NIDS - ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "13b7ce1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import joblib\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af8d35ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"cicids.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb2edbad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop([' Fwd Header Length.1'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c1f1112",
   "metadata": {
    "scrolled": false
   },
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Backward Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Total Length of Bwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Fwd Packet Length Mean</th>\n",
       "      <th>Fwd Packet Length Std</th>\n",
       "      <th>...</th>\n",
       "      <th>min_seg_size_forward</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>54865</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55054</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55055</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46236</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54863</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042552</th>\n",
       "      <td>53</td>\n",
       "      <td>32215</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>112</td>\n",
       "      <td>152</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042553</th>\n",
       "      <td>53</td>\n",
       "      <td>324</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>84</td>\n",
       "      <td>362</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042554</th>\n",
       "      <td>58030</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>21.92031</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042555</th>\n",
       "      <td>53</td>\n",
       "      <td>1048635</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>256</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042556</th>\n",
       "      <td>53</td>\n",
       "      <td>94939</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>226</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1042557 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                    54865               3                   2   \n",
       "1                    55054             109                   1   \n",
       "2                    55055              52                   1   \n",
       "3                    46236              34                   1   \n",
       "4                    54863               3                   2   \n",
       "...                    ...             ...                 ...   \n",
       "1042552                 53           32215                   4   \n",
       "1042553                 53             324                   2   \n",
       "1042554              58030              82                   2   \n",
       "1042555                 53         1048635                   6   \n",
       "1042556                 53           94939                   4   \n",
       "\n",
       "          Total Backward Packets  Total Length of Fwd Packets  \\\n",
       "0                              0                           12   \n",
       "1                              1                            6   \n",
       "2                              1                            6   \n",
       "3                              1                            6   \n",
       "4                              0                           12   \n",
       "...                          ...                          ...   \n",
       "1042552                        2                          112   \n",
       "1042553                        2                           84   \n",
       "1042554                        1                           31   \n",
       "1042555                        2                          192   \n",
       "1042556                        2                          188   \n",
       "\n",
       "          Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
       "0                                   0                       6   \n",
       "1                                   6                       6   \n",
       "2                                   6                       6   \n",
       "3                                   6                       6   \n",
       "4                                   0                       6   \n",
       "...                               ...                     ...   \n",
       "1042552                           152                      28   \n",
       "1042553                           362                      42   \n",
       "1042554                             6                      31   \n",
       "1042555                           256                      32   \n",
       "1042556                           226                      47   \n",
       "\n",
       "          Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
       "0                             6                      6.0   \n",
       "1                             6                      6.0   \n",
       "2                             6                      6.0   \n",
       "3                             6                      6.0   \n",
       "4                             6                      6.0   \n",
       "...                         ...                      ...   \n",
       "1042552                      28                     28.0   \n",
       "1042553                      42                     42.0   \n",
       "1042554                       0                     15.5   \n",
       "1042555                      32                     32.0   \n",
       "1042556                      47                     47.0   \n",
       "\n",
       "          Fwd Packet Length Std  ...   min_seg_size_forward  Active Mean  \\\n",
       "0                       0.00000  ...                     20          0.0   \n",
       "1                       0.00000  ...                     20          0.0   \n",
       "2                       0.00000  ...                     20          0.0   \n",
       "3                       0.00000  ...                     20          0.0   \n",
       "4                       0.00000  ...                     20          0.0   \n",
       "...                         ...  ...                    ...          ...   \n",
       "1042552                 0.00000  ...                     20          0.0   \n",
       "1042553                 0.00000  ...                     20          0.0   \n",
       "1042554                21.92031  ...                     32          0.0   \n",
       "1042555                 0.00000  ...                     20          0.0   \n",
       "1042556                 0.00000  ...                     20          0.0   \n",
       "\n",
       "          Active Std   Active Max   Active Min  Idle Mean   Idle Std  \\\n",
       "0                0.0            0            0        0.0        0.0   \n",
       "1                0.0            0            0        0.0        0.0   \n",
       "2                0.0            0            0        0.0        0.0   \n",
       "3                0.0            0            0        0.0        0.0   \n",
       "4                0.0            0            0        0.0        0.0   \n",
       "...              ...          ...          ...        ...        ...   \n",
       "1042552          0.0            0            0        0.0        0.0   \n",
       "1042553          0.0            0            0        0.0        0.0   \n",
       "1042554          0.0            0            0        0.0        0.0   \n",
       "1042555          0.0            0            0        0.0        0.0   \n",
       "1042556          0.0            0            0        0.0        0.0   \n",
       "\n",
       "          Idle Max   Idle Min   Label  \n",
       "0                0          0  BENIGN  \n",
       "1                0          0  BENIGN  \n",
       "2                0          0  BENIGN  \n",
       "3                0          0  BENIGN  \n",
       "4                0          0  BENIGN  \n",
       "...            ...        ...     ...  \n",
       "1042552          0          0  BENIGN  \n",
       "1042553          0          0  BENIGN  \n",
       "1042554          0          0  BENIGN  \n",
       "1042555          0          0  BENIGN  \n",
       "1042556          0          0  BENIGN  \n",
       "\n",
       "[1042557 rows x 78 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7712436f",
   "metadata": {},
   "source": [
    "# Analyzing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a46c03e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " Label\n",
       "BENIGN      629074\n",
       "DDoS        128022\n",
       "DoS         194642\n",
       "PortScan     90819\n",
       "Name:  Label, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(' Label')[' Label'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40d00ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.replace([np.inf, -np.inf], np.nan).dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "714086ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "names=list(df.iloc[:,:77].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bb1d658",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd1166cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.37224562e-01 1.33333321e-07 4.90335488e-06 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.40108649e-01 1.01666657e-06 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.40123909e-01 5.41666617e-07 0.00000000e+00 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " ...\n",
      " [8.85521577e-01 7.91666594e-07 4.90335488e-06 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.08765183e-04 8.73873253e-03 2.45167744e-05 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]\n",
      " [8.08765183e-04 7.91266594e-04 1.47100646e-05 ... 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "scaled = scaler.fit_transform(df.iloc[:,:77])\n",
    "print(scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9e0d54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled=pd.DataFrame(scaled, columns=names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ed934fc",
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Backward Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Total Length of Bwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Fwd Packet Length Mean</th>\n",
       "      <th>Fwd Packet Length Std</th>\n",
       "      <th>...</th>\n",
       "      <th>act_data_pkt_fwd</th>\n",
       "      <th>min_seg_size_forward</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.837225</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.840109</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.840124</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.705548</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.837194</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.685666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>2.424242e-07</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.013559</td>\n",
       "      <td>0.006033</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041895</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.808333e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>5.773525e-07</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.009050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041896</th>\n",
       "      <td>0.885522</td>\n",
       "      <td>7.916666e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003340</td>\n",
       "      <td>0.003275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.540984</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041897</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>8.738733e-03</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>4.082935e-07</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.006895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041898</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>7.912666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>3.604466e-07</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.022760</td>\n",
       "      <td>0.010128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.344262</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041899 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                 0.837225    1.333333e-07            0.000005   \n",
       "1                 0.840109    1.016667e-06            0.000000   \n",
       "2                 0.840124    5.416666e-07            0.000000   \n",
       "3                 0.705548    3.916666e-07            0.000000   \n",
       "4                 0.837194    1.333333e-07            0.000005   \n",
       "...                    ...             ...                 ...   \n",
       "1041894           0.000809    2.685666e-04            0.000015   \n",
       "1041895           0.000809    2.808333e-06            0.000005   \n",
       "1041896           0.885522    7.916666e-07            0.000005   \n",
       "1041897           0.000809    8.738733e-03            0.000025   \n",
       "1041898           0.000809    7.912666e-04            0.000015   \n",
       "\n",
       "          Total Backward Packets  Total Length of Fwd Packets  \\\n",
       "0                       0.000000                     0.000010   \n",
       "1                       0.000004                     0.000005   \n",
       "2                       0.000004                     0.000005   \n",
       "3                       0.000004                     0.000005   \n",
       "4                       0.000000                     0.000010   \n",
       "...                          ...                          ...   \n",
       "1041894                 0.000007                     0.000091   \n",
       "1041895                 0.000007                     0.000069   \n",
       "1041896                 0.000004                     0.000025   \n",
       "1041897                 0.000007                     0.000157   \n",
       "1041898                 0.000007                     0.000154   \n",
       "\n",
       "          Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
       "0                        0.000000e+00                0.000242   \n",
       "1                        9.569378e-09                0.000242   \n",
       "2                        9.569378e-09                0.000242   \n",
       "3                        9.569378e-09                0.000242   \n",
       "4                        0.000000e+00                0.000242   \n",
       "...                               ...                     ...   \n",
       "1041894                  2.424242e-07                0.001128   \n",
       "1041895                  5.773525e-07                0.001692   \n",
       "1041896                  9.569378e-09                0.001249   \n",
       "1041897                  4.082935e-07                0.001289   \n",
       "1041898                  3.604466e-07                0.001894   \n",
       "\n",
       "          Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
       "0                      0.002906                 0.001293   \n",
       "1                      0.002906                 0.001293   \n",
       "2                      0.002906                 0.001293   \n",
       "3                      0.002906                 0.001293   \n",
       "4                      0.002906                 0.001293   \n",
       "...                         ...                      ...   \n",
       "1041894                0.013559                 0.006033   \n",
       "1041895                0.020339                 0.009050   \n",
       "1041896                0.000000                 0.003340   \n",
       "1041897                0.015496                 0.006895   \n",
       "1041898                0.022760                 0.010128   \n",
       "\n",
       "          Fwd Packet Length Std  ...   act_data_pkt_fwd  \\\n",
       "0                      0.000000  ...           0.000005   \n",
       "1                      0.000000  ...           0.000000   \n",
       "2                      0.000000  ...           0.000000   \n",
       "3                      0.000000  ...           0.000000   \n",
       "4                      0.000000  ...           0.000005   \n",
       "...                         ...  ...                ...   \n",
       "1041894                0.000000  ...           0.000015   \n",
       "1041895                0.000000  ...           0.000005   \n",
       "1041896                0.003275  ...           0.000000   \n",
       "1041897                0.000000  ...           0.000025   \n",
       "1041898                0.000000  ...           0.000015   \n",
       "\n",
       "          min_seg_size_forward  Active Mean   Active Std   Active Max  \\\n",
       "0                     0.344262          0.0          0.0          0.0   \n",
       "1                     0.344262          0.0          0.0          0.0   \n",
       "2                     0.344262          0.0          0.0          0.0   \n",
       "3                     0.344262          0.0          0.0          0.0   \n",
       "4                     0.344262          0.0          0.0          0.0   \n",
       "...                        ...          ...          ...          ...   \n",
       "1041894               0.344262          0.0          0.0          0.0   \n",
       "1041895               0.344262          0.0          0.0          0.0   \n",
       "1041896               0.540984          0.0          0.0          0.0   \n",
       "1041897               0.344262          0.0          0.0          0.0   \n",
       "1041898               0.344262          0.0          0.0          0.0   \n",
       "\n",
       "          Active Min  Idle Mean   Idle Std   Idle Max   Idle Min  \n",
       "0                0.0        0.0        0.0        0.0        0.0  \n",
       "1                0.0        0.0        0.0        0.0        0.0  \n",
       "2                0.0        0.0        0.0        0.0        0.0  \n",
       "3                0.0        0.0        0.0        0.0        0.0  \n",
       "4                0.0        0.0        0.0        0.0        0.0  \n",
       "...              ...        ...        ...        ...        ...  \n",
       "1041894          0.0        0.0        0.0        0.0        0.0  \n",
       "1041895          0.0        0.0        0.0        0.0        0.0  \n",
       "1041896          0.0        0.0        0.0        0.0        0.0  \n",
       "1041897          0.0        0.0        0.0        0.0        0.0  \n",
       "1041898          0.0        0.0        0.0        0.0        0.0  \n",
       "\n",
       "[1041899 rows x 77 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2838bf5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled['Label']=df[' Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cc86555c",
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Backward Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Total Length of Bwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Fwd Packet Length Mean</th>\n",
       "      <th>Fwd Packet Length Std</th>\n",
       "      <th>...</th>\n",
       "      <th>min_seg_size_forward</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>0.837225</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>1</th>\n",
       "      <td>0.840109</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>2</th>\n",
       "      <td>0.840124</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>0.705548</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>4</th>\n",
       "      <td>0.837194</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.685666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>2.424242e-07</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.013559</td>\n",
       "      <td>0.006033</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>1041895</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.808333e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>5.773525e-07</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.009050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>1041896</th>\n",
       "      <td>0.885522</td>\n",
       "      <td>7.916666e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>9.569378e-09</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003340</td>\n",
       "      <td>0.003275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.540984</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>1041897</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>8.738733e-03</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>4.082935e-07</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.006895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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>1041898</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>7.912666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>3.604466e-07</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.022760</td>\n",
       "      <td>0.010128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.344262</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041899 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                 0.837225    1.333333e-07            0.000005   \n",
       "1                 0.840109    1.016667e-06            0.000000   \n",
       "2                 0.840124    5.416666e-07            0.000000   \n",
       "3                 0.705548    3.916666e-07            0.000000   \n",
       "4                 0.837194    1.333333e-07            0.000005   \n",
       "...                    ...             ...                 ...   \n",
       "1041894           0.000809    2.685666e-04            0.000015   \n",
       "1041895           0.000809    2.808333e-06            0.000005   \n",
       "1041896           0.885522    7.916666e-07            0.000005   \n",
       "1041897           0.000809    8.738733e-03            0.000025   \n",
       "1041898           0.000809    7.912666e-04            0.000015   \n",
       "\n",
       "          Total Backward Packets  Total Length of Fwd Packets  \\\n",
       "0                       0.000000                     0.000010   \n",
       "1                       0.000004                     0.000005   \n",
       "2                       0.000004                     0.000005   \n",
       "3                       0.000004                     0.000005   \n",
       "4                       0.000000                     0.000010   \n",
       "...                          ...                          ...   \n",
       "1041894                 0.000007                     0.000091   \n",
       "1041895                 0.000007                     0.000069   \n",
       "1041896                 0.000004                     0.000025   \n",
       "1041897                 0.000007                     0.000157   \n",
       "1041898                 0.000007                     0.000154   \n",
       "\n",
       "          Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
       "0                        0.000000e+00                0.000242   \n",
       "1                        9.569378e-09                0.000242   \n",
       "2                        9.569378e-09                0.000242   \n",
       "3                        9.569378e-09                0.000242   \n",
       "4                        0.000000e+00                0.000242   \n",
       "...                               ...                     ...   \n",
       "1041894                  2.424242e-07                0.001128   \n",
       "1041895                  5.773525e-07                0.001692   \n",
       "1041896                  9.569378e-09                0.001249   \n",
       "1041897                  4.082935e-07                0.001289   \n",
       "1041898                  3.604466e-07                0.001894   \n",
       "\n",
       "          Fwd Packet Length Min   Fwd Packet Length Mean  \\\n",
       "0                      0.002906                 0.001293   \n",
       "1                      0.002906                 0.001293   \n",
       "2                      0.002906                 0.001293   \n",
       "3                      0.002906                 0.001293   \n",
       "4                      0.002906                 0.001293   \n",
       "...                         ...                      ...   \n",
       "1041894                0.013559                 0.006033   \n",
       "1041895                0.020339                 0.009050   \n",
       "1041896                0.000000                 0.003340   \n",
       "1041897                0.015496                 0.006895   \n",
       "1041898                0.022760                 0.010128   \n",
       "\n",
       "          Fwd Packet Length Std  ...   min_seg_size_forward  Active Mean  \\\n",
       "0                      0.000000  ...               0.344262          0.0   \n",
       "1                      0.000000  ...               0.344262          0.0   \n",
       "2                      0.000000  ...               0.344262          0.0   \n",
       "3                      0.000000  ...               0.344262          0.0   \n",
       "4                      0.000000  ...               0.344262          0.0   \n",
       "...                         ...  ...                    ...          ...   \n",
       "1041894                0.000000  ...               0.344262          0.0   \n",
       "1041895                0.000000  ...               0.344262          0.0   \n",
       "1041896                0.003275  ...               0.540984          0.0   \n",
       "1041897                0.000000  ...               0.344262          0.0   \n",
       "1041898                0.000000  ...               0.344262          0.0   \n",
       "\n",
       "          Active Std   Active Max   Active Min  Idle Mean   Idle Std  \\\n",
       "0                0.0          0.0          0.0        0.0        0.0   \n",
       "1                0.0          0.0          0.0        0.0        0.0   \n",
       "2                0.0          0.0          0.0        0.0        0.0   \n",
       "3                0.0          0.0          0.0        0.0        0.0   \n",
       "4                0.0          0.0          0.0        0.0        0.0   \n",
       "...              ...          ...          ...        ...        ...   \n",
       "1041894          0.0          0.0          0.0        0.0        0.0   \n",
       "1041895          0.0          0.0          0.0        0.0        0.0   \n",
       "1041896          0.0          0.0          0.0        0.0        0.0   \n",
       "1041897          0.0          0.0          0.0        0.0        0.0   \n",
       "1041898          0.0          0.0          0.0        0.0        0.0   \n",
       "\n",
       "          Idle Max   Idle Min   Label  \n",
       "0              0.0        0.0  BENIGN  \n",
       "1              0.0        0.0  BENIGN  \n",
       "2              0.0        0.0  BENIGN  \n",
       "3              0.0        0.0  BENIGN  \n",
       "4              0.0        0.0  BENIGN  \n",
       "...            ...        ...     ...  \n",
       "1041894        0.0        0.0  BENIGN  \n",
       "1041895        0.0        0.0  BENIGN  \n",
       "1041896        0.0        0.0  BENIGN  \n",
       "1041897        0.0        0.0  BENIGN  \n",
       "1041898        0.0        0.0  BENIGN  \n",
       "\n",
       "[1041899 rows x 78 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8388eb5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prash\\AppData\\Local\\Temp\\ipykernel_3812\\1557876135.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(scaled.corr())\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(scaled.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "403505fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prash\\AppData\\Local\\Temp\\ipykernel_3812\\1450045545.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  corr_matrix = scaled.corr().abs()\n"
     ]
    },
    {
     "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Backward Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Total Length of Bwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Fwd Packet Length Mean</th>\n",
       "      <th>Fwd Packet Length Std</th>\n",
       "      <th>...</th>\n",
       "      <th>act_data_pkt_fwd</th>\n",
       "      <th>min_seg_size_forward</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Destination Port</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157036</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.089045</td>\n",
       "      <td>0.002989</td>\n",
       "      <td>0.290101</td>\n",
       "      <td>0.065984</td>\n",
       "      <td>0.340747</td>\n",
       "      <td>0.301904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.041774</td>\n",
       "      <td>0.017115</td>\n",
       "      <td>0.038161</td>\n",
       "      <td>0.035903</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>0.134710</td>\n",
       "      <td>0.041906</td>\n",
       "      <td>0.125343</td>\n",
       "      <td>0.141433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flow Duration</th>\n",
       "      <td>0.157036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015559</td>\n",
       "      <td>0.014238</td>\n",
       "      <td>0.108580</td>\n",
       "      <td>0.011530</td>\n",
       "      <td>0.223748</td>\n",
       "      <td>0.103777</td>\n",
       "      <td>0.134971</td>\n",
       "      <td>0.201298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012044</td>\n",
       "      <td>0.055663</td>\n",
       "      <td>0.177239</td>\n",
       "      <td>0.173826</td>\n",
       "      <td>0.236756</td>\n",
       "      <td>0.129436</td>\n",
       "      <td>0.857087</td>\n",
       "      <td>0.264598</td>\n",
       "      <td>0.873493</td>\n",
       "      <td>0.820578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.015559</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998900</td>\n",
       "      <td>0.684838</td>\n",
       "      <td>0.995852</td>\n",
       "      <td>0.004505</td>\n",
       "      <td>0.002161</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999699</td>\n",
       "      <td>0.004415</td>\n",
       "      <td>0.007213</td>\n",
       "      <td>0.007790</td>\n",
       "      <td>0.009949</td>\n",
       "      <td>0.004724</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>0.000693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Backward Packets</th>\n",
       "      <td>0.003363</td>\n",
       "      <td>0.014238</td>\n",
       "      <td>0.998900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.684172</td>\n",
       "      <td>0.992627</td>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.001578</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>...</td>\n",
       "      <td>0.998357</td>\n",
       "      <td>0.004121</td>\n",
       "      <td>0.006045</td>\n",
       "      <td>0.005845</td>\n",
       "      <td>0.007975</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <td>0.089045</td>\n",
       "      <td>0.108580</td>\n",
       "      <td>0.684838</td>\n",
       "      <td>0.684172</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.678660</td>\n",
       "      <td>0.372810</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.357691</td>\n",
       "      <td>0.335732</td>\n",
       "      <td>...</td>\n",
       "      <td>0.684236</td>\n",
       "      <td>0.026450</td>\n",
       "      <td>0.154549</td>\n",
       "      <td>0.183151</td>\n",
       "      <td>0.210402</td>\n",
       "      <td>0.088143</td>\n",
       "      <td>0.040546</td>\n",
       "      <td>0.064085</td>\n",
       "      <td>0.049269</td>\n",
       "      <td>0.030639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Active Min</th>\n",
       "      <td>0.004368</td>\n",
       "      <td>0.129436</td>\n",
       "      <td>0.004724</td>\n",
       "      <td>0.004511</td>\n",
       "      <td>0.088143</td>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.101948</td>\n",
       "      <td>0.024682</td>\n",
       "      <td>0.091827</td>\n",
       "      <td>0.097924</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003541</td>\n",
       "      <td>0.018670</td>\n",
       "      <td>0.908695</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.626160</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.104232</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.110979</td>\n",
       "      <td>0.097314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idle Mean</th>\n",
       "      <td>0.134710</td>\n",
       "      <td>0.857087</td>\n",
       "      <td>0.000811</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.040546</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.163720</td>\n",
       "      <td>0.087273</td>\n",
       "      <td>0.111882</td>\n",
       "      <td>0.164190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000974</td>\n",
       "      <td>0.048351</td>\n",
       "      <td>0.094675</td>\n",
       "      <td>0.001905</td>\n",
       "      <td>0.056973</td>\n",
       "      <td>0.104232</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.118729</td>\n",
       "      <td>0.988732</td>\n",
       "      <td>0.988671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idle Std</th>\n",
       "      <td>0.041906</td>\n",
       "      <td>0.264598</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.064085</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.194241</td>\n",
       "      <td>0.031633</td>\n",
       "      <td>0.194579</td>\n",
       "      <td>0.200980</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.080121</td>\n",
       "      <td>0.064199</td>\n",
       "      <td>0.068491</td>\n",
       "      <td>0.061901</td>\n",
       "      <td>0.038246</td>\n",
       "      <td>0.118729</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263930</td>\n",
       "      <td>0.031350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idle Max</th>\n",
       "      <td>0.125343</td>\n",
       "      <td>0.873493</td>\n",
       "      <td>0.000890</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.049269</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.187112</td>\n",
       "      <td>0.089659</td>\n",
       "      <td>0.137237</td>\n",
       "      <td>0.188609</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000881</td>\n",
       "      <td>0.057012</td>\n",
       "      <td>0.110680</td>\n",
       "      <td>0.021300</td>\n",
       "      <td>0.074742</td>\n",
       "      <td>0.110979</td>\n",
       "      <td>0.988732</td>\n",
       "      <td>0.263930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.955403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idle Min</th>\n",
       "      <td>0.141433</td>\n",
       "      <td>0.820578</td>\n",
       "      <td>0.000693</td>\n",
       "      <td>0.000082</td>\n",
       "      <td>0.030639</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.135077</td>\n",
       "      <td>0.082845</td>\n",
       "      <td>0.083281</td>\n",
       "      <td>0.134774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001057</td>\n",
       "      <td>0.036974</td>\n",
       "      <td>0.083556</td>\n",
       "      <td>0.013447</td>\n",
       "      <td>0.045590</td>\n",
       "      <td>0.097314</td>\n",
       "      <td>0.988671</td>\n",
       "      <td>0.031350</td>\n",
       "      <td>0.955403</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Destination Port   Flow Duration  \\\n",
       " Destination Port                     1.000000        0.157036   \n",
       " Flow Duration                        0.157036        1.000000   \n",
       " Total Fwd Packets                    0.003908        0.015559   \n",
       " Total Backward Packets               0.003363        0.014238   \n",
       "Total Length of Fwd Packets           0.089045        0.108580   \n",
       "...                                        ...             ...   \n",
       " Active Min                           0.004368        0.129436   \n",
       "Idle Mean                             0.134710        0.857087   \n",
       " Idle Std                             0.041906        0.264598   \n",
       " Idle Max                             0.125343        0.873493   \n",
       " Idle Min                             0.141433        0.820578   \n",
       "\n",
       "                              Total Fwd Packets   Total Backward Packets  \\\n",
       " Destination Port                      0.003908                 0.003363   \n",
       " Flow Duration                         0.015559                 0.014238   \n",
       " Total Fwd Packets                     1.000000                 0.998900   \n",
       " Total Backward Packets                0.998900                 1.000000   \n",
       "Total Length of Fwd Packets            0.684838                 0.684172   \n",
       "...                                         ...                      ...   \n",
       " Active Min                            0.004724                 0.004511   \n",
       "Idle Mean                              0.000811                 0.000120   \n",
       " Idle Std                              0.000519                 0.000028   \n",
       " Idle Max                              0.000890                 0.000116   \n",
       " Idle Min                              0.000693                 0.000082   \n",
       "\n",
       "                             Total Length of Fwd Packets  \\\n",
       " Destination Port                               0.089045   \n",
       " Flow Duration                                  0.108580   \n",
       " Total Fwd Packets                              0.684838   \n",
       " Total Backward Packets                         0.684172   \n",
       "Total Length of Fwd Packets                     1.000000   \n",
       "...                                                  ...   \n",
       " Active Min                                     0.088143   \n",
       "Idle Mean                                       0.040546   \n",
       " Idle Std                                       0.064085   \n",
       " Idle Max                                       0.049269   \n",
       " Idle Min                                       0.030639   \n",
       "\n",
       "                              Total Length of Bwd Packets  \\\n",
       " Destination Port                                0.002989   \n",
       " Flow Duration                                   0.011530   \n",
       " Total Fwd Packets                               0.995852   \n",
       " Total Backward Packets                          0.992627   \n",
       "Total Length of Fwd Packets                      0.678660   \n",
       "...                                                   ...   \n",
       " Active Min                                      0.003127   \n",
       "Idle Mean                                        0.000074   \n",
       " Idle Std                                        0.000162   \n",
       " Idle Max                                        0.000040   \n",
       " Idle Min                                        0.000086   \n",
       "\n",
       "                              Fwd Packet Length Max   Fwd Packet Length Min  \\\n",
       " Destination Port                          0.290101                0.065984   \n",
       " Flow Duration                             0.223748                0.103777   \n",
       " Total Fwd Packets                         0.004505                0.002161   \n",
       " Total Backward Packets                    0.004417                0.001578   \n",
       "Total Length of Fwd Packets                0.372810                0.001701   \n",
       "...                                             ...                     ...   \n",
       " Active Min                                0.101948                0.024682   \n",
       "Idle Mean                                  0.163720                0.087273   \n",
       " Idle Std                                  0.194241                0.031633   \n",
       " Idle Max                                  0.187112                0.089659   \n",
       " Idle Min                                  0.135077                0.082845   \n",
       "\n",
       "                              Fwd Packet Length Mean   Fwd Packet Length Std  \\\n",
       " Destination Port                           0.340747                0.301904   \n",
       " Flow Duration                              0.134971                0.201298   \n",
       " Total Fwd Packets                          0.000137                0.000763   \n",
       " Total Backward Packets                     0.000181                0.000602   \n",
       "Total Length of Fwd Packets                 0.357691                0.335732   \n",
       "...                                              ...                     ...   \n",
       " Active Min                                 0.091827                0.097924   \n",
       "Idle Mean                                   0.111882                0.164190   \n",
       " Idle Std                                   0.194579                0.200980   \n",
       " Idle Max                                   0.137237                0.188609   \n",
       " Idle Min                                   0.083281                0.134774   \n",
       "\n",
       "                             ...   act_data_pkt_fwd   min_seg_size_forward  \\\n",
       " Destination Port            ...           0.002999               0.041774   \n",
       " Flow Duration               ...           0.012044               0.055663   \n",
       " Total Fwd Packets           ...           0.999699               0.004415   \n",
       " Total Backward Packets      ...           0.998357               0.004121   \n",
       "Total Length of Fwd Packets  ...           0.684236               0.026450   \n",
       "...                          ...                ...                    ...   \n",
       " Active Min                  ...           0.003541               0.018670   \n",
       "Idle Mean                    ...           0.000974               0.048351   \n",
       " Idle Std                    ...           0.000355               0.080121   \n",
       " Idle Max                    ...           0.000881               0.057012   \n",
       " Idle Min                    ...           0.001057               0.036974   \n",
       "\n",
       "                             Active Mean   Active Std   Active Max  \\\n",
       " Destination Port               0.017115     0.038161     0.035903   \n",
       " Flow Duration                  0.177239     0.173826     0.236756   \n",
       " Total Fwd Packets              0.007213     0.007790     0.009949   \n",
       " Total Backward Packets         0.006045     0.005845     0.007975   \n",
       "Total Length of Fwd Packets     0.154549     0.183151     0.210402   \n",
       "...                                  ...          ...          ...   \n",
       " Active Min                     0.908695     0.012346     0.626160   \n",
       "Idle Mean                       0.094675     0.001905     0.056973   \n",
       " Idle Std                       0.064199     0.068491     0.061901   \n",
       " Idle Max                       0.110680     0.021300     0.074742   \n",
       " Idle Min                       0.083556     0.013447     0.045590   \n",
       "\n",
       "                              Active Min  Idle Mean   Idle Std   Idle Max  \\\n",
       " Destination Port               0.004368   0.134710   0.041906   0.125343   \n",
       " Flow Duration                  0.129436   0.857087   0.264598   0.873493   \n",
       " Total Fwd Packets              0.004724   0.000811   0.000519   0.000890   \n",
       " Total Backward Packets         0.004511   0.000120   0.000028   0.000116   \n",
       "Total Length of Fwd Packets     0.088143   0.040546   0.064085   0.049269   \n",
       "...                                  ...        ...        ...        ...   \n",
       " Active Min                     1.000000   0.104232   0.038246   0.110979   \n",
       "Idle Mean                       0.104232   1.000000   0.118729   0.988732   \n",
       " Idle Std                       0.038246   0.118729   1.000000   0.263930   \n",
       " Idle Max                       0.110979   0.988732   0.263930   1.000000   \n",
       " Idle Min                       0.097314   0.988671   0.031350   0.955403   \n",
       "\n",
       "                              Idle Min  \n",
       " Destination Port             0.141433  \n",
       " Flow Duration                0.820578  \n",
       " Total Fwd Packets            0.000693  \n",
       " Total Backward Packets       0.000082  \n",
       "Total Length of Fwd Packets   0.030639  \n",
       "...                                ...  \n",
       " Active Min                   0.097314  \n",
       "Idle Mean                     0.988671  \n",
       " Idle Std                     0.031350  \n",
       " Idle Max                     0.955403  \n",
       " Idle Min                     1.000000  \n",
       "\n",
       "[77 rows x 77 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = scaled.corr().abs()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e861e17c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prash\\AppData\\Local\\Temp\\ipykernel_3812\\540890198.py:2: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[' Total Backward Packets',\n",
       " ' Total Length of Bwd Packets',\n",
       " ' Fwd Packet Length Mean',\n",
       " ' Fwd Packet Length Std',\n",
       " ' Bwd Packet Length Mean',\n",
       " ' Bwd Packet Length Std',\n",
       " ' Flow IAT Std',\n",
       " ' Flow IAT Max',\n",
       " 'Fwd IAT Total',\n",
       " ' Fwd IAT Mean',\n",
       " ' Fwd IAT Std',\n",
       " ' Fwd IAT Max',\n",
       " ' Fwd IAT Min',\n",
       " ' Bwd IAT Mean',\n",
       " ' Bwd IAT Std',\n",
       " ' Bwd IAT Max',\n",
       " ' Bwd IAT Min',\n",
       " ' Fwd Header Length',\n",
       " ' Bwd Header Length',\n",
       " 'Fwd Packets/s',\n",
       " ' Max Packet Length',\n",
       " ' Packet Length Mean',\n",
       " ' Packet Length Std',\n",
       " ' Packet Length Variance',\n",
       " ' SYN Flag Count',\n",
       " ' ECE Flag Count',\n",
       " ' Average Packet Size',\n",
       " ' Avg Fwd Segment Size',\n",
       " ' Avg Bwd Segment Size',\n",
       " 'Subflow Fwd Packets',\n",
       " ' Subflow Fwd Bytes',\n",
       " ' Subflow Bwd Packets',\n",
       " ' Subflow Bwd Bytes',\n",
       " ' act_data_pkt_fwd',\n",
       " ' Active Max',\n",
       " ' Active Min',\n",
       " 'Idle Mean',\n",
       " ' Idle Max',\n",
       " ' Idle Min']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > 0.7)]\n",
    "to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6f6c0dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c2b75e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[' Total Backward Packets', ' Total Length of Bwd Packets', ' Fwd Packet Length Mean', ' Fwd Packet Length Std', ' Bwd Packet Length Mean', ' Bwd Packet Length Std', ' Flow IAT Std', ' Flow IAT Max', 'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max', ' Fwd IAT Min', ' Bwd IAT Mean', ' Bwd IAT Std', ' Bwd IAT Max', ' Bwd IAT Min', ' Fwd Header Length', ' Bwd Header Length', 'Fwd Packets/s', ' Max Packet Length', ' Packet Length Mean', ' Packet Length Std', ' Packet Length Variance', ' SYN Flag Count', ' ECE Flag Count', ' Average Packet Size', ' Avg Fwd Segment Size', ' Avg Bwd Segment Size', 'Subflow Fwd Packets', ' Subflow Fwd Bytes', ' Subflow Bwd Packets', ' Subflow Bwd Bytes', ' act_data_pkt_fwd', ' Active Max', ' Active Min', 'Idle Mean', ' Idle Max', ' Idle Min']\n"
     ]
    }
   ],
   "source": [
    "print(to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9e5683cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled.drop(to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ffbd996d",
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Bwd Packet Length Max</th>\n",
       "      <th>Bwd Packet Length Min</th>\n",
       "      <th>Flow Bytes/s</th>\n",
       "      <th>Flow Packets/s</th>\n",
       "      <th>...</th>\n",
       "      <th>Bwd Avg Bytes/Bulk</th>\n",
       "      <th>Bwd Avg Packets/Bulk</th>\n",
       "      <th>Bwd Avg Bulk Rate</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Active Mean</th>\n",
       "      <th>Active Std</th>\n",
       "      <th>Idle Std</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.837225</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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>1</th>\n",
       "      <td>0.840109</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085334</td>\n",
       "      <td>0.403670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.344262</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>2</th>\n",
       "      <td>0.840124</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085387</td>\n",
       "      <td>0.407692</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.344262</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>0.705548</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085441</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.005035</td>\n",
       "      <td>0.344262</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>4</th>\n",
       "      <td>0.837194</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.685666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.013559</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.038326</td>\n",
       "      <td>0.085289</td>\n",
       "      <td>0.400037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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>1041895</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.808333e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.009268</td>\n",
       "      <td>0.091276</td>\n",
       "      <td>0.085893</td>\n",
       "      <td>0.402469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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>1041896</th>\n",
       "      <td>0.885522</td>\n",
       "      <td>7.916666e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085484</td>\n",
       "      <td>0.407317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015366</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.540984</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>1041897</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>8.738733e-03</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.006554</td>\n",
       "      <td>0.064549</td>\n",
       "      <td>0.085285</td>\n",
       "      <td>0.400002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</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>1041898</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>7.912666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.022760</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.056984</td>\n",
       "      <td>0.085287</td>\n",
       "      <td>0.400013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</td>\n",
       "      <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>1041899 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                 0.837225    1.333333e-07            0.000005   \n",
       "1                 0.840109    1.016667e-06            0.000000   \n",
       "2                 0.840124    5.416666e-07            0.000000   \n",
       "3                 0.705548    3.916666e-07            0.000000   \n",
       "4                 0.837194    1.333333e-07            0.000005   \n",
       "...                    ...             ...                 ...   \n",
       "1041894           0.000809    2.685666e-04            0.000015   \n",
       "1041895           0.000809    2.808333e-06            0.000005   \n",
       "1041896           0.885522    7.916666e-07            0.000005   \n",
       "1041897           0.000809    8.738733e-03            0.000025   \n",
       "1041898           0.000809    7.912666e-04            0.000015   \n",
       "\n",
       "         Total Length of Fwd Packets   Fwd Packet Length Max  \\\n",
       "0                           0.000010                0.000242   \n",
       "1                           0.000005                0.000242   \n",
       "2                           0.000005                0.000242   \n",
       "3                           0.000005                0.000242   \n",
       "4                           0.000010                0.000242   \n",
       "...                              ...                     ...   \n",
       "1041894                     0.000091                0.001128   \n",
       "1041895                     0.000069                0.001692   \n",
       "1041896                     0.000025                0.001249   \n",
       "1041897                     0.000157                0.001289   \n",
       "1041898                     0.000154                0.001894   \n",
       "\n",
       "          Fwd Packet Length Min  Bwd Packet Length Max  \\\n",
       "0                      0.002906               0.000000   \n",
       "1                      0.002906               0.000307   \n",
       "2                      0.002906               0.000307   \n",
       "3                      0.002906               0.000307   \n",
       "4                      0.002906               0.000000   \n",
       "...                         ...                    ...   \n",
       "1041894                0.013559               0.003891   \n",
       "1041895                0.020339               0.009268   \n",
       "1041896                0.000000               0.000307   \n",
       "1041897                0.015496               0.006554   \n",
       "1041898                0.022760               0.005786   \n",
       "\n",
       "          Bwd Packet Length Min  Flow Bytes/s   Flow Packets/s  ...  \\\n",
       "0                      0.000000      0.087053         0.533333  ...   \n",
       "1                      0.003026      0.085334         0.403670  ...   \n",
       "2                      0.003026      0.085387         0.407692  ...   \n",
       "3                      0.003026      0.085441         0.411765  ...   \n",
       "4                      0.000000      0.087053         0.533333  ...   \n",
       "...                         ...           ...              ...  ...   \n",
       "1041894                0.038326      0.085289         0.400037  ...   \n",
       "1041895                0.091276      0.085893         0.402469  ...   \n",
       "1041896                0.003026      0.085484         0.407317  ...   \n",
       "1041897                0.064549      0.085285         0.400002  ...   \n",
       "1041898                0.056984      0.085287         0.400013  ...   \n",
       "\n",
       "          Bwd Avg Bytes/Bulk   Bwd Avg Packets/Bulk  Bwd Avg Bulk Rate  \\\n",
       "0                        0.0                    0.0                0.0   \n",
       "1                        0.0                    0.0                0.0   \n",
       "2                        0.0                    0.0                0.0   \n",
       "3                        0.0                    0.0                0.0   \n",
       "4                        0.0                    0.0                0.0   \n",
       "...                      ...                    ...                ...   \n",
       "1041894                  0.0                    0.0                0.0   \n",
       "1041895                  0.0                    0.0                0.0   \n",
       "1041896                  0.0                    0.0                0.0   \n",
       "1041897                  0.0                    0.0                0.0   \n",
       "1041898                  0.0                    0.0                0.0   \n",
       "\n",
       "         Init_Win_bytes_forward   Init_Win_bytes_backward  \\\n",
       "0                      0.000519                  0.000000   \n",
       "1                      0.000458                  0.003922   \n",
       "2                      0.000458                  0.003922   \n",
       "3                      0.000488                  0.005035   \n",
       "4                      0.000504                  0.000000   \n",
       "...                         ...                       ...   \n",
       "1041894                0.000000                  0.000000   \n",
       "1041895                0.000000                  0.000000   \n",
       "1041896                0.015366                  0.000015   \n",
       "1041897                0.000000                  0.000000   \n",
       "1041898                0.000000                  0.000000   \n",
       "\n",
       "          min_seg_size_forward  Active Mean   Active Std   Idle Std   Label  \n",
       "0                     0.344262          0.0          0.0        0.0  BENIGN  \n",
       "1                     0.344262          0.0          0.0        0.0  BENIGN  \n",
       "2                     0.344262          0.0          0.0        0.0  BENIGN  \n",
       "3                     0.344262          0.0          0.0        0.0  BENIGN  \n",
       "4                     0.344262          0.0          0.0        0.0  BENIGN  \n",
       "...                        ...          ...          ...        ...     ...  \n",
       "1041894               0.344262          0.0          0.0        0.0  BENIGN  \n",
       "1041895               0.344262          0.0          0.0        0.0  BENIGN  \n",
       "1041896               0.540984          0.0          0.0        0.0  BENIGN  \n",
       "1041897               0.344262          0.0          0.0        0.0  BENIGN  \n",
       "1041898               0.344262          0.0          0.0        0.0  BENIGN  \n",
       "\n",
       "[1041899 rows x 39 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b062c480",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_columns_name = scaled.columns.drop('Label')\n",
    "x = scaled[x_columns_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4bb9f95d",
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Bwd Packet Length Max</th>\n",
       "      <th>Bwd Packet Length Min</th>\n",
       "      <th>Flow Bytes/s</th>\n",
       "      <th>Flow Packets/s</th>\n",
       "      <th>...</th>\n",
       "      <th>Fwd Avg Bulk Rate</th>\n",
       "      <th>Bwd Avg Bytes/Bulk</th>\n",
       "      <th>Bwd Avg Packets/Bulk</th>\n",
       "      <th>Bwd Avg Bulk Rate</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Init_Win_bytes_backward</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Active Mean</th>\n",
       "      <th>Active Std</th>\n",
       "      <th>Idle Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.837225</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.840109</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085334</td>\n",
       "      <td>0.403670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.840124</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085387</td>\n",
       "      <td>0.407692</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.705548</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085441</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.005035</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.837194</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.685666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.013559</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.038326</td>\n",
       "      <td>0.085289</td>\n",
       "      <td>0.400037</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041895</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.808333e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.020339</td>\n",
       "      <td>0.009268</td>\n",
       "      <td>0.091276</td>\n",
       "      <td>0.085893</td>\n",
       "      <td>0.402469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041896</th>\n",
       "      <td>0.885522</td>\n",
       "      <td>7.916666e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.085484</td>\n",
       "      <td>0.407317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015366</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041897</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>8.738733e-03</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>0.006554</td>\n",
       "      <td>0.064549</td>\n",
       "      <td>0.085285</td>\n",
       "      <td>0.400002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041898</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>7.912666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.022760</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.056984</td>\n",
       "      <td>0.085287</td>\n",
       "      <td>0.400013</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041899 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                 0.837225    1.333333e-07            0.000005   \n",
       "1                 0.840109    1.016667e-06            0.000000   \n",
       "2                 0.840124    5.416666e-07            0.000000   \n",
       "3                 0.705548    3.916666e-07            0.000000   \n",
       "4                 0.837194    1.333333e-07            0.000005   \n",
       "...                    ...             ...                 ...   \n",
       "1041894           0.000809    2.685666e-04            0.000015   \n",
       "1041895           0.000809    2.808333e-06            0.000005   \n",
       "1041896           0.885522    7.916666e-07            0.000005   \n",
       "1041897           0.000809    8.738733e-03            0.000025   \n",
       "1041898           0.000809    7.912666e-04            0.000015   \n",
       "\n",
       "         Total Length of Fwd Packets   Fwd Packet Length Max  \\\n",
       "0                           0.000010                0.000242   \n",
       "1                           0.000005                0.000242   \n",
       "2                           0.000005                0.000242   \n",
       "3                           0.000005                0.000242   \n",
       "4                           0.000010                0.000242   \n",
       "...                              ...                     ...   \n",
       "1041894                     0.000091                0.001128   \n",
       "1041895                     0.000069                0.001692   \n",
       "1041896                     0.000025                0.001249   \n",
       "1041897                     0.000157                0.001289   \n",
       "1041898                     0.000154                0.001894   \n",
       "\n",
       "          Fwd Packet Length Min  Bwd Packet Length Max  \\\n",
       "0                      0.002906               0.000000   \n",
       "1                      0.002906               0.000307   \n",
       "2                      0.002906               0.000307   \n",
       "3                      0.002906               0.000307   \n",
       "4                      0.002906               0.000000   \n",
       "...                         ...                    ...   \n",
       "1041894                0.013559               0.003891   \n",
       "1041895                0.020339               0.009268   \n",
       "1041896                0.000000               0.000307   \n",
       "1041897                0.015496               0.006554   \n",
       "1041898                0.022760               0.005786   \n",
       "\n",
       "          Bwd Packet Length Min  Flow Bytes/s   Flow Packets/s  ...  \\\n",
       "0                      0.000000      0.087053         0.533333  ...   \n",
       "1                      0.003026      0.085334         0.403670  ...   \n",
       "2                      0.003026      0.085387         0.407692  ...   \n",
       "3                      0.003026      0.085441         0.411765  ...   \n",
       "4                      0.000000      0.087053         0.533333  ...   \n",
       "...                         ...           ...              ...  ...   \n",
       "1041894                0.038326      0.085289         0.400037  ...   \n",
       "1041895                0.091276      0.085893         0.402469  ...   \n",
       "1041896                0.003026      0.085484         0.407317  ...   \n",
       "1041897                0.064549      0.085285         0.400002  ...   \n",
       "1041898                0.056984      0.085287         0.400013  ...   \n",
       "\n",
       "          Fwd Avg Bulk Rate   Bwd Avg Bytes/Bulk   Bwd Avg Packets/Bulk  \\\n",
       "0                       0.0                  0.0                    0.0   \n",
       "1                       0.0                  0.0                    0.0   \n",
       "2                       0.0                  0.0                    0.0   \n",
       "3                       0.0                  0.0                    0.0   \n",
       "4                       0.0                  0.0                    0.0   \n",
       "...                     ...                  ...                    ...   \n",
       "1041894                 0.0                  0.0                    0.0   \n",
       "1041895                 0.0                  0.0                    0.0   \n",
       "1041896                 0.0                  0.0                    0.0   \n",
       "1041897                 0.0                  0.0                    0.0   \n",
       "1041898                 0.0                  0.0                    0.0   \n",
       "\n",
       "         Bwd Avg Bulk Rate  Init_Win_bytes_forward   Init_Win_bytes_backward  \\\n",
       "0                      0.0                0.000519                  0.000000   \n",
       "1                      0.0                0.000458                  0.003922   \n",
       "2                      0.0                0.000458                  0.003922   \n",
       "3                      0.0                0.000488                  0.005035   \n",
       "4                      0.0                0.000504                  0.000000   \n",
       "...                    ...                     ...                       ...   \n",
       "1041894                0.0                0.000000                  0.000000   \n",
       "1041895                0.0                0.000000                  0.000000   \n",
       "1041896                0.0                0.015366                  0.000015   \n",
       "1041897                0.0                0.000000                  0.000000   \n",
       "1041898                0.0                0.000000                  0.000000   \n",
       "\n",
       "          min_seg_size_forward  Active Mean   Active Std   Idle Std  \n",
       "0                     0.344262          0.0          0.0        0.0  \n",
       "1                     0.344262          0.0          0.0        0.0  \n",
       "2                     0.344262          0.0          0.0        0.0  \n",
       "3                     0.344262          0.0          0.0        0.0  \n",
       "4                     0.344262          0.0          0.0        0.0  \n",
       "...                        ...          ...          ...        ...  \n",
       "1041894               0.344262          0.0          0.0        0.0  \n",
       "1041895               0.344262          0.0          0.0        0.0  \n",
       "1041896               0.540984          0.0          0.0        0.0  \n",
       "1041897               0.344262          0.0          0.0        0.0  \n",
       "1041898               0.344262          0.0          0.0        0.0  \n",
       "\n",
       "[1041899 rows x 38 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce129aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies_variables = pd.get_dummies(scaled['Label'])\n",
    "outcomes = dummies_variables.columns\n",
    "y = dummies_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f779338d",
   "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>BENIGN</th>\n",
       "      <th>DDoS</th>\n",
       "      <th>DoS</th>\n",
       "      <th>PortScan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041895</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041896</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041897</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041898</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041899 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         BENIGN  DDoS  DoS  PortScan\n",
       "0             1     0    0         0\n",
       "1             1     0    0         0\n",
       "2             1     0    0         0\n",
       "3             1     0    0         0\n",
       "4             1     0    0         0\n",
       "...         ...   ...  ...       ...\n",
       "1041894       1     0    0         0\n",
       "1041895       1     0    0         0\n",
       "1041896       1     0    0         0\n",
       "1041897       1     0    0         0\n",
       "1041898       1     0    0         0\n",
       "\n",
       "[1041899 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5abe813d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(x, y)\n",
    "importance = model.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "692443b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.72953024e-02, 3.26462110e-02, 9.21358664e-02, 1.32448135e-02,\n",
       "       2.71818601e-01, 1.41675154e-03, 3.15821814e-01, 3.80714315e-03,\n",
       "       9.84211764e-02, 1.15914860e-02, 2.19879064e-02, 8.11072903e-03,\n",
       "       1.87796805e-02, 1.47484297e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 1.83133646e-02, 1.58759575e-02, 1.29837885e-03,\n",
       "       3.81625152e-06, 8.08164930e-05, 2.53572671e-03, 2.82453600e-03,\n",
       "       0.00000000e+00, 8.23160643e-04, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.15291239e-02, 5.48365575e-03, 3.30480201e-03, 7.04655766e-03,\n",
       "       1.76086305e-03, 1.89427546e-03])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5096b9e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.bar([x for x in range(len(importance))], importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "295fb8e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 5, Score: 0.00142\n",
      "Feature: 7, Score: 0.00381\n",
      "Feature: 11, Score: 0.00811\n",
      "Feature: 13, Score: 0.00015\n",
      "Feature: 14, Score: 0.00000\n",
      "Feature: 15, Score: 0.00000\n",
      "Feature: 16, Score: 0.00000\n",
      "Feature: 19, Score: 0.00130\n",
      "Feature: 20, Score: 0.00000\n",
      "Feature: 21, Score: 0.00008\n",
      "Feature: 22, Score: 0.00254\n",
      "Feature: 23, Score: 0.00282\n",
      "Feature: 24, Score: 0.00000\n",
      "Feature: 25, Score: 0.00082\n",
      "Feature: 26, Score: 0.00000\n",
      "Feature: 27, Score: 0.00000\n",
      "Feature: 28, Score: 0.00000\n",
      "Feature: 29, Score: 0.00000\n",
      "Feature: 30, Score: 0.00000\n",
      "Feature: 31, Score: 0.00000\n",
      "Feature: 33, Score: 0.00548\n",
      "Feature: 34, Score: 0.00330\n",
      "Feature: 35, Score: 0.00705\n",
      "Feature: 36, Score: 0.00176\n",
      "Feature: 37, Score: 0.00189\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "loc=[]\n",
    "for i,v in enumerate(importance):\n",
    "    if(v<0.01):\n",
    "        print('Feature: %0d, Score: %.5f' % (i,v))\n",
    "        loc.append(i)\n",
    "        \n",
    "print(len(loc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "89d31a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 7, 11, 13, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37]\n"
     ]
    }
   ],
   "source": [
    "print(loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1c8cfaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled=scaled.drop(scaled.columns[[5, 7, 11, 13, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37]],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3befe4cf",
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Bwd Packet Length Max</th>\n",
       "      <th>Flow Bytes/s</th>\n",
       "      <th>Flow Packets/s</th>\n",
       "      <th>Flow IAT Mean</th>\n",
       "      <th>Bwd IAT Total</th>\n",
       "      <th>Bwd Packets/s</th>\n",
       "      <th>Min Packet Length</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.837225</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.840109</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085334</td>\n",
       "      <td>0.403670</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.587156e-03</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.840124</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085387</td>\n",
       "      <td>0.407692</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.615385e-03</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.705548</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085441</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.470588e-02</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.837194</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000504</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.685666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.085289</td>\n",
       "      <td>0.400037</td>\n",
       "      <td>5.379999e-05</td>\n",
       "      <td>2.500000e-08</td>\n",
       "      <td>3.104144e-05</td>\n",
       "      <td>0.019337</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041895</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.808333e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.009268</td>\n",
       "      <td>0.085893</td>\n",
       "      <td>0.402469</td>\n",
       "      <td>1.008333e-06</td>\n",
       "      <td>1.666667e-08</td>\n",
       "      <td>3.086420e-03</td>\n",
       "      <td>0.029006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041896</th>\n",
       "      <td>0.885522</td>\n",
       "      <td>7.916666e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085484</td>\n",
       "      <td>0.407317</td>\n",
       "      <td>4.500000e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.097561e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015366</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041897</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>8.738733e-03</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.006554</td>\n",
       "      <td>0.085285</td>\n",
       "      <td>0.400002</td>\n",
       "      <td>1.248483e-03</td>\n",
       "      <td>2.500000e-08</td>\n",
       "      <td>9.536207e-07</td>\n",
       "      <td>0.022099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041898</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>7.912666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.085287</td>\n",
       "      <td>0.400013</td>\n",
       "      <td>1.583400e-04</td>\n",
       "      <td>4.000000e-07</td>\n",
       "      <td>1.053308e-05</td>\n",
       "      <td>0.032459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041899 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                 0.837225    1.333333e-07            0.000005   \n",
       "1                 0.840109    1.016667e-06            0.000000   \n",
       "2                 0.840124    5.416666e-07            0.000000   \n",
       "3                 0.705548    3.916666e-07            0.000000   \n",
       "4                 0.837194    1.333333e-07            0.000005   \n",
       "...                    ...             ...                 ...   \n",
       "1041894           0.000809    2.685666e-04            0.000015   \n",
       "1041895           0.000809    2.808333e-06            0.000005   \n",
       "1041896           0.885522    7.916666e-07            0.000005   \n",
       "1041897           0.000809    8.738733e-03            0.000025   \n",
       "1041898           0.000809    7.912666e-04            0.000015   \n",
       "\n",
       "         Total Length of Fwd Packets   Fwd Packet Length Max  \\\n",
       "0                           0.000010                0.000242   \n",
       "1                           0.000005                0.000242   \n",
       "2                           0.000005                0.000242   \n",
       "3                           0.000005                0.000242   \n",
       "4                           0.000010                0.000242   \n",
       "...                              ...                     ...   \n",
       "1041894                     0.000091                0.001128   \n",
       "1041895                     0.000069                0.001692   \n",
       "1041896                     0.000025                0.001249   \n",
       "1041897                     0.000157                0.001289   \n",
       "1041898                     0.000154                0.001894   \n",
       "\n",
       "         Bwd Packet Length Max  Flow Bytes/s   Flow Packets/s   Flow IAT Mean  \\\n",
       "0                     0.000000      0.087053         0.533333    1.333333e-07   \n",
       "1                     0.000307      0.085334         0.403670    1.016667e-06   \n",
       "2                     0.000307      0.085387         0.407692    5.416666e-07   \n",
       "3                     0.000307      0.085441         0.411765    3.916666e-07   \n",
       "4                     0.000000      0.087053         0.533333    1.333333e-07   \n",
       "...                        ...           ...              ...             ...   \n",
       "1041894               0.003891      0.085289         0.400037    5.379999e-05   \n",
       "1041895               0.009268      0.085893         0.402469    1.008333e-06   \n",
       "1041896               0.000307      0.085484         0.407317    4.500000e-07   \n",
       "1041897               0.006554      0.085285         0.400002    1.248483e-03   \n",
       "1041898               0.005786      0.085287         0.400013    1.583400e-04   \n",
       "\n",
       "         Bwd IAT Total   Bwd Packets/s   Min Packet Length  \\\n",
       "0         0.000000e+00    0.000000e+00            0.004144   \n",
       "1         0.000000e+00    4.587156e-03            0.004144   \n",
       "2         0.000000e+00    9.615385e-03            0.004144   \n",
       "3         0.000000e+00    1.470588e-02            0.004144   \n",
       "4         0.000000e+00    0.000000e+00            0.004144   \n",
       "...                ...             ...                 ...   \n",
       "1041894   2.500000e-08    3.104144e-05            0.019337   \n",
       "1041895   1.666667e-08    3.086420e-03            0.029006   \n",
       "1041896   0.000000e+00    6.097561e-03            0.000000   \n",
       "1041897   2.500000e-08    9.536207e-07            0.022099   \n",
       "1041898   4.000000e-07    1.053308e-05            0.032459   \n",
       "\n",
       "         Init_Win_bytes_forward   Label  \n",
       "0                      0.000519  BENIGN  \n",
       "1                      0.000458  BENIGN  \n",
       "2                      0.000458  BENIGN  \n",
       "3                      0.000488  BENIGN  \n",
       "4                      0.000504  BENIGN  \n",
       "...                         ...     ...  \n",
       "1041894                0.000000  BENIGN  \n",
       "1041895                0.000000  BENIGN  \n",
       "1041896                0.015366  BENIGN  \n",
       "1041897                0.000000  BENIGN  \n",
       "1041898                0.000000  BENIGN  \n",
       "\n",
       "[1041899 rows x 14 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "381c4fb8",
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Bwd Packet Length Max</th>\n",
       "      <th>Flow Bytes/s</th>\n",
       "      <th>Flow Packets/s</th>\n",
       "      <th>Flow IAT Mean</th>\n",
       "      <th>Bwd IAT Total</th>\n",
       "      <th>Bwd Packets/s</th>\n",
       "      <th>Min Packet Length</th>\n",
       "      <th>Init_Win_bytes_forward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.837225</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.840109</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085334</td>\n",
       "      <td>0.403670</td>\n",
       "      <td>1.016667e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.587156e-03</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.840124</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085387</td>\n",
       "      <td>0.407692</td>\n",
       "      <td>5.416666e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.615385e-03</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.705548</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085441</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>3.916666e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.470588e-02</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.837194</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087053</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.333333e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.004144</td>\n",
       "      <td>0.000504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.685666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.085289</td>\n",
       "      <td>0.400037</td>\n",
       "      <td>5.379999e-05</td>\n",
       "      <td>2.500000e-08</td>\n",
       "      <td>3.104144e-05</td>\n",
       "      <td>0.019337</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041895</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>2.808333e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.001692</td>\n",
       "      <td>0.009268</td>\n",
       "      <td>0.085893</td>\n",
       "      <td>0.402469</td>\n",
       "      <td>1.008333e-06</td>\n",
       "      <td>1.666667e-08</td>\n",
       "      <td>3.086420e-03</td>\n",
       "      <td>0.029006</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041896</th>\n",
       "      <td>0.885522</td>\n",
       "      <td>7.916666e-07</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.085484</td>\n",
       "      <td>0.407317</td>\n",
       "      <td>4.500000e-07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.097561e-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041897</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>8.738733e-03</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>0.006554</td>\n",
       "      <td>0.085285</td>\n",
       "      <td>0.400002</td>\n",
       "      <td>1.248483e-03</td>\n",
       "      <td>2.500000e-08</td>\n",
       "      <td>9.536207e-07</td>\n",
       "      <td>0.022099</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041898</th>\n",
       "      <td>0.000809</td>\n",
       "      <td>7.912666e-04</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.001894</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.085287</td>\n",
       "      <td>0.400013</td>\n",
       "      <td>1.583400e-04</td>\n",
       "      <td>4.000000e-07</td>\n",
       "      <td>1.053308e-05</td>\n",
       "      <td>0.032459</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041899 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                 0.837225    1.333333e-07            0.000005   \n",
       "1                 0.840109    1.016667e-06            0.000000   \n",
       "2                 0.840124    5.416666e-07            0.000000   \n",
       "3                 0.705548    3.916666e-07            0.000000   \n",
       "4                 0.837194    1.333333e-07            0.000005   \n",
       "...                    ...             ...                 ...   \n",
       "1041894           0.000809    2.685666e-04            0.000015   \n",
       "1041895           0.000809    2.808333e-06            0.000005   \n",
       "1041896           0.885522    7.916666e-07            0.000005   \n",
       "1041897           0.000809    8.738733e-03            0.000025   \n",
       "1041898           0.000809    7.912666e-04            0.000015   \n",
       "\n",
       "         Total Length of Fwd Packets   Fwd Packet Length Max  \\\n",
       "0                           0.000010                0.000242   \n",
       "1                           0.000005                0.000242   \n",
       "2                           0.000005                0.000242   \n",
       "3                           0.000005                0.000242   \n",
       "4                           0.000010                0.000242   \n",
       "...                              ...                     ...   \n",
       "1041894                     0.000091                0.001128   \n",
       "1041895                     0.000069                0.001692   \n",
       "1041896                     0.000025                0.001249   \n",
       "1041897                     0.000157                0.001289   \n",
       "1041898                     0.000154                0.001894   \n",
       "\n",
       "         Bwd Packet Length Max  Flow Bytes/s   Flow Packets/s   Flow IAT Mean  \\\n",
       "0                     0.000000      0.087053         0.533333    1.333333e-07   \n",
       "1                     0.000307      0.085334         0.403670    1.016667e-06   \n",
       "2                     0.000307      0.085387         0.407692    5.416666e-07   \n",
       "3                     0.000307      0.085441         0.411765    3.916666e-07   \n",
       "4                     0.000000      0.087053         0.533333    1.333333e-07   \n",
       "...                        ...           ...              ...             ...   \n",
       "1041894               0.003891      0.085289         0.400037    5.379999e-05   \n",
       "1041895               0.009268      0.085893         0.402469    1.008333e-06   \n",
       "1041896               0.000307      0.085484         0.407317    4.500000e-07   \n",
       "1041897               0.006554      0.085285         0.400002    1.248483e-03   \n",
       "1041898               0.005786      0.085287         0.400013    1.583400e-04   \n",
       "\n",
       "         Bwd IAT Total   Bwd Packets/s   Min Packet Length  \\\n",
       "0         0.000000e+00    0.000000e+00            0.004144   \n",
       "1         0.000000e+00    4.587156e-03            0.004144   \n",
       "2         0.000000e+00    9.615385e-03            0.004144   \n",
       "3         0.000000e+00    1.470588e-02            0.004144   \n",
       "4         0.000000e+00    0.000000e+00            0.004144   \n",
       "...                ...             ...                 ...   \n",
       "1041894   2.500000e-08    3.104144e-05            0.019337   \n",
       "1041895   1.666667e-08    3.086420e-03            0.029006   \n",
       "1041896   0.000000e+00    6.097561e-03            0.000000   \n",
       "1041897   2.500000e-08    9.536207e-07            0.022099   \n",
       "1041898   4.000000e-07    1.053308e-05            0.032459   \n",
       "\n",
       "         Init_Win_bytes_forward  \n",
       "0                      0.000519  \n",
       "1                      0.000458  \n",
       "2                      0.000458  \n",
       "3                      0.000488  \n",
       "4                      0.000504  \n",
       "...                         ...  \n",
       "1041894                0.000000  \n",
       "1041895                0.000000  \n",
       "1041896                0.015366  \n",
       "1041897                0.000000  \n",
       "1041898                0.000000  \n",
       "\n",
       "[1041899 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "7b2583f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1041899, 14)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "93d21b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(x.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "16468b0a",
   "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>BENIGN</th>\n",
       "      <th>DDoS</th>\n",
       "      <th>DoS</th>\n",
       "      <th>PortScan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041894</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041895</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041896</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041897</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041898</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1041899 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         BENIGN  DDoS  DoS  PortScan\n",
       "0             1     0    0         0\n",
       "1             1     0    0         0\n",
       "2             1     0    0         0\n",
       "3             1     0    0         0\n",
       "4             1     0    0         0\n",
       "...         ...   ...  ...       ...\n",
       "1041894       1     0    0         0\n",
       "1041895       1     0    0         0\n",
       "1041896       1     0    0         0\n",
       "1041897       1     0    0         0\n",
       "1041898       1     0    0         0\n",
       "\n",
       "[1041899 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec17532",
   "metadata": {},
   "source": [
    "## Test train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "663fde0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain, xtest, ytrain, ytest = tra0in_test_split(x, y, random_state=42,test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a88072bf",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b77a2385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9390024634481876\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier()\n",
    "clf.fit(xtrain,ytrain)\n",
    "print(clf.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ba8ef2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_params = {\n",
    "    'Random Forest': {\n",
    "        'model': RandomForestClassifier(),\n",
    "        'params' : {\n",
    "            'n_estimators': [10, 50, 100, 200, 300, 400, 500],\n",
    "            'criterion': ['gini', 'entropy', 'log_loss'],\n",
    "            'max_features': ['None', 'sqrt', 'log2']\n",
    "        }\n",
    "    },\n",
    "    'Decision Tree' : {\n",
    "        'model': DecisionTreeClassifier(),\n",
    "        'params': {\n",
    "            'criterion': ['gini', 'entropy', 'log_loss'],\n",
    "            'splitter': ['best', 'random'],\n",
    "            'max_features': ['auto', 'sqrt', 'log2']\n",
    "        }\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4551f622",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:378: FitFailedWarning: \n",
      "12 fits failed out of a total of 30.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "12 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 686, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 476, in fit\n",
      "    trees = Parallel(\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\joblib\\parallel.py\", line 1085, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\joblib\\parallel.py\", line 901, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\joblib\\parallel.py\", line 819, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\joblib\\_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\joblib\\_parallel_backends.py\", line 597, in __init__\n",
      "    self.results = batch()\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\joblib\\parallel.py\", line 288, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\joblib\\parallel.py\", line 288, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\utils\\fixes.py\", line 117, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\", line 189, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 969, in fit\n",
      "    super().fit(\n",
      "  File \"C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 317, in fit\n",
      "    raise ValueError(\n",
      "ValueError: Invalid value for max_features. Allowed string values are 'auto', 'sqrt' or 'log2'.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\model_selection\\_search.py:953: UserWarning: One or more of the test scores are non-finite: [       nan 0.93894251 0.93876152        nan        nan 0.93875329\n",
      "        nan 0.93899187 0.93898913 0.93889178]\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n",
      "C:\\Users\\prash\\anaconda3\\envs\\gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py:298: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "scores1 = []\n",
    "\n",
    "for model_name, mp in model_params.items():\n",
    "    clf =  RandomizedSearchCV(mp['model'], mp['params'], cv=3, return_train_score=False)\n",
    "    clf.fit(xtrain, ytrain)\n",
    "    \n",
    "    scores1.append({\n",
    "        'model': model_name,\n",
    "        'best_score': clf.best_score_,\n",
    "        'best_params': clf.best_params_,\n",
    "        'accuracy' : clf.score(xtest, ytest)\n",
    "    })\n",
    "    \n",
    "acc1 = pd.DataFrame(scores1,columns=['model','best_score','best_params', 'accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "753e75a5",
   "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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.938992</td>\n",
       "      <td>{'n_estimators': 300, 'max_features': 'log2', ...</td>\n",
       "      <td>0.939095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.912514</td>\n",
       "      <td>{'splitter': 'best', 'max_features': 'sqrt', '...</td>\n",
       "      <td>0.910772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  best_score  \\\n",
       "0  Random Forest    0.938992   \n",
       "1  Decision Tree    0.912514   \n",
       "\n",
       "                                         best_params  accuracy  \n",
       "0  {'n_estimators': 300, 'max_features': 'log2', ...  0.939095  \n",
       "1  {'splitter': 'best', 'max_features': 'sqrt', '...  0.910772  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "52509201",
   "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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.938992</td>\n",
       "      <td>{'n_estimators': 300, 'max_features': 'log2', ...</td>\n",
       "      <td>0.939095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           model  best_score  \\\n",
       "0  Random Forest    0.938992   \n",
       "\n",
       "                                         best_params  accuracy  \n",
       "0  {'n_estimators': 300, 'max_features': 'log2', ...  0.939095  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxAccuracy = acc1['accuracy'].max()\n",
    "bestModel = acc1.loc[acc1['accuracy'] == maxAccuracy]\n",
    "modelSelect = model_params[bestModel['model'].iloc[0]]\n",
    "bestModelParameters = bestModel['best_params'].iloc[0]\n",
    "bestModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f9a44d5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;log_loss&#x27;, max_features=&#x27;log2&#x27;,\n",
       "                       n_estimators=300)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;log_loss&#x27;, max_features=&#x27;log2&#x27;,\n",
       "                       n_estimators=300)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='log_loss', max_features='log2',\n",
       "                       n_estimators=300)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = modelSelect['model']\n",
    "model.set_params(**bestModelParameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9f9869bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifier = KNeighborsClassifier(n_neighbors = 5, p = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e977b84a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1260f1f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7b847b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=[]\n",
    "for i in range(3,11,2):\n",
    "    classifier = KNeighborsClassifier(n_neighbors = i, p = 2)\n",
    "    classifier.fit(xtrain, ytrain)\n",
    "    y_pred = classifier.predict(xtest)\n",
    "    ac = accuracy_score(ytest,y_pred)\n",
    "    acc.append(ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "623f176a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ef6e594f10>]"
      ]
     },
     "execution_count": 105,
     "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": [
    "plt.plot([3,5,7,9],acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "d89f7e54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9281632914227214, 0.9336756566529097, 0.935182519115718, 0.935582429535784]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "c42f534c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(ytest.values.argmax(axis=1), y_pred.argmax(axis=1))\n",
    "ac = accuracy_score(ytest,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "54baba9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[180085    135   5591   2834]\n",
      " [   130  38071     15      1]\n",
      " [  8911     10  49416     24]\n",
      " [  2669      1     18  24659]]\n",
      "\n",
      "0.9336756566529097\n"
     ]
    }
   ],
   "source": [
    "print(cm)\n",
    "print()\n",
    "print(ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a4f1215",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b646c664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;log_loss&#x27;, max_features=&#x27;log2&#x27;,\n",
       "                       n_estimators=300)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;log_loss&#x27;, max_features=&#x27;log2&#x27;,\n",
       "                       n_estimators=300)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='log_loss', max_features='log2',\n",
       "                       n_estimators=300)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1926afe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9390504526985956\n"
     ]
    }
   ],
   "source": [
    "print(model.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "87e4717b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred=model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "cb8da07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(ytest.values.argmax(axis=1), ypred.argmax(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "72f7f77a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[180361,    100,   5588,   2596],\n",
       "       [   106,  38107,      4,      0],\n",
       "       [  8121,      0,  50234,      6],\n",
       "       [  2303,      0,      7,  25037]], dtype=int64)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}