bot-attack.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>443</td>\n",
       "      <td>6</td>\n",
       "      <td>02/03/2018 08:47:38</td>\n",
       "      <td>141385</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>553</td>\n",
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       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49684</td>\n",
       "      <td>6</td>\n",
       "      <td>02/03/2018 08:47:38</td>\n",
       "      <td>281</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>443</td>\n",
       "      <td>6</td>\n",
       "      <td>02/03/2018 08:47:40</td>\n",
       "      <td>279824</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>1086</td>\n",
       "      <td>10527.0</td>\n",
       "      <td>385</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>443</td>\n",
       "      <td>6</td>\n",
       "      <td>02/03/2018 08:47:40</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</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>443</td>\n",
       "      <td>6</td>\n",
       "      <td>02/03/2018 08:47:41</td>\n",
       "      <td>274016</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>1285</td>\n",
       "      <td>6141.0</td>\n",
       "      <td>517</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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       443         6  02/03/2018 08:47:38         141385             9   \n",
       "1     49684         6  02/03/2018 08:47:38            281             2   \n",
       "2       443         6  02/03/2018 08:47:40         279824            11   \n",
       "3       443         6  02/03/2018 08:47:40            132             2   \n",
       "4       443         6  02/03/2018 08:47:41         274016             9   \n",
       "\n",
       "   tot_bwd_pkts  totlen_fwd_pkts  totlen_bwd_pkts  fwd_pkt_len_max  \\\n",
       "0             7              553           3773.0              202   \n",
       "1             1               38              0.0               38   \n",
       "2            15             1086          10527.0              385   \n",
       "3             0                0              0.0                0   \n",
       "4            13             1285           6141.0              517   \n",
       "\n",
       "   fwd_pkt_len_min  ...  fwd_seg_size_min  active_mean  active_std  \\\n",
       "0                0  ...                20          0.0         0.0   \n",
       "1                0  ...                20          0.0         0.0   \n",
       "2                0  ...                20          0.0         0.0   \n",
       "3                0  ...                20          0.0         0.0   \n",
       "4                0  ...                20          0.0         0.0   \n",
       "\n",
       "   active_max  active_min  idle_mean  idle_std  idle_max  idle_min   label  \n",
       "0         0.0         0.0        0.0       0.0       0.0       0.0  Benign  \n",
       "1         0.0         0.0        0.0       0.0       0.0       0.0  Benign  \n",
       "2         0.0         0.0        0.0       0.0       0.0       0.0  Benign  \n",
       "3         0.0         0.0        0.0       0.0       0.0       0.0  Benign  \n",
       "4         0.0         0.0        0.0       0.0       0.0       0.0  Benign  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('bot_02-03-2018.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "Benign    762384\n",
       "Bot       286191\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": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <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",
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       "      <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",
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       "      <th>idle_mean</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
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       "      <td>61.444444</td>\n",
       "      <td>87.534438</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>30597.305230</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
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       "      <td>38</td>\n",
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       "      <td>19.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>129.392497</td>\n",
       "      <td>701.800000</td>\n",
       "      <td>41501.086400</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",
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       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>6</td>\n",
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       "      <td>183.887722</td>\n",
       "      <td>472.384615</td>\n",
       "      <td>27100.607260</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>1047</td>\n",
       "      <td>20</td>\n",
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       "      <td>6</td>\n",
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       "      <td>8</td>\n",
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       "      <td>796.799143</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>20</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>6</td>\n",
       "      <td>3802316</td>\n",
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       "      <td>8</td>\n",
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       "      <td>191.172841</td>\n",
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       "      <td>833.965404</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>62560</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>6</td>\n",
       "      <td>4004239</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>1459</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>104.214286</td>\n",
       "      <td>195.113450</td>\n",
       "      <td>216.375000</td>\n",
       "      <td>796.655744</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>62626</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>1048573</th>\n",
       "      <td>6</td>\n",
       "      <td>3998435</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>1459</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>104.214286</td>\n",
       "      <td>195.113450</td>\n",
       "      <td>216.375000</td>\n",
       "      <td>797.812144</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>62541</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>3972651</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>1439</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>102.785714</td>\n",
       "      <td>191.197111</td>\n",
       "      <td>216.375000</td>\n",
       "      <td>797.955823</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>62646</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         141385             9             7              553   \n",
       "1               6            281             2             1               38   \n",
       "2               6         279824            11            15             1086   \n",
       "3               6            132             2             0                0   \n",
       "4               6         274016             9            13             1285   \n",
       "...           ...            ...           ...           ...              ...   \n",
       "1048570         6        3982183            14             8             1442   \n",
       "1048571         6        3802316            14             8             1440   \n",
       "1048572         6        4004239            14             8             1459   \n",
       "1048573         6        3998435            14             8             1459   \n",
       "1048574         6        3972651            14             8             1439   \n",
       "\n",
       "         totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
       "0                 3773.0         61.444444        87.534438        539.000000   \n",
       "1                    0.0         19.000000        26.870058          0.000000   \n",
       "2                10527.0         98.727273       129.392497        701.800000   \n",
       "3                    0.0          0.000000         0.000000          0.000000   \n",
       "4                 6141.0        142.777778       183.887722        472.384615   \n",
       "...                  ...               ...              ...               ...   \n",
       "1048570           1731.0        103.000000       191.125412        216.375000   \n",
       "1048571           1731.0        102.857143       191.172841        216.375000   \n",
       "1048572           1731.0        104.214286       195.113450        216.375000   \n",
       "1048573           1731.0        104.214286       195.113450        216.375000   \n",
       "1048574           1731.0        102.785714       191.197111        216.375000   \n",
       "\n",
       "           flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
       "0         30597.305230  ...             1             0             0   \n",
       "1        135231.316700  ...             0             1             0   \n",
       "2         41501.086400  ...             1             0             0   \n",
       "3             0.000000  ...             0             1             0   \n",
       "4         27100.607260  ...             1             0             0   \n",
       "...                ...  ...           ...           ...           ...   \n",
       "1048570     796.799143  ...             1             0             0   \n",
       "1048571     833.965404  ...             1             0             0   \n",
       "1048572     796.655744  ...             1             0             0   \n",
       "1048573     797.812144  ...             1             0             0   \n",
       "1048574     797.955823  ...             1             0             0   \n",
       "\n",
       "         down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  \\\n",
       "0                    0               8192                119   \n",
       "1                    0                123                  0   \n",
       "2                    1               8192               1047   \n",
       "3                    0                256                 -1   \n",
       "4                    1               8192               1047   \n",
       "...                ...                ...                ...   \n",
       "1048570              0               8192              62558   \n",
       "1048571              0               8192              62560   \n",
       "1048572              0               8192              62626   \n",
       "1048573              0               8192              62541   \n",
       "1048574              0               8192              62646   \n",
       "\n",
       "         fwd_seg_size_min  active_mean  idle_mean   label  \n",
       "0                      20          0.0        0.0  Benign  \n",
       "1                      20          0.0        0.0  Benign  \n",
       "2                      20          0.0        0.0  Benign  \n",
       "3                      20          0.0        0.0  Benign  \n",
       "4                      20          0.0        0.0  Benign  \n",
       "...                   ...          ...        ...     ...  \n",
       "1048570                20          0.0        0.0  Benign  \n",
       "1048571                20          0.0        0.0  Benign  \n",
       "1048572                20          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_48081/3086081652.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_48081/3086081652.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    832492\n",
       "2    202701\n",
       "0     13382\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         141385             9             7              553   \n",
      "1               1            281             2             1               38   \n",
      "2               1         279824            11            15             1086   \n",
      "3               1            132             2             0                0   \n",
      "4               1         274016             9            13             1285   \n",
      "...           ...            ...           ...           ...              ...   \n",
      "1048570         1        3982183            14             8             1442   \n",
      "1048571         1        3802316            14             8             1440   \n",
      "1048572         1        4004239            14             8             1459   \n",
      "1048573         1        3998435            14             8             1459   \n",
      "1048574         1        3972651            14             8             1439   \n",
      "\n",
      "         totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
      "0                 3773.0         61.444444        87.534438        539.000000   \n",
      "1                    0.0         19.000000        26.870058          0.000000   \n",
      "2                10527.0         98.727273       129.392497        701.800000   \n",
      "3                    0.0          0.000000         0.000000          0.000000   \n",
      "4                 6141.0        142.777778       183.887722        472.384615   \n",
      "...                  ...               ...              ...               ...   \n",
      "1048570           1731.0        103.000000       191.125412        216.375000   \n",
      "1048571           1731.0        102.857143       191.172841        216.375000   \n",
      "1048572           1731.0        104.214286       195.113450        216.375000   \n",
      "1048573           1731.0        104.214286       195.113450        216.375000   \n",
      "1048574           1731.0        102.785714       191.197111        216.375000   \n",
      "\n",
      "           flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
      "0         30597.305230  ...             1             0             0   \n",
      "1        135231.316700  ...             0             1             0   \n",
      "2         41501.086400  ...             1             0             0   \n",
      "3             0.000000  ...             0             1             0   \n",
      "4         27100.607260  ...             1             0             0   \n",
      "...                ...  ...           ...           ...           ...   \n",
      "1048570     796.799143  ...             1             0             0   \n",
      "1048571     833.965404  ...             1             0             0   \n",
      "1048572     796.655744  ...             1             0             0   \n",
      "1048573     797.812144  ...             1             0             0   \n",
      "1048574     797.955823  ...             1             0             0   \n",
      "\n",
      "         down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  \\\n",
      "0                    0               8192                119   \n",
      "1                    0                123                  0   \n",
      "2                    1               8192               1047   \n",
      "3                    0                256                 -1   \n",
      "4                    1               8192               1047   \n",
      "...                ...                ...                ...   \n",
      "1048570              0               8192              62558   \n",
      "1048571              0               8192              62560   \n",
      "1048572              0               8192              62626   \n",
      "1048573              0               8192              62541   \n",
      "1048574              0               8192              62646   \n",
      "\n",
      "         fwd_seg_size_min  active_mean  idle_mean  label  \n",
      "0                      20          0.0        0.0      0  \n",
      "1                      20          0.0        0.0      0  \n",
      "2                      20          0.0        0.0      0  \n",
      "3                      20          0.0        0.0      0  \n",
      "4                      20          0.0        0.0      0  \n",
      "...                   ...          ...        ...    ...  \n",
      "1048570                20          0.0        0.0      0  \n",
      "1048571                20          0.0        0.0      0  \n",
      "1048572                20          0.0        0.0      0  \n",
      "1048573                20          0.0        0.0      0  \n",
      "1048574                20          0.0        0.0      0  \n",
      "\n",
      "[1044525 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_48081/2687928747.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    758334\n",
       "1    286191\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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (1044525, 31)\n",
      "Shape of y: (1044525,)\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": 17,
   "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>141385</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>553</td>\n",
       "      <td>3773.0</td>\n",
       "      <td>61.444444</td>\n",
       "      <td>87.534438</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>30597.305230</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>119</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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>281</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>26.870058</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>135231.316700</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>279824</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>1086</td>\n",
       "      <td>10527.0</td>\n",
       "      <td>98.727273</td>\n",
       "      <td>129.392497</td>\n",
       "      <td>701.800000</td>\n",
       "      <td>41501.086400</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>1047</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>-1</td>\n",
       "      <td>20</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>274016</td>\n",
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       "      <td>13</td>\n",
       "      <td>1285</td>\n",
       "      <td>6141.0</td>\n",
       "      <td>142.777778</td>\n",
       "      <td>183.887722</td>\n",
       "      <td>472.384615</td>\n",
       "      <td>27100.607260</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8192</td>\n",
       "      <td>1047</td>\n",
       "      <td>20</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>1</td>\n",
       "      <td>3982183</td>\n",
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       "      <td>216.375000</td>\n",
       "      <td>796.799143</td>\n",
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       "      <td>8192</td>\n",
       "      <td>62558</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>1048571</th>\n",
       "      <td>1</td>\n",
       "      <td>3802316</td>\n",
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       "      <td>8</td>\n",
       "      <td>1440</td>\n",
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       "      <td>102.857143</td>\n",
       "      <td>191.172841</td>\n",
       "      <td>216.375000</td>\n",
       "      <td>833.965404</td>\n",
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       "    <tr>\n",
       "      <th>1048572</th>\n",
       "      <td>1</td>\n",
       "      <td>4004239</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>1459</td>\n",
       "      <td>1731.0</td>\n",
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       "      <td>195.113450</td>\n",
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       "    <tr>\n",
       "      <th>1048573</th>\n",
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       "    <tr>\n",
       "      <th>1048574</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1044525 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         protocol  flow_duration  tot_fwd_pkts  tot_bwd_pkts  totlen_fwd_pkts  \\\n",
       "0               1         141385             9             7              553   \n",
       "1               1            281             2             1               38   \n",
       "2               1         279824            11            15             1086   \n",
       "3               1            132             2             0                0   \n",
       "4               1         274016             9            13             1285   \n",
       "...           ...            ...           ...           ...              ...   \n",
       "1048570         1        3982183            14             8             1442   \n",
       "1048571         1        3802316            14             8             1440   \n",
       "1048572         1        4004239            14             8             1459   \n",
       "1048573         1        3998435            14             8             1459   \n",
       "1048574         1        3972651            14             8             1439   \n",
       "\n",
       "         totlen_bwd_pkts  fwd_pkt_len_mean  fwd_pkt_len_std  bwd_pkt_len_mean  \\\n",
       "0                 3773.0         61.444444        87.534438        539.000000   \n",
       "1                    0.0         19.000000        26.870058          0.000000   \n",
       "2                10527.0         98.727273       129.392497        701.800000   \n",
       "3                    0.0          0.000000         0.000000          0.000000   \n",
       "4                 6141.0        142.777778       183.887722        472.384615   \n",
       "...                  ...               ...              ...               ...   \n",
       "1048570           1731.0        103.000000       191.125412        216.375000   \n",
       "1048571           1731.0        102.857143       191.172841        216.375000   \n",
       "1048572           1731.0        104.214286       195.113450        216.375000   \n",
       "1048573           1731.0        104.214286       195.113450        216.375000   \n",
       "1048574           1731.0        102.785714       191.197111        216.375000   \n",
       "\n",
       "           flow_byts_s  ...  psh_flag_cnt  ack_flag_cnt  urg_flag_cnt  \\\n",
       "0         30597.305230  ...             1             0             0   \n",
       "1        135231.316700  ...             0             1             0   \n",
       "2         41501.086400  ...             1             0             0   \n",
       "3             0.000000  ...             0             1             0   \n",
       "4         27100.607260  ...             1             0             0   \n",
       "...                ...  ...           ...           ...           ...   \n",
       "1048570     796.799143  ...             1             0             0   \n",
       "1048571     833.965404  ...             1             0             0   \n",
       "1048572     796.655744  ...             1             0             0   \n",
       "1048573     797.812144  ...             1             0             0   \n",
       "1048574     797.955823  ...             1             0             0   \n",
       "\n",
       "         down_up_ratio  init_fwd_win_byts  init_bwd_win_byts  \\\n",
       "0                    0               8192                119   \n",
       "1                    0                123                  0   \n",
       "2                    1               8192               1047   \n",
       "3                    0                256                 -1   \n",
       "4                    1               8192               1047   \n",
       "...                ...                ...                ...   \n",
       "1048570              0               8192              62558   \n",
       "1048571              0               8192              62560   \n",
       "1048572              0               8192              62626   \n",
       "1048573              0               8192              62541   \n",
       "1048574              0               8192              62646   \n",
       "\n",
       "         fwd_seg_size_min  active_mean  idle_mean  label  \n",
       "0                      20          0.0        0.0      0  \n",
       "1                      20          0.0        0.0      0  \n",
       "2                      20          0.0        0.0      0  \n",
       "3                      20          0.0        0.0      0  \n",
       "4                      20          0.0        0.0      0  \n",
       "...                   ...          ...        ...    ...  \n",
       "1048570                20          0.0        0.0      0  \n",
       "1048571                20          0.0        0.0      0  \n",
       "1048572                20          0.0        0.0      0  \n",
       "1048573                20          0.0        0.0      0  \n",
       "1048574                20          0.0        0.0      0  \n",
       "\n",
       "[1044525 rows x 32 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "0    758334\n",
       "1    286191\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number transactions X_train dataset:  (835620, 31)\n",
      "Number transactions y_train dataset:  (835620,)\n",
      "Number transactions X_test dataset:  (208905, 31)\n",
      "Number transactions y_test dataset:  (208905,)\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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '0': 606617\n",
      "Before OverSampling, counts of label '1': 229003\n",
      "Before OverSampling, counts of label '2': 0\n",
      "Before OverSampling, counts of label '3': 0\n",
      "After OverSampling, the shape of train_X: (970406, 31)\n",
      "After OverSampling, the shape of train_y: (970406,) \n",
      "\n",
      "After OverSampling, counts of label '0': 485203\n",
      "After OverSampling, counts of label '1': 485203\n",
      "After OverSampling, counts of label '2': 0\n",
      "After OverSampling, counts of label '3': 0\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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 8 candidates, totalling 16 fits\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.990 total time=   3.5s\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=0.990 total time=   3.5s\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.997 total time=   5.6s\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.999 total time=   4.3s\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=0.997 total time=   5.9s\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=0.998 total time=   4.4s\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.998 total time=   3.7s\n",
      "[CV 1/2] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.999 total time=   7.3s\n",
      "[CV 2/2] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=0.999 total time=   7.3s\n",
      "[CV 2/2] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.998 total time=   3.8s\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.999 total time=   6.0s\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=1.000 total time=   4.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karthiknamboori/Desktop/Hackathons/Vajra/.venv/lib/python3.11/site-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/2] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=1.000 total time=   6.0s\n",
      "[CV 2/2] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=1.000 total time=   3.7s\n",
      "[CV 1/2] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=1.000 total time=   5.5s\n",
      "[CV 2/2] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=1.000 total time=   4.7s\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",
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       "  --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",
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       "}\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": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9997008209473206"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9997080012445848"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[151665,     52],\n",
       "       [     9,  57179]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_test_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "668496"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_t.size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167124"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_v.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "208905"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import dump, load\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bot_02-03-2018.joblib']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(clf, \"bot_02-03-2018.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_clf = load(\"bruteforce-web-xss_sql-injection_23-02-2018new.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_row = X_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_row = current_row.reshape(1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4290 [1] 0\n",
      "13879 [1] 0\n",
      "15448 [1] 0\n",
      "15918 [1] 0\n",
      "16716 [1] 0\n",
      "17515 [1] 0\n",
      "20946 [0] 1\n",
      "25608 [0] 1\n",
      "28750 [0] 1\n",
      "28898 [1] 0\n",
      "29106 [1] 0\n",
      "30621 [1] 0\n",
      "39221 [1] 0\n",
      "41025 [1] 0\n",
      "46507 [1] 0\n",
      "47159 [1] 0\n",
      "52541 [1] 0\n",
      "52711 [1] 0\n",
      "59605 [0] 1\n",
      "65811 [1] 0\n",
      "67635 [1] 0\n",
      "70394 [1] 0\n",
      "75368 [1] 0\n",
      "76126 [1] 0\n",
      "76436 [0] 1\n",
      "76646 [1] 0\n",
      "79429 [1] 0\n",
      "85443 [1] 0\n",
      "87246 [1] 0\n",
      "93819 [1] 0\n",
      "103699 [1] 0\n",
      "105572 [1] 0\n",
      "108689 [1] 0\n",
      "110721 [0] 1\n",
      "120210 [1] 0\n",
      "122400 [1] 0\n",
      "125247 [1] 0\n",
      "125438 [1] 0\n",
      "125886 [1] 0\n",
      "129150 [1] 0\n",
      "131825 [1] 0\n",
      "140731 [1] 0\n",
      "141515 [1] 0\n",
      "147808 [1] 0\n",
      "149316 [1] 0\n",
      "150000 [0] 1\n",
      "156923 [1] 0\n",
      "159423 [1] 0\n",
      "162034 [1] 0\n",
      "168729 [1] 0\n",
      "172335 [1] 0\n",
      "175538 [1] 0\n",
      "176441 [1] 0\n",
      "180008 [1] 0\n",
      "180727 [1] 0\n",
      "184226 [1] 0\n",
      "185808 [1] 0\n",
      "193911 [0] 1\n",
      "195721 [1] 0\n",
      "203179 [0] 1\n",
      "206222 [1] 0\n",
      "61\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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "61\n"
     ]
    }
   ],
   "source": [
    "print(mis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Encodings:\n",
      "Benign: 0\n",
      "Bot: 1\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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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