bruteforce-sql-injection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset, get the desired values only.\n",
    "# Make the points into categorical data\n",
    "# USe smoot\n",
    "# train \n",
    "# evaluate.\n",
    "\n",
    "# 'protocol'\n",
    "# 'flow_duration'\n",
    "# 'tot_fwd_pkts'\n",
    "# 'tot_bwd_pkts'\n",
    "# 'totlen_fwd_pkts'\n",
    "# 'totlen_bwd_pkts'\n",
    "# 'fwd_pkt_len_mean'\n",
    "# 'fwd_pkt_len_std'\n",
    "# 'bwd_pkt_len_mean'\n",
    "# 'flow_byts_s'\n",
    "# 'flow_pkts_s'\n",
    "# 'flow_iat_std'\n",
    "# 'flow_iat_min'\n",
    "# 'fwd_iat_tot'\n",
    "# 'fwd_iat_min'\n",
    "# 'bwd_iat_tot'\n",
    "# 'bwd_iat_min'\n",
    "# 'fwd_psh_flags'\n",
    "# 'fwd_urg_flags'\n",
    "# 'bwd_pkts_s'\n",
    "# 'fin_flag_cnt'\n",
    "# 'rst_flag_cnt'\n",
    "# 'psh_flag_cnt'\n",
    "# 'ack_flag_cnt'\n",
    "# 'urg_flag_cnt'\n",
    "# 'down_up_ratio'\n",
    "# 'init_fwd_win_byts'\n",
    "# 'init_bwd_win_byts'\n",
    "# 'fwd_seg_size_min'\n",
    "# 'active_mean'\n",
    "# 'idle_mean'\n",
    "\n",
    "\n",
    "# label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dst_port</th>\n",
       "      <th>protocol</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>flow_duration</th>\n",
       "      <th>tot_fwd_pkts</th>\n",
       "      <th>tot_bwd_pkts</th>\n",
       "      <th>totlen_fwd_pkts</th>\n",
       "      <th>totlen_bwd_pkts</th>\n",
       "      <th>fwd_pkt_len_max</th>\n",
       "      <th>fwd_pkt_len_min</th>\n",
       "      <th>...</th>\n",
       "      <th>fwd_seg_size_min</th>\n",
       "      <th>active_mean</th>\n",
       "      <th>active_std</th>\n",
       "      <th>active_max</th>\n",
       "      <th>active_min</th>\n",
       "      <th>idle_mean</th>\n",
       "      <th>idle_std</th>\n",
       "      <th>idle_max</th>\n",
       "      <th>idle_min</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>23/02/2018 08:18:29</td>\n",
       "      <td>1532698</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1179</td>\n",
       "      <td>1969</td>\n",
       "      <td>648</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "      <td>17</td>\n",
       "      <td>23/q02/2018 08:17:45</td>\n",
       "      <td>117573855</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58786927.5</td>\n",
       "      <td>2.375324e+07</td>\n",
       "      <td>75583006</td>\n",
       "      <td>41990849</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500</td>\n",
       "      <td>17</td>\n",
       "      <td>23/02/2018 08:17:45</td>\n",
       "      <td>117573848</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58786924.0</td>\n",
       "      <td>2.375325e+07</td>\n",
       "      <td>75583007</td>\n",
       "      <td>41990841</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>23/02/2018 08:19:55</td>\n",
       "      <td>1745392</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1179</td>\n",
       "      <td>1969</td>\n",
       "      <td>648</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>17</td>\n",
       "      <td>23/02/2018 08:18:17</td>\n",
       "      <td>89483474</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>4000364.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4000364</td>\n",
       "      <td>4000364</td>\n",
       "      <td>21370777.5</td>\n",
       "      <td>1.528092e+07</td>\n",
       "      <td>41989576</td>\n",
       "      <td>7200485</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dst_port  protocol             timestamp  flow_duration  tot_fwd_pkts  \\\n",
       "0        22         6   23/02/2018 08:18:29        1532698            11   \n",
       "1       500        17  23/q02/2018 08:17:45      117573855             3   \n",
       "2       500        17   23/02/2018 08:17:45      117573848             3   \n",
       "3        22         6   23/02/2018 08:19:55        1745392            11   \n",
       "4       500        17   23/02/2018 08:18:17       89483474             6   \n",
       "\n",
       "   tot_bwd_pkts  totlen_fwd_pkts  totlen_bwd_pkts  fwd_pkt_len_max  \\\n",
       "0            11             1179             1969              648   \n",
       "1             0             1500                0              500   \n",
       "2             0             1500                0              500   \n",
       "3            11             1179             1969              648   \n",
       "4             0             3000                0              500   \n",
       "\n",
       "   fwd_pkt_len_min  ...  fwd_seg_size_min  active_mean  active_std  \\\n",
       "0                0  ...                32          0.0         0.0   \n",
       "1              500  ...                 8          0.0         0.0   \n",
       "2              500  ...                 8          0.0         0.0   \n",
       "3                0  ...                32          0.0         0.0   \n",
       "4              500  ...                 8    4000364.0         0.0   \n",
       "\n",
       "   active_max  active_min   idle_mean      idle_std  idle_max  idle_min  \\\n",
       "0           0           0         0.0  0.000000e+00         0         0   \n",
       "1           0           0  58786927.5  2.375324e+07  75583006  41990849   \n",
       "2           0           0  58786924.0  2.375325e+07  75583007  41990841   \n",
       "3           0           0         0.0  0.000000e+00         0         0   \n",
       "4     4000364     4000364  21370777.5  1.528092e+07  41989576   7200485   \n",
       "\n",
       "    label  \n",
       "0  Benign  \n",
       "1  Benign  \n",
       "2  Benign  \n",
       "3  Benign  \n",
       "4  Benign  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('bruteforce-web-xss_sql-injection_23-02-2018.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "Benign              1048009\n",
       "Brute Force -Web        362\n",
       "Brute Force -XSS        151\n",
       "SQL Injection            53\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\n",
    "    'protocol',\n",
    "'flow_duration',\n",
    "'tot_fwd_pkts',\n",
    "'tot_bwd_pkts',\n",
    "'totlen_fwd_pkts',\n",
    "'totlen_bwd_pkts',\n",
    "'fwd_pkt_len_mean',\n",
    "'fwd_pkt_len_std',\n",
    "'bwd_pkt_len_mean',\n",
    "'flow_byts_s',\n",
    "'flow_pkts_s',\n",
    "'flow_iat_std',\n",
    "'flow_iat_min',\n",
    "'fwd_iat_tot',\n",
    "'fwd_iat_min',\n",
    "'bwd_iat_tot',\n",
    "'bwd_iat_min',\n",
    "'fwd_psh_flags',\n",
    "'fwd_urg_flags',\n",
    "'bwd_pkts_s',\n",
    "'fin_flag_cnt',\n",
    "'rst_flag_cnt',\n",
    "'psh_flag_cnt',\n",
    "'ack_flag_cnt',\n",
    "'urg_flag_cnt',\n",
    "'down_up_ratio',\n",
    "'init_fwd_win_byts',\n",
    "'init_bwd_win_byts',\n",
    "'fwd_seg_size_min',\n",
    "'active_mean',\n",
    "'idle_mean',\n",
    "'label'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protocol</th>\n",
       "      <th>flow_duration</th>\n",
       "      <th>tot_fwd_pkts</th>\n",
       "      <th>tot_bwd_pkts</th>\n",
       "      <th>totlen_fwd_pkts</th>\n",
       "      <th>totlen_bwd_pkts</th>\n",
       "      <th>fwd_pkt_len_mean</th>\n",
       "      <th>fwd_pkt_len_std</th>\n",
       "      <th>bwd_pkt_len_mean</th>\n",
       "      <th>flow_byts_s</th>\n",
       "      <th>...</th>\n",
       "      <th>psh_flag_cnt</th>\n",
       "      <th>ack_flag_cnt</th>\n",
       "      <th>urg_flag_cnt</th>\n",
       "      <th>down_up_ratio</th>\n",
       "      <th>init_fwd_win_byts</th>\n",
       "      <th>init_bwd_win_byts</th>\n",
       "      <th>fwd_seg_size_min</th>\n",
       "      <th>active_mean</th>\n",
       "      <th>idle_mean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1532698</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1179</td>\n",
       "      <td>1969</td>\n",
       "      <td>107.181818</td>\n",
       "      <td>196.245162</td>\n",
       "      <td>179.00</td>\n",
       "      <td>2053.894505</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>117573855</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.757938</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58786927.5</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>117573848</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.757939</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58786924.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>1745392</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1179</td>\n",
       "      <td>1969</td>\n",
       "      <td>107.181818</td>\n",
       "      <td>196.245162</td>\n",
       "      <td>179.00</td>\n",
       "      <td>1803.606296</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29200</td>\n",
       "      <td>230</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>89483474</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.525744</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>4000364.0</td>\n",
       "      <td>21370777.5</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>6</td>\n",
       "      <td>85992425</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62569</td>\n",
       "      <td>-1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85992425.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>6</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "      <td>-1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048572</th>\n",
       "      <td>17</td>\n",
       "      <td>79134</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>98</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.00</td>\n",
       "      <td>1756.514267</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048573</th>\n",
       "      <td>6</td>\n",
       "      <td>10053785</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1321</td>\n",
       "      <td>1659</td>\n",
       "      <td>120.090909</td>\n",
       "      <td>197.116440</td>\n",
       "      <td>138.25</td>\n",
       "      <td>296.405782</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8192</td>\n",
       "      <td>62856</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048574</th>\n",
       "      <td>6</td>\n",
       "      <td>566835</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>455</td>\n",
       "      <td>338</td>\n",
       "      <td>75.833333</td>\n",
       "      <td>62.936211</td>\n",
       "      <td>67.60</td>\n",
       "      <td>1398.996181</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1048575 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         protocol  flow_duration  tot_fwd_pkts  tot_bwd_pkts  totlen_fwd_pkts  \\\n",
       "0               6        1532698            11            11             1179   \n",
       "1              17      117573855             3             0             1500   \n",
       "2              17      117573848             3             0             1500   \n",
       "3               6        1745392            11            11             1179   \n",
       "4              17       89483474             6             0             3000   \n",
       "...           ...            ...           ...           ...              ...   \n",
       "1048570         6       85992425             2             0                0   \n",
       "1048571         6            132             2             0                0   \n",
       "1048572        17          79134             1             1               41   \n",
       "1048573         6       10053785            11            12             1321   \n",
       "1048574         6         566835             6             5              455   \n",
       "\n",
       "         totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
       "0                   1969        107.181818       196.245162            179.00   \n",
       "1                      0        500.000000         0.000000              0.00   \n",
       "2                      0        500.000000         0.000000              0.00   \n",
       "3                   1969        107.181818       196.245162            179.00   \n",
       "4                      0        500.000000         0.000000              0.00   \n",
       "...                  ...               ...              ...               ...   \n",
       "1048570                0          0.000000         0.000000              0.00   \n",
       "1048571                0          0.000000         0.000000              0.00   \n",
       "1048572               98         41.000000         0.000000             98.00   \n",
       "1048573             1659        120.090909       197.116440            138.25   \n",
       "1048574              338         75.833333        62.936211             67.60   \n",
       "\n",
       "         flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
       "0        2053.894505  ...             1             0             0   \n",
       "1          12.757938  ...             0             0             0   \n",
       "2          12.757939  ...             0             0             0   \n",
       "3        1803.606296  ...             1             0             0   \n",
       "4          33.525744  ...             0             0             0   \n",
       "...              ...  ...           ...           ...           ...   \n",
       "1048570     0.000000  ...             0             1             0   \n",
       "1048571     0.000000  ...             0             1             0   \n",
       "1048572  1756.514267  ...             0             0             0   \n",
       "1048573   296.405782  ...             1             0             0   \n",
       "1048574  1398.996181  ...             1             0             0   \n",
       "\n",
       "         down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  \\\n",
       "0                    1              29200                230   \n",
       "1                    0                 -1                 -1   \n",
       "2                    0                 -1                 -1   \n",
       "3                    1              29200                230   \n",
       "4                    0                 -1                 -1   \n",
       "...                ...                ...                ...   \n",
       "1048570              0              62569                 -1   \n",
       "1048571              0                258                 -1   \n",
       "1048572              1                 -1                 -1   \n",
       "1048573              1               8192              62856   \n",
       "1048574              0               8192                  0   \n",
       "\n",
       "         fwd_seg_size_min  active_mean   idle_mean   label  \n",
       "0                      32          0.0         0.0  Benign  \n",
       "1                       8          0.0  58786927.5  Benign  \n",
       "2                       8          0.0  58786924.0  Benign  \n",
       "3                      32          0.0         0.0  Benign  \n",
       "4                       8    4000364.0  21370777.5  Benign  \n",
       "...                   ...          ...         ...     ...  \n",
       "1048570                20          0.0  85992425.0  Benign  \n",
       "1048571                20          0.0         0.0  Benign  \n",
       "1048572                 8          0.0         0.0  Benign  \n",
       "1048573                20          0.0         0.0  Benign  \n",
       "1048574                20          0.0         0.0  Benign  \n",
       "\n",
       "[1048575 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = data[cols]\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "LE = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ld/bwffsrf52mnfq4_gy_k9p9480000gn/T/ipykernel_47411/3086081652.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['protocol'] = LE.fit_transform(df_new['protocol'])\n",
      "/var/folders/ld/bwffsrf52mnfq4_gy_k9p9480000gn/T/ipykernel_47411/3086081652.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['label'] = LE.fit_transform(df_new['label'])\n"
     ]
    }
   ],
   "source": [
    "df_new['protocol'] = LE.fit_transform(df_new['protocol'])\n",
    "df_new['label'] = LE.fit_transform(df_new['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "protocol\n",
       "1    740107\n",
       "2    292401\n",
       "0     16067\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['protocol'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame after removing rows with any value greater than float64:\n",
      "         protocol  flow_duration  tot_fwd_pkts  tot_bwd_pkts  totlen_fwd_pkts  \\\n",
      "0               1        1532698            11            11             1179   \n",
      "1               2      117573855             3             0             1500   \n",
      "2               2      117573848             3             0             1500   \n",
      "3               1        1745392            11            11             1179   \n",
      "4               2       89483474             6             0             3000   \n",
      "...           ...            ...           ...           ...              ...   \n",
      "1048570         1       85992425             2             0                0   \n",
      "1048571         1            132             2             0                0   \n",
      "1048572         2          79134             1             1               41   \n",
      "1048573         1       10053785            11            12             1321   \n",
      "1048574         1         566835             6             5              455   \n",
      "\n",
      "         totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
      "0                   1969        107.181818       196.245162            179.00   \n",
      "1                      0        500.000000         0.000000              0.00   \n",
      "2                      0        500.000000         0.000000              0.00   \n",
      "3                   1969        107.181818       196.245162            179.00   \n",
      "4                      0        500.000000         0.000000              0.00   \n",
      "...                  ...               ...              ...               ...   \n",
      "1048570                0          0.000000         0.000000              0.00   \n",
      "1048571                0          0.000000         0.000000              0.00   \n",
      "1048572               98         41.000000         0.000000             98.00   \n",
      "1048573             1659        120.090909       197.116440            138.25   \n",
      "1048574              338         75.833333        62.936211             67.60   \n",
      "\n",
      "         flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
      "0        2053.894505  ...             1             0             0   \n",
      "1          12.757938  ...             0             0             0   \n",
      "2          12.757939  ...             0             0             0   \n",
      "3        1803.606296  ...             1             0             0   \n",
      "4          33.525744  ...             0             0             0   \n",
      "...              ...  ...           ...           ...           ...   \n",
      "1048570     0.000000  ...             0             1             0   \n",
      "1048571     0.000000  ...             0             1             0   \n",
      "1048572  1756.514267  ...             0             0             0   \n",
      "1048573   296.405782  ...             1             0             0   \n",
      "1048574  1398.996181  ...             1             0             0   \n",
      "\n",
      "         down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  \\\n",
      "0                    1              29200                230   \n",
      "1                    0                 -1                 -1   \n",
      "2                    0                 -1                 -1   \n",
      "3                    1              29200                230   \n",
      "4                    0                 -1                 -1   \n",
      "...                ...                ...                ...   \n",
      "1048570              0              62569                 -1   \n",
      "1048571              0                258                 -1   \n",
      "1048572              1                 -1                 -1   \n",
      "1048573              1               8192              62856   \n",
      "1048574              0               8192                  0   \n",
      "\n",
      "         fwd_seg_size_min  active_mean   idle_mean  label  \n",
      "0                      32          0.0         0.0      0  \n",
      "1                       8          0.0  58786927.5      0  \n",
      "2                       8          0.0  58786924.0      0  \n",
      "3                      32          0.0         0.0      0  \n",
      "4                       8    4000364.0  21370777.5      0  \n",
      "...                   ...          ...         ...    ...  \n",
      "1048570                20          0.0  85992425.0      0  \n",
      "1048571                20          0.0         0.0      0  \n",
      "1048572                 8          0.0         0.0      0  \n",
      "1048573                20          0.0         0.0      0  \n",
      "1048574                20          0.0         0.0      0  \n",
      "\n",
      "[1042867 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "threshold = pd.to_numeric(np.finfo(np.float64).max)\n",
    "\n",
    "# Remove rows with any value greater than float64\n",
    "df_new = df_new[(df_new <= threshold).all(axis=1)]\n",
    "\n",
    "print(\"DataFrame after removing rows with any value greater than float64:\")\n",
    "print(df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with NaN values: []\n"
     ]
    }
   ],
   "source": [
    "columns_with_na = df_new.columns[df_new.isna().any()].tolist()\n",
    "print(\"Columns with NaN values:\", columns_with_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df_new.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with NaN values: []\n"
     ]
    }
   ],
   "source": [
    "columns_with_na = df_new.columns[df_new.isna().any()].tolist()\n",
    "print(\"Columns with NaN values:\", columns_with_na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ld/bwffsrf52mnfq4_gy_k9p9480000gn/T/ipykernel_47411/2687928747.py:1: FutureWarning: pandas.value_counts is deprecated and will be removed in a future version. Use pd.Series(obj).value_counts() instead.\n",
      "  pd.value_counts(df_new['label']).plot.bar()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    1042301\n",
       "1        362\n",
       "2        151\n",
       "3         53\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.value_counts(df_new['label']).plot.bar()\n",
    "plt.title('Fraud class histogram')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Frequency')\n",
    "df_new['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_x = [\n",
    "    'protocol',\n",
    "'flow_duration',\n",
    "'tot_fwd_pkts',\n",
    "'tot_bwd_pkts',\n",
    "'totlen_fwd_pkts',\n",
    "'totlen_bwd_pkts',\n",
    "'fwd_pkt_len_mean',\n",
    "'fwd_pkt_len_std',\n",
    "'bwd_pkt_len_mean',\n",
    "'flow_byts_s',\n",
    "'flow_pkts_s',\n",
    "'flow_iat_std',\n",
    "'flow_iat_min',\n",
    "'fwd_iat_tot',\n",
    "'fwd_iat_min',\n",
    "'bwd_iat_tot',\n",
    "'bwd_iat_min',\n",
    "'fwd_psh_flags',\n",
    "'fwd_urg_flags',\n",
    "'bwd_pkts_s',\n",
    "'fin_flag_cnt',\n",
    "'rst_flag_cnt',\n",
    "'psh_flag_cnt',\n",
    "'ack_flag_cnt',\n",
    "'urg_flag_cnt',\n",
    "'down_up_ratio',\n",
    "'init_fwd_win_byts',\n",
    "'init_bwd_win_byts',\n",
    "'fwd_seg_size_min',\n",
    "'active_mean',\n",
    "'idle_mean'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (1042867, 31)\n",
      "Shape of y: (1042867,)\n"
     ]
    }
   ],
   "source": [
    "X = np.array(df_new[cols_x])\n",
    "y = np.array(df_new['label'])\n",
    "print('Shape of X: {}'.format(X.shape))\n",
    "print('Shape of y: {}'.format(y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>protocol</th>\n",
       "      <th>flow_duration</th>\n",
       "      <th>tot_fwd_pkts</th>\n",
       "      <th>tot_bwd_pkts</th>\n",
       "      <th>totlen_fwd_pkts</th>\n",
       "      <th>totlen_bwd_pkts</th>\n",
       "      <th>fwd_pkt_len_mean</th>\n",
       "      <th>fwd_pkt_len_std</th>\n",
       "      <th>bwd_pkt_len_mean</th>\n",
       "      <th>flow_byts_s</th>\n",
       "      <th>...</th>\n",
       "      <th>psh_flag_cnt</th>\n",
       "      <th>ack_flag_cnt</th>\n",
       "      <th>urg_flag_cnt</th>\n",
       "      <th>down_up_ratio</th>\n",
       "      <th>init_fwd_win_byts</th>\n",
       "      <th>init_bwd_win_byts</th>\n",
       "      <th>fwd_seg_size_min</th>\n",
       "      <th>active_mean</th>\n",
       "      <th>idle_mean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1532698</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1179</td>\n",
       "      <td>1969</td>\n",
       "      <td>107.181818</td>\n",
       "      <td>196.245162</td>\n",
       "      <td>179.00</td>\n",
       "      <td>2053.894505</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29200</td>\n",
       "      <td>230</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>117573855</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.757938</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58786927.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>117573848</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.757939</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58786924.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1745392</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1179</td>\n",
       "      <td>1969</td>\n",
       "      <td>107.181818</td>\n",
       "      <td>196.245162</td>\n",
       "      <td>179.00</td>\n",
       "      <td>1803.606296</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29200</td>\n",
       "      <td>230</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>89483474</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3000</td>\n",
       "      <td>0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>33.525744</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>4000364.0</td>\n",
       "      <td>21370777.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>1</td>\n",
       "      <td>85992425</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62569</td>\n",
       "      <td>-1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85992425.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>258</td>\n",
       "      <td>-1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048572</th>\n",
       "      <td>2</td>\n",
       "      <td>79134</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>98</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.00</td>\n",
       "      <td>1756.514267</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048573</th>\n",
       "      <td>1</td>\n",
       "      <td>10053785</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>1321</td>\n",
       "      <td>1659</td>\n",
       "      <td>120.090909</td>\n",
       "      <td>197.116440</td>\n",
       "      <td>138.25</td>\n",
       "      <td>296.405782</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8192</td>\n",
       "      <td>62856</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048574</th>\n",
       "      <td>1</td>\n",
       "      <td>566835</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>455</td>\n",
       "      <td>338</td>\n",
       "      <td>75.833333</td>\n",
       "      <td>62.936211</td>\n",
       "      <td>67.60</td>\n",
       "      <td>1398.996181</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8192</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1042867 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         protocol  flow_duration  tot_fwd_pkts  tot_bwd_pkts  totlen_fwd_pkts  \\\n",
       "0               1        1532698            11            11             1179   \n",
       "1               2      117573855             3             0             1500   \n",
       "2               2      117573848             3             0             1500   \n",
       "3               1        1745392            11            11             1179   \n",
       "4               2       89483474             6             0             3000   \n",
       "...           ...            ...           ...           ...              ...   \n",
       "1048570         1       85992425             2             0                0   \n",
       "1048571         1            132             2             0                0   \n",
       "1048572         2          79134             1             1               41   \n",
       "1048573         1       10053785            11            12             1321   \n",
       "1048574         1         566835             6             5              455   \n",
       "\n",
       "         totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
       "0                   1969        107.181818       196.245162            179.00   \n",
       "1                      0        500.000000         0.000000              0.00   \n",
       "2                      0        500.000000         0.000000              0.00   \n",
       "3                   1969        107.181818       196.245162            179.00   \n",
       "4                      0        500.000000         0.000000              0.00   \n",
       "...                  ...               ...              ...               ...   \n",
       "1048570                0          0.000000         0.000000              0.00   \n",
       "1048571                0          0.000000         0.000000              0.00   \n",
       "1048572               98         41.000000         0.000000             98.00   \n",
       "1048573             1659        120.090909       197.116440            138.25   \n",
       "1048574              338         75.833333        62.936211             67.60   \n",
       "\n",
       "         flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
       "0        2053.894505  ...             1             0             0   \n",
       "1          12.757938  ...             0             0             0   \n",
       "2          12.757939  ...             0             0             0   \n",
       "3        1803.606296  ...             1             0             0   \n",
       "4          33.525744  ...             0             0             0   \n",
       "...              ...  ...           ...           ...           ...   \n",
       "1048570     0.000000  ...             0             1             0   \n",
       "1048571     0.000000  ...             0             1             0   \n",
       "1048572  1756.514267  ...             0             0             0   \n",
       "1048573   296.405782  ...             1             0             0   \n",
       "1048574  1398.996181  ...             1             0             0   \n",
       "\n",
       "         down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  \\\n",
       "0                    1              29200                230   \n",
       "1                    0                 -1                 -1   \n",
       "2                    0                 -1                 -1   \n",
       "3                    1              29200                230   \n",
       "4                    0                 -1                 -1   \n",
       "...                ...                ...                ...   \n",
       "1048570              0              62569                 -1   \n",
       "1048571              0                258                 -1   \n",
       "1048572              1                 -1                 -1   \n",
       "1048573              1               8192              62856   \n",
       "1048574              0               8192                  0   \n",
       "\n",
       "         fwd_seg_size_min  active_mean   idle_mean  label  \n",
       "0                      32          0.0         0.0      0  \n",
       "1                       8          0.0  58786927.5      0  \n",
       "2                       8          0.0  58786924.0      0  \n",
       "3                      32          0.0         0.0      0  \n",
       "4                       8    4000364.0  21370777.5      0  \n",
       "...                   ...          ...         ...    ...  \n",
       "1048570                20          0.0  85992425.0      0  \n",
       "1048571                20          0.0         0.0      0  \n",
       "1048572                 8          0.0         0.0      0  \n",
       "1048573                20          0.0         0.0      0  \n",
       "1048574                20          0.0         0.0      0  \n",
       "\n",
       "[1042867 rows x 32 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    1042301\n",
       "1        362\n",
       "2        151\n",
       "3         53\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions X_train dataset:  (834293, 31)\n",
      "Number transactions y_train dataset:  (834293,)\n",
      "Number transactions X_test dataset:  (208574, 31)\n",
      "Number transactions y_test dataset:  (208574,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "X_t, X_v, y_t, y_v = train_test_split(X_train, y_train, test_size=0.2)\n",
    "print(\"Number transactions X_train dataset: \", X_train.shape)\n",
    "print(\"Number transactions y_train dataset: \", y_train.shape)\n",
    "print(\"Number transactions X_test dataset: \", X_test.shape)\n",
    "print(\"Number transactions y_test dataset: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '0': 833849\n",
      "Before OverSampling, counts of label '1': 285\n",
      "Before OverSampling, counts of label '2': 115\n",
      "Before OverSampling, counts of label '3': 44\n",
      "After OverSampling, the shape of train_X: (2668324, 31)\n",
      "After OverSampling, the shape of train_y: (2668324,) \n",
      "\n",
      "After OverSampling, counts of label '0': 667081\n",
      "After OverSampling, counts of label '1': 667081\n",
      "After OverSampling, counts of label '2': 667081\n",
      "After OverSampling, counts of label '3': 667081\n"
     ]
    }
   ],
   "source": [
    "print(\"Before OverSampling, counts of label '0': {}\".format(sum(y_train==0)))\n",
    "print(\"Before OverSampling, counts of label '1': {}\".format(sum(y_train==1)))\n",
    "print(\"Before OverSampling, counts of label '2': {}\".format(sum(y_train==2)))\n",
    "print(\"Before OverSampling, counts of label '3': {}\".format(sum(y_train==3)))\n",
    "\n",
    "sm = SMOTE(random_state=2)\n",
    "X_train_res, y_train_res = sm.fit_resample(X_t, y_t.ravel())\n",
    "\n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_train_res.shape))\n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format(y_train_res.shape))\n",
    "\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum(y_train_res==0)))\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum(y_train_res==1)))\n",
    "print(\"After OverSampling, counts of label '2': {}\".format(sum(y_train_res==2)))\n",
    "print(\"After OverSampling, counts of label '3': {}\".format(sum(y_train_res==3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    'learning_rate': [0.01, 0.1],\n",
    "    'max_depth': [3, 5],\n",
    "    'n_estimators': [50, 100]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 8 candidates, totalling 16 fits\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.921 total time=  31.5s\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.921 total time=  31.7s\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.937 total time=  58.3s\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.984 total time=  39.0s\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.936 total time=  57.6s\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.982 total time=  37.6s\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.980 total time=  31.3s\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.984 total time= 1.2min\n",
      "[CV 2/2] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.980 total time=  30.5s\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.985 total time= 1.2min\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.995 total time=  57.3s\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.996 total time=  39.2s\n",
      "[CV 2/2] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.995 total time=  57.5s\n",
      "[CV 2/2] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.996 total time=  39.2s\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.999 total time= 1.1min\n",
      "[CV 2/2] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.999 total time=  57.0s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1], &#x27;max_depth&#x27;: [3, 5],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100]},\n",
       "             verbose=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=2,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1], &#x27;max_depth&#x27;: [3, 5],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100]},\n",
       "             verbose=5)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=2,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={'learning_rate': [0.01, 0.1], 'max_depth': [3, 5],\n",
       "                         'n_estimators': [50, 100]},\n",
       "             verbose=5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GridSearchCV(xgb_model, parameters, cv=2, verbose=5, n_jobs=3)\n",
    "clf.fit(X_train_res, y_train_res.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9974109877201709"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "acc = accuracy_score(y_pred, y_v)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9973294849789524"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred = clf.predict(X_test)\n",
    "acc = accuracy_score(y_test_pred, y_test)\n",
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[207902,    489,     36,     25],\n",
       "       [     1,     75,      0,      1],\n",
       "       [     0,      0,     35,      1],\n",
       "       [     0,      3,      1,      5]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_test_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "667434"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_t.size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166859"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_v.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208574"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1], &#x27;max_depth&#x27;: [3, 5],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100]},\n",
       "             verbose=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=2,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.1], &#x27;max_depth&#x27;: [3, 5],\n",
       "                         &#x27;n_estimators&#x27;: [50, 100]},\n",
       "             verbose=5)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBClassifier</label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=2,\n",
       "             estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     callbacks=None, colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, device=None,\n",
       "                                     early_stopping_rounds=None,\n",
       "                                     enable_categorical=False, eval_metric=None,\n",
       "                                     feature_types=None, gamma=None,\n",
       "                                     grow_policy=None, importance_type=None,\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None,...\n",
       "                                     max_cat_threshold=None,\n",
       "                                     max_cat_to_onehot=None,\n",
       "                                     max_delta_step=None, max_depth=None,\n",
       "                                     max_leaves=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     multi_strategy=None, n_estimators=None,\n",
       "                                     n_jobs=None, num_parallel_tree=None,\n",
       "                                     random_state=None, ...),\n",
       "             n_jobs=3,\n",
       "             param_grid={'learning_rate': [0.01, 0.1], 'max_depth': [3, 5],\n",
       "                         'n_estimators': [50, 100]},\n",
       "             verbose=5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bruteforce-web-xss_sql-injection_23-02-2018new.joblib']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(clf, \"bruteforce-web-xss_sql-injection_23-02-2018new.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_clf = load(\"bruteforce-web-xss_sql-injection_23-02-2018new.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_row = X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_row = current_row.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "prediction = loaded_clf.predict(current_row)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "208353 [1] 0\n",
      "557\n"
     ]
    }
   ],
   "source": [
    "mis = 0\n",
    "for i in range(len(X_test)):\n",
    "\n",
    "    current_row = X_test[i]\n",
    "    current_row = current_row.reshape(1, -1)\n",
    "    prediction = loaded_clf.predict(current_row)\n",
    "    actual = y_test[i]\n",
    "    if prediction[0] != actual :\n",
    "        mis+=1\n",
    "        print(i, prediction, actual)\n",
    "print(mis)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "557\n"
     ]
    }
   ],
   "source": [
    "print(mis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Encodings:\n",
      "Benign: 0\n",
      "Brute Force -Web: 1\n",
      "Brute Force -XSS: 2\n",
      "SQL Injection: 3\n"
     ]
    }
   ],
   "source": [
    "label_encodings = LE.classes_\n",
    "print(\"Label Encodings:\")\n",
    "for label, encoding in zip(label_encodings, range(len(label_encodings))):\n",
    "    print(f\"{label}: {encoding}\")\n",
    "\n",
    "# Print the predicted labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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