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "848192eb-06cf-4bac-8035-5e64e9b106a1",
   "metadata": {},
   "source": [
    "**Note:** Since,the dataset is very big and we cannot fit complete data at once so we use **batch size**.\n",
    "This **divides** our data into batches each of size equal to batch_size.\n",
    "Now only this number of samples will be loaded into memory and processed. \n",
    "Once we are done with one batch it is flushed from memory and the next batch will be processed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "618030a0-bbe6-4813-8e6f-ab541a7296ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "modNN = KerasClassifier(build_fn= func, epochs= 100, batch_size= 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f3289aa9-2ad7-45ff-87bb-292c38ad584d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "5172/5172 [==============================] - 14s 708us/step - loss: 1.2984 - accuracy: 0.4389\n",
      "Epoch 2/100\n",
      "5172/5172 [==============================] - 4s 716us/step - loss: 0.9239 - accuracy: 0.6012\n",
      "Epoch 3/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.8308 - accuracy: 0.6157\n",
      "Epoch 4/100\n",
      "5172/5172 [==============================] - ETA: 0s - loss: 0.7729 - accuracy: 0.68 - 4s 712us/step - loss: 0.7728 - accuracy: 0.6866\n",
      "Epoch 5/100\n",
      "5172/5172 [==============================] - 4s 716us/step - loss: 0.7262 - accuracy: 0.7217\n",
      "Epoch 6/100\n",
      "5172/5172 [==============================] - 4s 708us/step - loss: 0.6921 - accuracy: 0.7371\n",
      "Epoch 7/100\n",
      "5172/5172 [==============================] - 4s 731us/step - loss: 0.6685 - accuracy: 0.7501\n",
      "Epoch 8/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.6443 - accuracy: 0.7679\n",
      "Epoch 9/100\n",
      "5172/5172 [==============================] - 4s 715us/step - loss: 0.6214 - accuracy: 0.7920\n",
      "Epoch 10/100\n",
      "5172/5172 [==============================] - 4s 707us/step - loss: 0.5847 - accuracy: 0.8361\n",
      "Epoch 11/100\n",
      "5172/5172 [==============================] - 4s 731us/step - loss: 0.5405 - accuracy: 0.8648\n",
      "Epoch 12/100\n",
      "5172/5172 [==============================] - 4s 716us/step - loss: 0.5030 - accuracy: 0.8726\n",
      "Epoch 13/100\n",
      "5172/5172 [==============================] - 4s 729us/step - loss: 0.4695 - accuracy: 0.8758\n",
      "Epoch 14/100\n",
      "5172/5172 [==============================] - 4s 801us/step - loss: 0.4465 - accuracy: 0.8791\n",
      "Epoch 15/100\n",
      "5172/5172 [==============================] - 4s 735us/step - loss: 0.4260 - accuracy: 0.8821\n",
      "Epoch 16/100\n",
      "5172/5172 [==============================] - 4s 723us/step - loss: 0.4087 - accuracy: 0.8835\n",
      "Epoch 17/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.3951 - accuracy: 0.8851\n",
      "Epoch 18/100\n",
      "5172/5172 [==============================] - 4s 716us/step - loss: 0.3818 - accuracy: 0.8862\n",
      "Epoch 19/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.3702 - accuracy: 0.8882\n",
      "Epoch 20/100\n",
      "5172/5172 [==============================] - 4s 733us/step - loss: 0.3602 - accuracy: 0.8892\n",
      "Epoch 21/100\n",
      "5172/5172 [==============================] - 4s 708us/step - loss: 0.3532 - accuracy: 0.8906\n",
      "Epoch 22/100\n",
      "5172/5172 [==============================] - 4s 719us/step - loss: 0.3413 - accuracy: 0.8932\n",
      "Epoch 23/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.3387 - accuracy: 0.8930\n",
      "Epoch 24/100\n",
      "5172/5172 [==============================] - 4s 727us/step - loss: 0.3306 - accuracy: 0.8946\n",
      "Epoch 25/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.3253 - accuracy: 0.8959\n",
      "Epoch 26/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.3210 - accuracy: 0.8961\n",
      "Epoch 27/100\n",
      "5172/5172 [==============================] - 4s 708us/step - loss: 0.3146 - accuracy: 0.8992\n",
      "Epoch 28/100\n",
      "5172/5172 [==============================] - 4s 746us/step - loss: 0.3059 - accuracy: 0.9009\n",
      "Epoch 29/100\n",
      "5172/5172 [==============================] - 4s 737us/step - loss: 0.3024 - accuracy: 0.9016\n",
      "Epoch 30/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.2988 - accuracy: 0.9032\n",
      "Epoch 31/100\n",
      "5172/5172 [==============================] - 4s 708us/step - loss: 0.2937 - accuracy: 0.9048\n",
      "Epoch 32/100\n",
      "5172/5172 [==============================] - 4s 720us/step - loss: 0.2882 - accuracy: 0.9067\n",
      "Epoch 33/100\n",
      "5172/5172 [==============================] - 4s 727us/step - loss: 0.2827 - accuracy: 0.9073\n",
      "Epoch 34/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2814 - accuracy: 0.9080\n",
      "Epoch 35/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2771 - accuracy: 0.9084\n",
      "Epoch 36/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2732 - accuracy: 0.9097\n",
      "Epoch 37/100\n",
      "5172/5172 [==============================] - 4s 733us/step - loss: 0.2742 - accuracy: 0.9104\n",
      "Epoch 38/100\n",
      "5172/5172 [==============================] - 4s 721us/step - loss: 0.2698 - accuracy: 0.9092\n",
      "Epoch 39/100\n",
      "5172/5172 [==============================] - 4s 719us/step - loss: 0.2651 - accuracy: 0.9103\n",
      "Epoch 40/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2651 - accuracy: 0.9102\n",
      "Epoch 41/100\n",
      "5172/5172 [==============================] - 4s 733us/step - loss: 0.2640 - accuracy: 0.9112\n",
      "Epoch 42/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2633 - accuracy: 0.9102\n",
      "Epoch 43/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2615 - accuracy: 0.9111\n",
      "Epoch 44/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2600 - accuracy: 0.9114\n",
      "Epoch 45/100\n",
      "5172/5172 [==============================] - 4s 725us/step - loss: 0.2605 - accuracy: 0.9107\n",
      "Epoch 46/100\n",
      "5172/5172 [==============================] - 4s 745us/step - loss: 0.2567 - accuracy: 0.9114\n",
      "Epoch 47/100\n",
      "5172/5172 [==============================] - 4s 713us/step - loss: 0.2581 - accuracy: 0.9114\n",
      "Epoch 48/100\n",
      "5172/5172 [==============================] - 4s 709us/step - loss: 0.2541 - accuracy: 0.9111\n",
      "Epoch 49/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2565 - accuracy: 0.9118\n",
      "Epoch 50/100\n",
      "5172/5172 [==============================] - 4s 733us/step - loss: 0.2533 - accuracy: 0.9119\n",
      "Epoch 51/100\n",
      "5172/5172 [==============================] - 4s 716us/step - loss: 0.2568 - accuracy: 0.9102\n",
      "Epoch 52/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2519 - accuracy: 0.9118\n",
      "Epoch 53/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2540 - accuracy: 0.9120\n",
      "Epoch 54/100\n",
      "5172/5172 [==============================] - 4s 729us/step - loss: 0.2515 - accuracy: 0.9118\n",
      "Epoch 55/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2496 - accuracy: 0.9120\n",
      "Epoch 56/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2498 - accuracy: 0.9124\n",
      "Epoch 57/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2499 - accuracy: 0.9122\n",
      "Epoch 58/100\n",
      "5172/5172 [==============================] - 4s 729us/step - loss: 0.2484 - accuracy: 0.9114\n",
      "Epoch 59/100\n",
      "5172/5172 [==============================] - 4s 717us/step - loss: 0.2458 - accuracy: 0.9121\n",
      "Epoch 60/100\n",
      "5172/5172 [==============================] - 4s 723us/step - loss: 0.2459 - accuracy: 0.9121\n",
      "Epoch 61/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.2456 - accuracy: 0.9122\n",
      "Epoch 62/100\n",
      "5172/5172 [==============================] - 4s 707us/step - loss: 0.2469 - accuracy: 0.9115\n",
      "Epoch 63/100\n",
      "5172/5172 [==============================] - 4s 734us/step - loss: 0.2462 - accuracy: 0.9125\n",
      "Epoch 64/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2443 - accuracy: 0.9127\n",
      "Epoch 65/100\n",
      "5172/5172 [==============================] - 4s 715us/step - loss: 0.2430 - accuracy: 0.9132\n",
      "Epoch 66/100\n",
      "5172/5172 [==============================] - 4s 715us/step - loss: 0.2422 - accuracy: 0.9125\n",
      "Epoch 67/100\n",
      "5172/5172 [==============================] - 4s 735us/step - loss: 0.2449 - accuracy: 0.9123\n",
      "Epoch 68/100\n",
      "5172/5172 [==============================] - 4s 717us/step - loss: 0.2452 - accuracy: 0.9124\n",
      "Epoch 69/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2421 - accuracy: 0.9136\n",
      "Epoch 70/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.2422 - accuracy: 0.9130\n",
      "Epoch 71/100\n",
      "5172/5172 [==============================] - 4s 739us/step - loss: 0.2411 - accuracy: 0.9133\n",
      "Epoch 72/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.2451 - accuracy: 0.9126\n",
      "Epoch 73/100\n",
      "5172/5172 [==============================] - 4s 706us/step - loss: 0.2405 - accuracy: 0.9123\n",
      "Epoch 74/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.2390 - accuracy: 0.9133\n",
      "Epoch 75/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2387 - accuracy: 0.9133\n",
      "Epoch 76/100\n",
      "5172/5172 [==============================] - 4s 745us/step - loss: 0.2407 - accuracy: 0.9125\n",
      "Epoch 77/100\n",
      "5172/5172 [==============================] - 4s 708us/step - loss: 0.2404 - accuracy: 0.9136\n",
      "Epoch 78/100\n",
      "5172/5172 [==============================] - 4s 706us/step - loss: 0.2372 - accuracy: 0.9136\n",
      "Epoch 79/100\n",
      "5172/5172 [==============================] - 4s 731us/step - loss: 0.2411 - accuracy: 0.9133\n",
      "Epoch 80/100\n",
      "5172/5172 [==============================] - 4s 733us/step - loss: 0.2379 - accuracy: 0.9150\n",
      "Epoch 81/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.2392 - accuracy: 0.9143\n",
      "Epoch 82/100\n",
      "5172/5172 [==============================] - 4s 760us/step - loss: 0.2406 - accuracy: 0.9144\n",
      "Epoch 83/100\n",
      "5172/5172 [==============================] - 4s 716us/step - loss: 0.2383 - accuracy: 0.9146\n",
      "Epoch 84/100\n",
      "5172/5172 [==============================] - 4s 741us/step - loss: 0.2372 - accuracy: 0.9161\n",
      "Epoch 85/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2332 - accuracy: 0.9162\n",
      "Epoch 86/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.2214 - accuracy: 0.9248\n",
      "Epoch 87/100\n",
      "5172/5172 [==============================] - 4s 735us/step - loss: 0.0966 - accuracy: 0.9859\n",
      "Epoch 88/100\n",
      "5172/5172 [==============================] - 4s 727us/step - loss: 0.0896 - accuracy: 0.9871\n",
      "Epoch 89/100\n",
      "5172/5172 [==============================] - 4s 721us/step - loss: 0.0897 - accuracy: 0.9871\n",
      "Epoch 90/100\n",
      "5172/5172 [==============================] - 4s 729us/step - loss: 0.0851 - accuracy: 0.9873\n",
      "Epoch 91/100\n",
      "5172/5172 [==============================] - 4s 708us/step - loss: 0.0862 - accuracy: 0.9872\n",
      "Epoch 92/100\n",
      "5172/5172 [==============================] - 4s 715us/step - loss: 0.0869 - accuracy: 0.9872\n",
      "Epoch 93/100\n",
      "5172/5172 [==============================] - 4s 752us/step - loss: 0.0872 - accuracy: 0.9875\n",
      "Epoch 94/100\n",
      "5172/5172 [==============================] - 4s 710us/step - loss: 0.0848 - accuracy: 0.9878\n",
      "Epoch 95/100\n",
      "5172/5172 [==============================] - 4s 714us/step - loss: 0.0851 - accuracy: 0.9875\n",
      "Epoch 96/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.0822 - accuracy: 0.9878\n",
      "Epoch 97/100\n",
      "5172/5172 [==============================] - 4s 761us/step - loss: 0.0837 - accuracy: 0.9875\n",
      "Epoch 98/100\n",
      "5172/5172 [==============================] - 4s 719us/step - loss: 0.0831 - accuracy: 0.9879\n",
      "Epoch 99/100\n",
      "5172/5172 [==============================] - 4s 708us/step - loss: 0.0835 - accuracy: 0.9876\n",
      "Epoch 100/100\n",
      "5172/5172 [==============================] - 4s 712us/step - loss: 0.0823 - accuracy: 0.9876\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "modNN.fit(X_train_rus, y_train_rus.values.ravel())\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b8aaf852-3310-4b3e-87b4-0edef7778a1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time 384.8384850025177\n"
     ]
    }
   ],
   "source": [
    "print('Training time',end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "436a5334-941b-4885-906d-3cb9588fd9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_time = time.time()\n",
    "y_test_pred = modNN.predict(X_test)\n",
    "e_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "4cf1fa48-28a2-4809-b358-8586df52bb9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing time 1.557352066040039\n"
     ]
    }
   ],
   "source": [
    "print('Testing time', e_time - s_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "6b2fc7a1-1306-4382-aad4-16a99cfc1f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_time = time.time() \n",
    "y_train_pred = modNN.predict(X_train_rus)\n",
    "e_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b18fd74a-1437-4fb5-9a88-be2d8a9ad590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9870630532644491"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train_rus, y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "3b4c7bc2-1b3b-4693-8016-44c480b1d18a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9928723462984659"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384a6b4e-fb3a-43b3-a44d-2ce23c42fbb7",
   "metadata": {},
   "source": [
    "# Visualize Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f10bbb4d-16c4-49af-a988-0483e5158ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3240x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alg_names = ['GNB', 'DT', 'RF', 'SVM', 'LR', 'GB', 'ANN']\n",
    "vals_train = [79.37, 92.15, 99.99, 97.39, 94.22, 99.73, 98.70]\n",
    "\n",
    "figure = plt.figure(figsize= (45,6), num=10)\n",
    "plt.subplot(131)\n",
    "plt.ylim(80, 102)\n",
    "plt.bar(alg_names, vals_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12095f82-b00e-4a1f-b86e-12bc947a2b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save bar to png # Save bar to png \n",
    "figure.savefig('training_accuracy_figure.png', bbox_inches= 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d4a352-78bb-4737-8ff6-d82b6d711b06",
   "metadata": {},
   "source": [
    "# Visualize Testing Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f68d569d-f176-4bc7-a368-38aa3900d75a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3240x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alg_names = ['GNB', 'DT', 'RF', 'SVM', 'LR', 'GB', 'ANN']\n",
    "vals_test = [88.02, 95.60, 99.94, 99.17, 97.96, 99.73, 99.28]\n",
    "\n",
    "figure = plt.figure(figsize= (45,6), num=10)\n",
    "plt.subplot(131)\n",
    "plt.ylim(80, 102)\n",
    "plt.bar(alg_names, vals_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3a27966-3771-4ee7-b090-710cdd6c0fd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save bar to png \n",
    "figure.savefig('testing_accuracy_figure.png', bbox_inches= 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80d75fd2-5cc7-4592-a29d-d300e4f322a7",
   "metadata": {},
   "source": [
    "# Training Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c8e32b2-939b-4123-a77f-1053f6db4bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3240x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alg_names = ['GNB', 'DT', 'RF', 'SVM', 'LR', 'GB', 'ANN']\n",
    "vals_test = [0.9606, 2.077, 10.982, 685.378, 105.909, 514.679, 384.838]\n",
    "\n",
    "figure = plt.figure(figsize= (45,6), num=10)\n",
    "plt.subplot(131)\n",
    "plt.bar(alg_names, vals_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a35177d9-3409-4f6e-a637-64f43469cec6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save bar to png \n",
    "figure.savefig('training_time_figure.png', bbox_inches= 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7037905-fd95-4c14-8d0a-fe4710d941fb",
   "metadata": {},
   "source": [
    "# Testing Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ad908ff-adfa-421c-8b52-b007ff33d417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 3240x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alg_names = ['GNB', 'DT', 'RF', 'SVM', 'LR', 'GB', 'ANN']\n",
    "vals_test = [0.3005, 0.2399, 0.4870, 563.937, 0.0298, 1.2446, 1.557]\n",
    "\n",
    "figure = plt.figure(figsize= (45,6), num=10)\n",
    "plt.subplot(131)\n",
    "plt.bar(alg_names, vals_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4b0a2da-b21a-4dd5-8f62-be78f6dbf3ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save bar to png \n",
    "figure.savefig('testing_time_figure.png', bbox_inches= 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b618d93-0bfd-4cff-919b-aa873a12418e",
   "metadata": {},
   "source": [
    "# Now we can say Random forest The best based on accuracy and time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aa0c512-97b3-4e3f-af38-7183211da229",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}