training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "path = \"/home/jakhongir/Documents/data/CSVs/ISCX_Botnet-Training.pcap_Flow.csv\"\n",
    "\n",
    "df = pandas.read_csv(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow ID</th>\n",
       "      <th>Src IP</th>\n",
       "      <th>Src Port</th>\n",
       "      <th>Dst IP</th>\n",
       "      <th>Dst Port</th>\n",
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       "      <th>Total Fwd Packet</th>\n",
       "      <th>Total Bwd packets</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",
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       "      <td>137</td>\n",
       "      <td>17</td>\n",
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       "      <td>12452268</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 84 columns</p>\n",
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      "text/plain": [
       "                                  Flow ID         Src IP  Src Port   \n",
       "0                   8.6.0.1-8.0.6.4-0-0-0        8.6.0.1         0  \\\n",
       "1  147.32.84.180-74.125.232.195-1027-80-6  147.32.84.180      1027   \n",
       "2  147.32.84.180-147.32.84.255-137-137-17  147.32.84.180       137   \n",
       "3    147.32.84.180-147.32.80.9-1025-53-17  147.32.84.180      1025   \n",
       "4   147.32.84.180-94.63.149.152-1040-80-6  147.32.84.180      1040   \n",
       "\n",
       "           Dst IP  Dst Port  Protocol               Timestamp  Flow Duration   \n",
       "0         8.0.6.4         0         0  10/08/2011 02:01:40 PM       59086131  \\\n",
       "1  74.125.232.195        80         6  10/08/2011 02:04:26 PM          45126   \n",
       "2   147.32.84.255       137        17  10/08/2011 02:04:23 PM       12452268   \n",
       "3     147.32.80.9        53        17  10/08/2011 02:04:24 PM      118741070   \n",
       "4   94.63.149.152        80         6  10/08/2011 02:06:36 PM         180643   \n",
       "\n",
       "   Total Fwd Packet  Total Bwd packets  ...  Fwd Seg Size Min  Active Mean   \n",
       "0                 8                  0  ...                 0    2987276.0  \\\n",
       "1                 7                  3  ...                20          0.0   \n",
       "2                38                  0  ...                 8          0.0   \n",
       "3                 6                  3  ...                 8    2276383.0   \n",
       "4                26                 10  ...                20          0.0   \n",
       "\n",
       "   Active Std  Active Max  Active Min     Idle Mean      Idle Std   \n",
       "0         0.0   2987276.0   2987276.0  1.869962e+07  1.947112e+07  \\\n",
       "1         0.0         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "2         0.0         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "3         0.0   2276383.0   2276383.0  1.161281e+08  0.000000e+00   \n",
       "4         0.0         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "\n",
       "      Idle Max     Idle Min            Label  \n",
       "0   41116855.0    5999291.0  NeedManualLabel  \n",
       "1          0.0          0.0  NeedManualLabel  \n",
       "2          0.0          0.0  NeedManualLabel  \n",
       "3  116128125.0  116128125.0  NeedManualLabel  \n",
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       "\n",
       "[5 rows x 84 columns]"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ip_addresses = [\n",
    "    '192.168.2.112',\n",
    "    '131.202.243.84',\n",
    "    '192.168.5.122',\n",
    "    '198.164.30.2',\n",
    "    '192.168.2.110',\n",
    "    '192.168.4.118',\n",
    "    '192.168.2.113',\n",
    "    '192.168.1.103',\n",
    "    '192.168.4.120',\n",
    "    '192.168.2.112',\n",
    "    '192.168.2.110',\n",
    "    '192.168.4.120',\n",
    "    '192.168.1.103',\n",
    "    '192.168.2.113',\n",
    "    '192.168.4.118',\n",
    "    '192.168.2.109',\n",
    "    '192.168.2.105',\n",
    "    '192.168.1.105',\n",
    "    '192.168.5.122',\n",
    "    '147.32.84.180',\n",
    "    '147.32.84.170',\n",
    "    '147.32.84.150',\n",
    "    '147.32.84.140',\n",
    "    '147.32.84.130',\n",
    "    '147.32.84.160',\n",
    "    '10.0.2.15',\n",
    "    '192.168.106.141',\n",
    "    '192.168.106.131',\n",
    "    '172.16.253.130',\n",
    "    '172.16.253.131',\n",
    "    '172.16.253.129',\n",
    "    '172.16.253.240',\n",
    "    '74.78.117.238',\n",
    "    '158.65.110.24',\n",
    "    '192.168.3.35',\n",
    "    '192.168.3.25',\n",
    "    '192.168.3.65',\n",
    "    '172.29.0.116',\n",
    "    '172.29.0.109',\n",
    "    '172.16.253.132',\n",
    "    '192.168.248.165',\n",
    "    '10.37.130.4'\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Flow ID', 'Src IP', 'Src Port', 'Dst IP', 'Dst Port', 'Protocol',\n",
       "       'Timestamp', 'Flow Duration', 'Total Fwd Packet', 'Total Bwd packets',\n",
       "       'Total Length of Fwd Packet', 'Total Length of Bwd Packet',\n",
       "       'Fwd Packet Length Max', 'Fwd Packet Length Min',\n",
       "       'Fwd Packet Length Mean', 'Fwd Packet Length Std',\n",
       "       'Bwd Packet Length Max', 'Bwd Packet Length Min',\n",
       "       'Bwd Packet Length Mean', 'Bwd Packet Length Std', 'Flow Bytes/s',\n",
       "       'Flow Packets/s', 'Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max',\n",
       "       'Flow IAT Min', 'Fwd IAT Total', 'Fwd IAT Mean', 'Fwd IAT Std',\n",
       "       'Fwd IAT Max', 'Fwd IAT Min', 'Bwd IAT Total', 'Bwd IAT Mean',\n",
       "       'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min', 'Fwd PSH Flags',\n",
       "       'Bwd PSH Flags', 'Fwd URG Flags', 'Bwd URG Flags', 'Fwd Header Length',\n",
       "       'Bwd Header Length', 'Fwd Packets/s', 'Bwd Packets/s',\n",
       "       'Packet Length Min', 'Packet Length Max', 'Packet Length Mean',\n",
       "       'Packet Length Std', 'Packet Length Variance', 'FIN Flag Count',\n",
       "       'SYN Flag Count', 'RST Flag Count', 'PSH Flag Count', 'ACK Flag Count',\n",
       "       'URG Flag Count', 'CWR Flag Count', 'ECE Flag Count', 'Down/Up Ratio',\n",
       "       'Average Packet Size', 'Fwd Segment Size Avg', 'Bwd Segment Size Avg',\n",
       "       'Fwd Bytes/Bulk Avg', 'Fwd Packet/Bulk Avg', 'Fwd Bulk Rate Avg',\n",
       "       'Bwd Bytes/Bulk Avg', 'Bwd Packet/Bulk Avg', 'Bwd Bulk Rate Avg',\n",
       "       'Subflow Fwd Packets', 'Subflow Fwd Bytes', 'Subflow Bwd Packets',\n",
       "       'Subflow Bwd Bytes', 'FWD Init Win Bytes', 'Bwd Init Win Bytes',\n",
       "       'Fwd Act Data Pkts', 'Fwd Seg Size Min', 'Active Mean', 'Active Std',\n",
       "       'Active Max', 'Active Min', 'Idle Mean', 'Idle Std', 'Idle Max',\n",
       "       'Idle Min', 'Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Label'] = 'Benign'\n",
    "\n",
    "# Update the 'Label' column to 'Malicious' if Source or Destination IP is in the ip_addresses list\n",
    "df.loc[df['Src IP'].isin(ip_addresses) | df['Dst IP'].isin(ip_addresses), 'Label'] = 'Malicious'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Flow ID</th>\n",
       "      <th>Src IP</th>\n",
       "      <th>Src Port</th>\n",
       "      <th>Dst IP</th>\n",
       "      <th>Dst Port</th>\n",
       "      <th>Protocol</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packet</th>\n",
       "      <th>Total Bwd packets</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",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>8.6.0.1-8.0.6.4-0-0-0</td>\n",
       "      <td>8.6.0.1</td>\n",
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       "      <td>1.869962e+07</td>\n",
       "      <td>1.947112e+07</td>\n",
       "      <td>41116855.0</td>\n",
       "      <td>5999291.0</td>\n",
       "      <td>Benign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>147.32.84.180-74.125.232.195-1027-80-6</td>\n",
       "      <td>147.32.84.180</td>\n",
       "      <td>1027</td>\n",
       "      <td>74.125.232.195</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>10/08/2011 02:04:26 PM</td>\n",
       "      <td>45126</td>\n",
       "      <td>7</td>\n",
       "      <td>3</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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>147.32.84.180-147.32.84.255-137-137-17</td>\n",
       "      <td>147.32.84.180</td>\n",
       "      <td>137</td>\n",
       "      <td>147.32.84.255</td>\n",
       "      <td>137</td>\n",
       "      <td>17</td>\n",
       "      <td>10/08/2011 02:04:23 PM</td>\n",
       "      <td>12452268</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>147.32.84.180-147.32.80.9-1025-53-17</td>\n",
       "      <td>147.32.84.180</td>\n",
       "      <td>1025</td>\n",
       "      <td>147.32.80.9</td>\n",
       "      <td>53</td>\n",
       "      <td>17</td>\n",
       "      <td>10/08/2011 02:04:24 PM</td>\n",
       "      <td>118741070</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
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       "      <td>0.000000e+00</td>\n",
       "      <td>116128125.0</td>\n",
       "      <td>116128125.0</td>\n",
       "      <td>Malicious</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>147.32.84.180-94.63.149.152-1040-80-6</td>\n",
       "      <td>147.32.84.180</td>\n",
       "      <td>1040</td>\n",
       "      <td>94.63.149.152</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>10/08/2011 02:06:36 PM</td>\n",
       "      <td>180643</td>\n",
       "      <td>26</td>\n",
       "      <td>10</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>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Malicious</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Flow ID         Src IP  Src Port   \n",
       "0                   8.6.0.1-8.0.6.4-0-0-0        8.6.0.1         0  \\\n",
       "1  147.32.84.180-74.125.232.195-1027-80-6  147.32.84.180      1027   \n",
       "2  147.32.84.180-147.32.84.255-137-137-17  147.32.84.180       137   \n",
       "3    147.32.84.180-147.32.80.9-1025-53-17  147.32.84.180      1025   \n",
       "4   147.32.84.180-94.63.149.152-1040-80-6  147.32.84.180      1040   \n",
       "\n",
       "           Dst IP  Dst Port  Protocol               Timestamp  Flow Duration   \n",
       "0         8.0.6.4         0         0  10/08/2011 02:01:40 PM       59086131  \\\n",
       "1  74.125.232.195        80         6  10/08/2011 02:04:26 PM          45126   \n",
       "2   147.32.84.255       137        17  10/08/2011 02:04:23 PM       12452268   \n",
       "3     147.32.80.9        53        17  10/08/2011 02:04:24 PM      118741070   \n",
       "4   94.63.149.152        80         6  10/08/2011 02:06:36 PM         180643   \n",
       "\n",
       "   Total Fwd Packet  Total Bwd packets  ...  Fwd Seg Size Min  Active Mean   \n",
       "0                 8                  0  ...                 0    2987276.0  \\\n",
       "1                 7                  3  ...                20          0.0   \n",
       "2                38                  0  ...                 8          0.0   \n",
       "3                 6                  3  ...                 8    2276383.0   \n",
       "4                26                 10  ...                20          0.0   \n",
       "\n",
       "   Active Std  Active Max  Active Min     Idle Mean      Idle Std   \n",
       "0         0.0   2987276.0   2987276.0  1.869962e+07  1.947112e+07  \\\n",
       "1         0.0         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "2         0.0         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "3         0.0   2276383.0   2276383.0  1.161281e+08  0.000000e+00   \n",
       "4         0.0         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "\n",
       "      Idle Max     Idle Min      Label  \n",
       "0   41116855.0    5999291.0     Benign  \n",
       "1          0.0          0.0  Malicious  \n",
       "2          0.0          0.0  Malicious  \n",
       "3  116128125.0  116128125.0  Malicious  \n",
       "4          0.0          0.0  Malicious  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Label\n",
       "Malicious    208721\n",
       "Benign       123131\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"/home/jakhongir/Documents/data/CSVs/ISCX_Botnet-Training.pcap_Flow_labelled.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input X contains infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[34], line 49\u001b[0m\n\u001b[1;32m     46\u001b[0m pipeline \u001b[39m=\u001b[39m make_pipeline(preprocessor, model)\n\u001b[1;32m     48\u001b[0m \u001b[39m# Fit the pipeline on the training data\u001b[39;00m\n\u001b[0;32m---> 49\u001b[0m pipeline\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[1;32m     51\u001b[0m \u001b[39m# Predict on the validation data\u001b[39;00m\n\u001b[1;32m     52\u001b[0m y_pred \u001b[39m=\u001b[39m pipeline\u001b[39m.\u001b[39mpredict(X_val)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/pipeline.py:401\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Fit the model.\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \n\u001b[1;32m    377\u001b[0m \u001b[39mFit all the transformers one after the other and transform the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[39m    Pipeline with fitted steps.\u001b[39;00m\n\u001b[1;32m    399\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    400\u001b[0m fit_params_steps \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_fit_params(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\n\u001b[0;32m--> 401\u001b[0m Xt \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params_steps)\n\u001b[1;32m    402\u001b[0m \u001b[39mwith\u001b[39;00m _print_elapsed_time(\u001b[39m\"\u001b[39m\u001b[39mPipeline\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_log_message(\u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps) \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m)):\n\u001b[1;32m    403\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_final_estimator \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mpassthrough\u001b[39m\u001b[39m\"\u001b[39m:\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/pipeline.py:359\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[0;34m(self, X, y, **fit_params_steps)\u001b[0m\n\u001b[1;32m    357\u001b[0m     cloned_transformer \u001b[39m=\u001b[39m clone(transformer)\n\u001b[1;32m    358\u001b[0m \u001b[39m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m X, fitted_transformer \u001b[39m=\u001b[39m fit_transform_one_cached(\n\u001b[1;32m    360\u001b[0m     cloned_transformer,\n\u001b[1;32m    361\u001b[0m     X,\n\u001b[1;32m    362\u001b[0m     y,\n\u001b[1;32m    363\u001b[0m     \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m    364\u001b[0m     message_clsname\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mPipeline\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    365\u001b[0m     message\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_log_message(step_idx),\n\u001b[1;32m    366\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params_steps[name],\n\u001b[1;32m    367\u001b[0m )\n\u001b[1;32m    368\u001b[0m \u001b[39m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[1;32m    369\u001b[0m \u001b[39m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[39m# from the cache.\u001b[39;00m\n\u001b[1;32m    371\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps[step_idx] \u001b[39m=\u001b[39m (name, fitted_transformer)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/joblib/memory.py:349\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m--> 349\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunc(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/pipeline.py:893\u001b[0m, in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    891\u001b[0m \u001b[39mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[1;32m    892\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(transformer, \u001b[39m\"\u001b[39m\u001b[39mfit_transform\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 893\u001b[0m         res \u001b[39m=\u001b[39m transformer\u001b[39m.\u001b[39;49mfit_transform(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params)\n\u001b[1;32m    894\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    895\u001b[0m         res \u001b[39m=\u001b[39m transformer\u001b[39m.\u001b[39mfit(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\u001b[39m.\u001b[39mtransform(X)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/pipeline.py:437\u001b[0m, in \u001b[0;36mPipeline.fit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Fit the model and transform with the final estimator.\u001b[39;00m\n\u001b[1;32m    411\u001b[0m \n\u001b[1;32m    412\u001b[0m \u001b[39mFits all the transformers one after the other and transform the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    434\u001b[0m \u001b[39m    Transformed samples.\u001b[39;00m\n\u001b[1;32m    435\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    436\u001b[0m fit_params_steps \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_fit_params(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\n\u001b[0;32m--> 437\u001b[0m Xt \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params_steps)\n\u001b[1;32m    439\u001b[0m last_step \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_final_estimator\n\u001b[1;32m    440\u001b[0m \u001b[39mwith\u001b[39;00m _print_elapsed_time(\u001b[39m\"\u001b[39m\u001b[39mPipeline\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_log_message(\u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps) \u001b[39m-\u001b[39m \u001b[39m1\u001b[39m)):\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/pipeline.py:359\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[0;34m(self, X, y, **fit_params_steps)\u001b[0m\n\u001b[1;32m    357\u001b[0m     cloned_transformer \u001b[39m=\u001b[39m clone(transformer)\n\u001b[1;32m    358\u001b[0m \u001b[39m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[0;32m--> 359\u001b[0m X, fitted_transformer \u001b[39m=\u001b[39m fit_transform_one_cached(\n\u001b[1;32m    360\u001b[0m     cloned_transformer,\n\u001b[1;32m    361\u001b[0m     X,\n\u001b[1;32m    362\u001b[0m     y,\n\u001b[1;32m    363\u001b[0m     \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m    364\u001b[0m     message_clsname\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mPipeline\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    365\u001b[0m     message\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_log_message(step_idx),\n\u001b[1;32m    366\u001b[0m     \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params_steps[name],\n\u001b[1;32m    367\u001b[0m )\n\u001b[1;32m    368\u001b[0m \u001b[39m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[1;32m    369\u001b[0m \u001b[39m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[1;32m    370\u001b[0m \u001b[39m# from the cache.\u001b[39;00m\n\u001b[1;32m    371\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msteps[step_idx] \u001b[39m=\u001b[39m (name, fitted_transformer)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/joblib/memory.py:349\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m--> 349\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfunc(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/pipeline.py:893\u001b[0m, in \u001b[0;36m_fit_transform_one\u001b[0;34m(transformer, X, y, weight, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    891\u001b[0m \u001b[39mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[1;32m    892\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(transformer, \u001b[39m\"\u001b[39m\u001b[39mfit_transform\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 893\u001b[0m         res \u001b[39m=\u001b[39m transformer\u001b[39m.\u001b[39;49mfit_transform(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params)\n\u001b[1;32m    894\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    895\u001b[0m         res \u001b[39m=\u001b[39m transformer\u001b[39m.\u001b[39mfit(X, y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\u001b[39m.\u001b[39mtransform(X)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/utils/_set_output.py:140\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[39m@wraps\u001b[39m(f)\n\u001b[1;32m    139\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwrapped\u001b[39m(\u001b[39mself\u001b[39m, X, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m--> 140\u001b[0m     data_to_wrap \u001b[39m=\u001b[39m f(\u001b[39mself\u001b[39;49m, X, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    141\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(data_to_wrap, \u001b[39mtuple\u001b[39m):\n\u001b[1;32m    142\u001b[0m         \u001b[39m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m    143\u001b[0m         \u001b[39mreturn\u001b[39;00m (\n\u001b[1;32m    144\u001b[0m             _wrap_data_with_container(method, data_to_wrap[\u001b[39m0\u001b[39m], X, \u001b[39mself\u001b[39m),\n\u001b[1;32m    145\u001b[0m             \u001b[39m*\u001b[39mdata_to_wrap[\u001b[39m1\u001b[39m:],\n\u001b[1;32m    146\u001b[0m         )\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/base.py:881\u001b[0m, in \u001b[0;36mTransformerMixin.fit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    878\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfit(X, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfit_params)\u001b[39m.\u001b[39mtransform(X)\n\u001b[1;32m    879\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    880\u001b[0m     \u001b[39m# fit method of arity 2 (supervised transformation)\u001b[39;00m\n\u001b[0;32m--> 881\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mfit_params)\u001b[39m.\u001b[39mtransform(X)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/impute/_base.py:390\u001b[0m, in \u001b[0;36mSimpleImputer.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    381\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mdeprecated\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    382\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m    383\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mThe \u001b[39m\u001b[39m'\u001b[39m\u001b[39mverbose\u001b[39m\u001b[39m'\u001b[39m\u001b[39m parameter was deprecated in version \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    384\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m1.1 and will be removed in 1.3. A warning will \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m    388\u001b[0m     )\n\u001b[0;32m--> 390\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_input(X, in_fit\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m    392\u001b[0m \u001b[39m# default fill_value is 0 for numerical input and \"missing_value\"\u001b[39;00m\n\u001b[1;32m    393\u001b[0m \u001b[39m# otherwise\u001b[39;00m\n\u001b[1;32m    394\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfill_value \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/impute/_base.py:344\u001b[0m, in \u001b[0;36mSimpleImputer._validate_input\u001b[0;34m(self, X, in_fit)\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[39mraise\u001b[39;00m new_ve \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    343\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 344\u001b[0m         \u001b[39mraise\u001b[39;00m ve\n\u001b[1;32m    346\u001b[0m \u001b[39mif\u001b[39;00m in_fit:\n\u001b[1;32m    347\u001b[0m     \u001b[39m# Use the dtype seen in `fit` for non-`fit` conversion\u001b[39;00m\n\u001b[1;32m    348\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_fit_dtype \u001b[39m=\u001b[39m X\u001b[39m.\u001b[39mdtype\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/impute/_base.py:327\u001b[0m, in \u001b[0;36mSimpleImputer._validate_input\u001b[0;34m(self, X, in_fit)\u001b[0m\n\u001b[1;32m    324\u001b[0m     force_all_finite \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    326\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 327\u001b[0m     X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[1;32m    328\u001b[0m         X,\n\u001b[1;32m    329\u001b[0m         reset\u001b[39m=\u001b[39;49min_fit,\n\u001b[1;32m    330\u001b[0m         accept_sparse\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcsc\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    331\u001b[0m         dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m    332\u001b[0m         force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[1;32m    333\u001b[0m         copy\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcopy,\n\u001b[1;32m    334\u001b[0m     )\n\u001b[1;32m    335\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m ve:\n\u001b[1;32m    336\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcould not convert\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m \u001b[39mstr\u001b[39m(ve):\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/base.py:565\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    563\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mValidation should be done on X, y or both.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    564\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 565\u001b[0m     X \u001b[39m=\u001b[39m check_array(X, input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_params)\n\u001b[1;32m    566\u001b[0m     out \u001b[39m=\u001b[39m X\n\u001b[1;32m    567\u001b[0m \u001b[39melif\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/utils/validation.py:921\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    916\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mFound array with dim \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m expected <= 2.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    917\u001b[0m             \u001b[39m%\u001b[39m (array\u001b[39m.\u001b[39mndim, estimator_name)\n\u001b[1;32m    918\u001b[0m         )\n\u001b[1;32m    920\u001b[0m     \u001b[39mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 921\u001b[0m         _assert_all_finite(\n\u001b[1;32m    922\u001b[0m             array,\n\u001b[1;32m    923\u001b[0m             input_name\u001b[39m=\u001b[39;49minput_name,\n\u001b[1;32m    924\u001b[0m             estimator_name\u001b[39m=\u001b[39;49mestimator_name,\n\u001b[1;32m    925\u001b[0m             allow_nan\u001b[39m=\u001b[39;49mforce_all_finite \u001b[39m==\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mallow-nan\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    926\u001b[0m         )\n\u001b[1;32m    928\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_samples \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m    929\u001b[0m     n_samples \u001b[39m=\u001b[39m _num_samples(array)\n",
      "File \u001b[0;32m~/Documents/CICFlowMeter/.venv/lib/python3.10/site-packages/sklearn/utils/validation.py:161\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[39mif\u001b[39;00m estimator_name \u001b[39mand\u001b[39;00m input_name \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mand\u001b[39;00m has_nan_error:\n\u001b[1;32m    145\u001b[0m     \u001b[39m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    146\u001b[0m     \u001b[39m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    147\u001b[0m     msg_err \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[1;32m    148\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m does not accept missing values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    149\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m#estimators-that-handle-nan-values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m     )\n\u001b[0;32m--> 161\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg_err)\n",
      "\u001b[0;31mValueError\u001b[0m: Input X contains infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# Load the train and test data\n",
    "train_data = pd.read_csv('/home/jakhongir/Documents/data/CSVs/ISCX_Botnet-Training.pcap_Flow_labelled.csv')\n",
    "test_data = pd.read_csv('/home/jakhongir/Documents/data/CSVs/ISCX_Botnet-Testing.pcap_Flow_labelled.csv')\n",
    "\n",
    "# Separate the features and the labels\n",
    "X_train = train_data.drop('Label', axis=1).iloc[:, 7:]\n",
    "y_train = train_data['Label']\n",
    "X_test = test_data.drop('Label', axis=1).iloc[:, 7:]\n",
    "y_test = test_data['Label']\n",
    "\n",
    "# Encode the labels (if they are not already encoded)\n",
    "label_encoder = LabelEncoder()\n",
    "y_train = label_encoder.fit_transform(y_train)\n",
    "y_test = label_encoder.transform(y_test)\n",
    "\n",
    "# Split the train data into train and validation sets\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define the preprocessing pipeline with an imputer and scaler\n",
    "preprocessor = make_pipeline(\n",
    "    SimpleImputer(strategy='mean'),\n",
    "    StandardScaler()\n",
    ")\n",
    "\n",
    "# Initialize and fit the classification models\n",
    "models = [\n",
    "    DecisionTreeClassifier(),\n",
    "    LogisticRegression(),\n",
    "    RandomForestClassifier(),\n",
    "    SVC()\n",
    "]\n",
    "\n",
    "for model in models:\n",
    "    # Create a pipeline with the preprocessor and the model\n",
    "    pipeline = make_pipeline(preprocessor, model)\n",
    "\n",
    "    # Fit the pipeline on the training data\n",
    "    pipeline.fit(X_train, y_train)\n",
    "\n",
    "    # Predict on the validation data\n",
    "    y_pred = pipeline.predict(X_val)\n",
    "\n",
    "    # Evaluate the model\n",
    "    accuracy = accuracy_score(y_val, y_pred)\n",
    "    classification_report = classification_report(y_val, y_pred)\n",
    "\n",
    "    # Print the evaluation metrics\n",
    "    print(f\"Model: {type(model).__name__}\")\n",
    "    print(f\"Accuracy: {accuracy:.4f}\")\n",
    "    print(f\"Classification Report:\\n{classification_report}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in ./.venv/lib/python3.10/site-packages (from scikit-learn) (1.24.3)\n",
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      "\u001b[?25hCollecting joblib>=1.1.1 (from scikit-learn)\n",
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      "\u001b[?25hCollecting threadpoolctl>=2.0.0 (from scikit-learn)\n",
      "  Downloading threadpoolctl-3.1.0-py3-none-any.whl (14 kB)\n",
      "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
      "Successfully installed joblib-1.2.0 scikit-learn-1.2.2 scipy-1.10.1 threadpoolctl-3.1.0\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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