intrusionDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas : 0.23.1\n",
      "numpy : 1.19.5\n",
      "matplotlib : 3.3.4\n",
      "seaborn : 0.11.1\n",
      "sklearn : 0.24.1\n",
      "imblearn : 0.8.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "import imblearn\n",
    "import sys\n",
    "\n",
    "# Ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Settings\n",
    "pd.set_option('display.max_columns', None)\n",
    "np.set_printoptions(threshold=sys.maxsize)\n",
    "np.set_printoptions(precision=3)\n",
    "sns.set(style=\"darkgrid\")\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "print(\"pandas : {0}\".format(pd.__version__))\n",
    "print(\"numpy : {0}\".format(np.__version__))\n",
    "print(\"matplotlib : {0}\".format(matplotlib.__version__))\n",
    "print(\"seaborn : {0}\".format(sns.__version__))\n",
    "print(\"sklearn : {0}\".format(sklearn.__version__))\n",
    "print(\"imblearn : {0}\".format(imblearn.__version__))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset field names\n",
    "datacols = [\"duration\",\"protocol_type\",\"service\",\"flag\",\"src_bytes\",\n",
    "    \"dst_bytes\",\"land\",\"wrong_fragment\",\"urgent\",\"hot\",\"num_failed_logins\",\n",
    "    \"logged_in\",\"num_compromised\",\"root_shell\",\"su_attempted\",\"num_root\",\n",
    "    \"num_file_creations\",\"num_shells\",\"num_access_files\",\"num_outbound_cmds\",\n",
    "    \"is_host_login\",\"is_guest_login\",\"count\",\"srv_count\",\"serror_rate\",\n",
    "    \"srv_serror_rate\",\"rerror_rate\",\"srv_rerror_rate\",\"same_srv_rate\",\n",
    "    \"diff_srv_rate\",\"srv_diff_host_rate\",\"dst_host_count\",\"dst_host_srv_count\",\n",
    "    \"dst_host_same_srv_rate\",\"dst_host_diff_srv_rate\",\"dst_host_same_src_port_rate\",\n",
    "    \"dst_host_srv_diff_host_rate\",\"dst_host_serror_rate\",\"dst_host_srv_serror_rate\",\n",
    "    \"dst_host_rerror_rate\",\"dst_host_srv_rerror_rate\",\"attack\", \"last_flag\"]\n",
    "\n",
    "# Load NSL_KDD train dataset\n",
    "dfkdd_train = pd.read_table(\"KDDTrain.txt\", sep=\",\", names=datacols) # change path to where the dataset is located.\n",
    "dfkdd_train = dfkdd_train.iloc[:,:-1] # removes an unwanted extra field\n",
    "\n",
    "# Load NSL_KDD test dataset\n",
    "dfkdd_test = pd.read_table(\"KDDTest.txt\", sep=\",\", names=datacols)\n",
    "dfkdd_test = dfkdd_test.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set dimension: 125973 rows, 42 columns\n"
     ]
    }
   ],
   "source": [
    "#View train data\n",
    "dfkdd_train.head(3)\n",
    "\n",
    "# train set dimension\n",
    "print('Train set dimension: {} rows, {} columns'.format(dfkdd_train.shape[0], dfkdd_train.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set dimension: 22544 rows, 42 columns\n"
     ]
    }
   ],
   "source": [
    "# View test data\n",
    "dfkdd_test.head(3)\n",
    "\n",
    "# test set dimension\n",
    "print('Test set dimension: {} rows, {} columns'.format(dfkdd_test.shape[0], dfkdd_test.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'ipsweep': 'Probe','satan': 'Probe','nmap': 'Probe','portsweep': 'Probe','saint': 'Probe','mscan': 'Probe',\n",
    "        'teardrop': 'DoS','pod': 'DoS','land': 'DoS','back': 'DoS','neptune': 'DoS','smurf': 'DoS','mailbomb': 'DoS',\n",
    "        'udpstorm': 'DoS','apache2': 'DoS','processtable': 'DoS',\n",
    "        'perl': 'U2R','loadmodule': 'U2R','rootkit': 'U2R','buffer_overflow': 'U2R','xterm': 'U2R','ps': 'U2R',\n",
    "        'sqlattack': 'U2R','httptunnel': 'U2R',\n",
    "        'ftp_write': 'R2L','phf': 'R2L','guess_passwd': 'R2L','warezmaster': 'R2L','warezclient': 'R2L','imap': 'R2L',\n",
    "        'spy': 'R2L','multihop': 'R2L','named': 'R2L','snmpguess': 'R2L','worm': 'R2L','snmpgetattack': 'R2L',\n",
    "        'xsnoop': 'R2L','xlock': 'R2L','sendmail': 'R2L',\n",
    "        'normal': 'Normal'\n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply attack class mappings to the dataset\n",
    "dfkdd_train['attack_class'] = dfkdd_train['attack'].apply(lambda v: mapping[v])\n",
    "dfkdd_test['attack_class'] = dfkdd_test['attack'].apply(lambda v: mapping[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop attack field from both train and test data\n",
    "dfkdd_train.drop(['attack'], axis=1, inplace=True)\n",
    "dfkdd_test.drop(['attack'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
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       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>root_shell</th>\n",
       "      <th>su_attempted</th>\n",
       "      <th>num_root</th>\n",
       "      <th>num_file_creations</th>\n",
       "      <th>num_shells</th>\n",
       "      <th>num_access_files</th>\n",
       "      <th>num_outbound_cmds</th>\n",
       "      <th>is_host_login</th>\n",
       "      <th>is_guest_login</th>\n",
       "      <th>count</th>\n",
       "      <th>srv_count</th>\n",
       "      <th>serror_rate</th>\n",
       "      <th>srv_serror_rate</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>srv_rerror_rate</th>\n",
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       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150</td>\n",
       "      <td>25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
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       "      <td>26</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>DoS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp  ftp_data   SF        491          0     0   \n",
       "1         0           udp     other   SF        146          0     0   \n",
       "2         0           tcp   private   S0          0          0     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  num_failed_logins  logged_in  num_compromised  \\\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",
       "\n",
       "   root_shell  su_attempted  num_root  num_file_creations  num_shells  \\\n",
       "0           0             0         0                   0           0   \n",
       "1           0             0         0                   0           0   \n",
       "2           0             0         0                   0           0   \n",
       "\n",
       "   num_access_files  num_outbound_cmds  is_host_login  is_guest_login  count  \\\n",
       "0                 0                  0              0               0      2   \n",
       "1                 0                  0              0               0     13   \n",
       "2                 0                  0              0               0    123   \n",
       "\n",
       "   srv_count  serror_rate  srv_serror_rate  rerror_rate  srv_rerror_rate  \\\n",
       "0          2          0.0              0.0          0.0              0.0   \n",
       "1          1          0.0              0.0          0.0              0.0   \n",
       "2          6          1.0              1.0          0.0              0.0   \n",
       "\n",
       "   same_srv_rate  diff_srv_rate  srv_diff_host_rate  dst_host_count  \\\n",
       "0           1.00           0.00                 0.0             150   \n",
       "1           0.08           0.15                 0.0             255   \n",
       "2           0.05           0.07                 0.0             255   \n",
       "\n",
       "   dst_host_srv_count  dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                  25                    0.17                    0.03   \n",
       "1                   1                    0.00                    0.60   \n",
       "2                  26                    0.10                    0.05   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.17                          0.0   \n",
       "1                         0.88                          0.0   \n",
       "2                         0.00                          0.0   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                   0.0                       0.0                  0.05   \n",
       "1                   0.0                       0.0                  0.00   \n",
       "2                   1.0                       1.0                  0.00   \n",
       "\n",
       "   dst_host_srv_rerror_rate attack_class  \n",
       "0                       0.0       Normal  \n",
       "1                       0.0       Normal  \n",
       "2                       0.0          DoS  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View top 3 train data \n",
    "dfkdd_train.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "      <td>125973.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>287.14465</td>\n",
       "      <td>4.556674e+04</td>\n",
       "      <td>1.977911e+04</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.022687</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.204409</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.395736</td>\n",
       "      <td>0.279250</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>0.302192</td>\n",
       "      <td>0.012669</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.004096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.009423</td>\n",
       "      <td>84.107555</td>\n",
       "      <td>27.737888</td>\n",
       "      <td>0.284485</td>\n",
       "      <td>0.282485</td>\n",
       "      <td>0.119958</td>\n",
       "      <td>0.121183</td>\n",
       "      <td>0.660928</td>\n",
       "      <td>0.063053</td>\n",
       "      <td>0.097322</td>\n",
       "      <td>182.148945</td>\n",
       "      <td>115.653005</td>\n",
       "      <td>0.521242</td>\n",
       "      <td>0.082951</td>\n",
       "      <td>0.148379</td>\n",
       "      <td>0.032542</td>\n",
       "      <td>0.284452</td>\n",
       "      <td>0.278485</td>\n",
       "      <td>0.118832</td>\n",
       "      <td>0.120240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2604.51531</td>\n",
       "      <td>5.870331e+06</td>\n",
       "      <td>4.021269e+06</td>\n",
       "      <td>0.014086</td>\n",
       "      <td>0.253530</td>\n",
       "      <td>0.014366</td>\n",
       "      <td>2.149968</td>\n",
       "      <td>0.045239</td>\n",
       "      <td>0.489010</td>\n",
       "      <td>23.942042</td>\n",
       "      <td>0.036603</td>\n",
       "      <td>0.045154</td>\n",
       "      <td>24.399618</td>\n",
       "      <td>0.483935</td>\n",
       "      <td>0.022181</td>\n",
       "      <td>0.099370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>0.096612</td>\n",
       "      <td>114.508607</td>\n",
       "      <td>72.635840</td>\n",
       "      <td>0.446456</td>\n",
       "      <td>0.447022</td>\n",
       "      <td>0.320436</td>\n",
       "      <td>0.323647</td>\n",
       "      <td>0.439623</td>\n",
       "      <td>0.180314</td>\n",
       "      <td>0.259830</td>\n",
       "      <td>99.206213</td>\n",
       "      <td>110.702741</td>\n",
       "      <td>0.448949</td>\n",
       "      <td>0.188922</td>\n",
       "      <td>0.308997</td>\n",
       "      <td>0.112564</td>\n",
       "      <td>0.444784</td>\n",
       "      <td>0.445669</td>\n",
       "      <td>0.306557</td>\n",
       "      <td>0.319459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.760000e+02</td>\n",
       "      <td>5.160000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42908.00000</td>\n",
       "      <td>1.379964e+09</td>\n",
       "      <td>1.309937e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7479.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7468.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           duration     src_bytes     dst_bytes           land  \\\n",
       "count  125973.00000  1.259730e+05  1.259730e+05  125973.000000   \n",
       "mean      287.14465  4.556674e+04  1.977911e+04       0.000198   \n",
       "std      2604.51531  5.870331e+06  4.021269e+06       0.014086   \n",
       "min         0.00000  0.000000e+00  0.000000e+00       0.000000   \n",
       "25%         0.00000  0.000000e+00  0.000000e+00       0.000000   \n",
       "50%         0.00000  4.400000e+01  0.000000e+00       0.000000   \n",
       "75%         0.00000  2.760000e+02  5.160000e+02       0.000000   \n",
       "max     42908.00000  1.379964e+09  1.309937e+09       1.000000   \n",
       "\n",
       "       wrong_fragment         urgent            hot  num_failed_logins  \\\n",
       "count   125973.000000  125973.000000  125973.000000      125973.000000   \n",
       "mean         0.022687       0.000111       0.204409           0.001222   \n",
       "std          0.253530       0.014366       2.149968           0.045239   \n",
       "min          0.000000       0.000000       0.000000           0.000000   \n",
       "25%          0.000000       0.000000       0.000000           0.000000   \n",
       "50%          0.000000       0.000000       0.000000           0.000000   \n",
       "75%          0.000000       0.000000       0.000000           0.000000   \n",
       "max          3.000000       3.000000      77.000000           5.000000   \n",
       "\n",
       "           logged_in  num_compromised     root_shell   su_attempted  \\\n",
       "count  125973.000000    125973.000000  125973.000000  125973.000000   \n",
       "mean        0.395736         0.279250       0.001342       0.001103   \n",
       "std         0.489010        23.942042       0.036603       0.045154   \n",
       "min         0.000000         0.000000       0.000000       0.000000   \n",
       "25%         0.000000         0.000000       0.000000       0.000000   \n",
       "50%         0.000000         0.000000       0.000000       0.000000   \n",
       "75%         1.000000         0.000000       0.000000       0.000000   \n",
       "max         1.000000      7479.000000       1.000000       2.000000   \n",
       "\n",
       "            num_root  num_file_creations     num_shells  num_access_files  \\\n",
       "count  125973.000000       125973.000000  125973.000000     125973.000000   \n",
       "mean        0.302192            0.012669       0.000413          0.004096   \n",
       "std        24.399618            0.483935       0.022181          0.099370   \n",
       "min         0.000000            0.000000       0.000000          0.000000   \n",
       "25%         0.000000            0.000000       0.000000          0.000000   \n",
       "50%         0.000000            0.000000       0.000000          0.000000   \n",
       "75%         0.000000            0.000000       0.000000          0.000000   \n",
       "max      7468.000000           43.000000       2.000000          9.000000   \n",
       "\n",
       "       num_outbound_cmds  is_host_login  is_guest_login          count  \\\n",
       "count           125973.0  125973.000000   125973.000000  125973.000000   \n",
       "mean                 0.0       0.000008        0.009423      84.107555   \n",
       "std                  0.0       0.002817        0.096612     114.508607   \n",
       "min                  0.0       0.000000        0.000000       0.000000   \n",
       "25%                  0.0       0.000000        0.000000       2.000000   \n",
       "50%                  0.0       0.000000        0.000000      14.000000   \n",
       "75%                  0.0       0.000000        0.000000     143.000000   \n",
       "max                  0.0       1.000000        1.000000     511.000000   \n",
       "\n",
       "           srv_count    serror_rate  srv_serror_rate    rerror_rate  \\\n",
       "count  125973.000000  125973.000000    125973.000000  125973.000000   \n",
       "mean       27.737888       0.284485         0.282485       0.119958   \n",
       "std        72.635840       0.446456         0.447022       0.320436   \n",
       "min         0.000000       0.000000         0.000000       0.000000   \n",
       "25%         2.000000       0.000000         0.000000       0.000000   \n",
       "50%         8.000000       0.000000         0.000000       0.000000   \n",
       "75%        18.000000       1.000000         1.000000       0.000000   \n",
       "max       511.000000       1.000000         1.000000       1.000000   \n",
       "\n",
       "       srv_rerror_rate  same_srv_rate  diff_srv_rate  srv_diff_host_rate  \\\n",
       "count    125973.000000  125973.000000  125973.000000       125973.000000   \n",
       "mean          0.121183       0.660928       0.063053            0.097322   \n",
       "std           0.323647       0.439623       0.180314            0.259830   \n",
       "min           0.000000       0.000000       0.000000            0.000000   \n",
       "25%           0.000000       0.090000       0.000000            0.000000   \n",
       "50%           0.000000       1.000000       0.000000            0.000000   \n",
       "75%           0.000000       1.000000       0.060000            0.000000   \n",
       "max           1.000000       1.000000       1.000000            1.000000   \n",
       "\n",
       "       dst_host_count  dst_host_srv_count  dst_host_same_srv_rate  \\\n",
       "count   125973.000000       125973.000000           125973.000000   \n",
       "mean       182.148945          115.653005                0.521242   \n",
       "std         99.206213          110.702741                0.448949   \n",
       "min          0.000000            0.000000                0.000000   \n",
       "25%         82.000000           10.000000                0.050000   \n",
       "50%        255.000000           63.000000                0.510000   \n",
       "75%        255.000000          255.000000                1.000000   \n",
       "max        255.000000          255.000000                1.000000   \n",
       "\n",
       "       dst_host_diff_srv_rate  dst_host_same_src_port_rate  \\\n",
       "count           125973.000000                125973.000000   \n",
       "mean                 0.082951                     0.148379   \n",
       "std                  0.188922                     0.308997   \n",
       "min                  0.000000                     0.000000   \n",
       "25%                  0.000000                     0.000000   \n",
       "50%                  0.020000                     0.000000   \n",
       "75%                  0.070000                     0.060000   \n",
       "max                  1.000000                     1.000000   \n",
       "\n",
       "       dst_host_srv_diff_host_rate  dst_host_serror_rate  \\\n",
       "count                125973.000000         125973.000000   \n",
       "mean                      0.032542              0.284452   \n",
       "std                       0.112564              0.444784   \n",
       "min                       0.000000              0.000000   \n",
       "25%                       0.000000              0.000000   \n",
       "50%                       0.000000              0.000000   \n",
       "75%                       0.020000              1.000000   \n",
       "max                       1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "count             125973.000000         125973.000000   \n",
       "mean                   0.278485              0.118832   \n",
       "std                    0.445669              0.306557   \n",
       "min                    0.000000              0.000000   \n",
       "25%                    0.000000              0.000000   \n",
       "50%                    0.000000              0.000000   \n",
       "75%                    1.000000              0.000000   \n",
       "max                    1.000000              1.000000   \n",
       "\n",
       "       dst_host_srv_rerror_rate  \n",
       "count             125973.000000  \n",
       "mean                   0.120240  \n",
       "std                    0.319459  \n",
       "min                    0.000000  \n",
       "25%                    0.000000  \n",
       "50%                    0.000000  \n",
       "75%                    0.000000  \n",
       "max                    1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "dfkdd_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22544\n",
       "Name: num_outbound_cmds, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dfkdd_train['num_outbound_cmds'].value_counts()\n",
    "dfkdd_test['num_outbound_cmds'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'num_outbound_cmds' field has all 0 values. Hence, it will be removed from both train and test dataset since it is a redundant field.\n",
    "dfkdd_train.drop(['num_outbound_cmds'], axis=1, inplace=True)\n",
    "dfkdd_test.drop(['num_outbound_cmds'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>attack_class</th>\n",
       "      <th>frequency_percent_train</th>\n",
       "      <th>attack_class</th>\n",
       "      <th>frequency_percent_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DoS</th>\n",
       "      <td>45927</td>\n",
       "      <td>36.46</td>\n",
       "      <td>7458</td>\n",
       "      <td>33.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>67343</td>\n",
       "      <td>53.46</td>\n",
       "      <td>9711</td>\n",
       "      <td>43.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Probe</th>\n",
       "      <td>11656</td>\n",
       "      <td>9.25</td>\n",
       "      <td>2421</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2L</th>\n",
       "      <td>995</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2754</td>\n",
       "      <td>12.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U2R</th>\n",
       "      <td>52</td>\n",
       "      <td>0.04</td>\n",
       "      <td>200</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        attack_class  frequency_percent_train  attack_class  \\\n",
       "DoS            45927                    36.46          7458   \n",
       "Normal         67343                    53.46          9711   \n",
       "Probe          11656                     9.25          2421   \n",
       "R2L              995                     0.79          2754   \n",
       "U2R               52                     0.04           200   \n",
       "\n",
       "        frequency_percent_test  \n",
       "DoS                      33.08  \n",
       "Normal                   43.08  \n",
       "Probe                    10.74  \n",
       "R2L                      12.22  \n",
       "U2R                       0.89  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attack Class Distribution\n",
    "attack_class_freq_train = dfkdd_train[['attack_class']].apply(lambda x: x.value_counts())\n",
    "attack_class_freq_test = dfkdd_test[['attack_class']].apply(lambda x: x.value_counts())\n",
    "attack_class_freq_train['frequency_percent_train'] = round((100 * attack_class_freq_train / attack_class_freq_train.sum()),2)\n",
    "attack_class_freq_test['frequency_percent_test'] = round((100 * attack_class_freq_test / attack_class_freq_test.sum()),2)\n",
    "\n",
    "attack_class_dist = pd.concat([attack_class_freq_train,attack_class_freq_test], axis=1) \n",
    "attack_class_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Attack class bar plot\n",
    "plot = attack_class_dist[['frequency_percent_train', 'frequency_percent_test']].plot(kind=\"bar\");\n",
    "plot.set_title(\"Attack Class Distribution\", fontsize=20);\n",
    "plot.grid(color='lightgray', alpha=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocol_type</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>logged_in</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>root_shell</th>\n",
       "      <th>su_attempted</th>\n",
       "      <th>num_root</th>\n",
       "      <th>num_file_creations</th>\n",
       "      <th>num_shells</th>\n",
       "      <th>num_access_files</th>\n",
       "      <th>is_host_login</th>\n",
       "      <th>is_guest_login</th>\n",
       "      <th>count</th>\n",
       "      <th>srv_count</th>\n",
       "      <th>serror_rate</th>\n",
       "      <th>srv_serror_rate</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>srv_rerror_rate</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>attack_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>ftp_data</td>\n",
       "      <td>SF</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>150</td>\n",
       "      <td>25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>udp</td>\n",
       "      <td>other</td>\n",
       "      <td>SF</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>private</td>\n",
       "      <td>S0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>255</td>\n",
       "      <td>26</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>DoS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>232</td>\n",
       "      <td>8153</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>199</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.09</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
       "0         0           tcp  ftp_data   SF        491          0     0   \n",
       "1         0           udp     other   SF        146          0     0   \n",
       "2         0           tcp   private   S0          0          0     0   \n",
       "3         0           tcp      http   SF        232       8153     0   \n",
       "4         0           tcp      http   SF        199        420     0   \n",
       "\n",
       "   wrong_fragment  urgent  hot  num_failed_logins  logged_in  num_compromised  \\\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          1                0   \n",
       "4               0       0    0                  0          1                0   \n",
       "\n",
       "   root_shell  su_attempted  num_root  num_file_creations  num_shells  \\\n",
       "0           0             0         0                   0           0   \n",
       "1           0             0         0                   0           0   \n",
       "2           0             0         0                   0           0   \n",
       "3           0             0         0                   0           0   \n",
       "4           0             0         0                   0           0   \n",
       "\n",
       "   num_access_files  is_host_login  is_guest_login  count  srv_count  \\\n",
       "0                 0              0               0      2          2   \n",
       "1                 0              0               0     13          1   \n",
       "2                 0              0               0    123          6   \n",
       "3                 0              0               0      5          5   \n",
       "4                 0              0               0     30         32   \n",
       "\n",
       "   serror_rate  srv_serror_rate  rerror_rate  srv_rerror_rate  same_srv_rate  \\\n",
       "0          0.0              0.0          0.0              0.0           1.00   \n",
       "1          0.0              0.0          0.0              0.0           0.08   \n",
       "2          1.0              1.0          0.0              0.0           0.05   \n",
       "3          0.2              0.2          0.0              0.0           1.00   \n",
       "4          0.0              0.0          0.0              0.0           1.00   \n",
       "\n",
       "   diff_srv_rate  srv_diff_host_rate  dst_host_count  dst_host_srv_count  \\\n",
       "0           0.00                0.00             150                  25   \n",
       "1           0.15                0.00             255                   1   \n",
       "2           0.07                0.00             255                  26   \n",
       "3           0.00                0.00              30                 255   \n",
       "4           0.00                0.09             255                 255   \n",
       "\n",
       "   dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
       "0                    0.17                    0.03   \n",
       "1                    0.00                    0.60   \n",
       "2                    0.10                    0.05   \n",
       "3                    1.00                    0.00   \n",
       "4                    1.00                    0.00   \n",
       "\n",
       "   dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
       "0                         0.17                         0.00   \n",
       "1                         0.88                         0.00   \n",
       "2                         0.00                         0.00   \n",
       "3                         0.03                         0.04   \n",
       "4                         0.00                         0.00   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
       "0                  0.00                      0.00                  0.05   \n",
       "1                  0.00                      0.00                  0.00   \n",
       "2                  1.00                      1.00                  0.00   \n",
       "3                  0.03                      0.01                  0.00   \n",
       "4                  0.00                      0.00                  0.00   \n",
       "\n",
       "   dst_host_srv_rerror_rate attack_class  \n",
       "0                      0.00       Normal  \n",
       "1                      0.00       Normal  \n",
       "2                      0.00          DoS  \n",
       "3                      0.01       Normal  \n",
       "4                      0.00       Normal  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfkdd_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# extract numerical attributes and scale it to have zero mean and unit variance  \n",
    "cols = dfkdd_train.select_dtypes(include=['float64','int64']).columns\n",
    "sc_train = scaler.fit_transform(dfkdd_train.select_dtypes(include=['float64','int64']))\n",
    "sc_test = scaler.fit_transform(dfkdd_test.select_dtypes(include=['float64','int64']))\n",
    "\n",
    "# turn the result back to a dataframe\n",
    "sc_traindf = pd.DataFrame(sc_train, columns = cols)\n",
    "sc_testdf = pd.DataFrame(sc_test, columns = cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "\n",
    "# extract categorical attributes from both training and test sets \n",
    "cattrain = dfkdd_train.select_dtypes(include=['object']).copy()\n",
    "cattest = dfkdd_test.select_dtypes(include=['object']).copy()\n",
    "\n",
    "# encode the categorical attributes\n",
    "traincat = cattrain.apply(encoder.fit_transform)\n",
    "testcat = cattest.apply(encoder.fit_transform)\n",
    "\n",
    "# separate target column from encoded data \n",
    "enctrain = traincat.drop(['attack_class'], axis=1)\n",
    "enctest = testcat.drop(['attack_class'], axis=1)\n",
    "\n",
    "cat_Ytrain = traincat[['attack_class']].copy()\n",
    "cat_Ytest = testcat[['attack_class']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({1: 67343, 0: 45927, 2: 11656, 3: 995, 4: 52})\n",
      "Resampled dataset shape Counter({1: 67343, 0: 67343, 3: 67343, 2: 67343, 4: 67343})\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler \n",
    "from collections import Counter\n",
    "\n",
    "# define columns and extract encoded train set for sampling \n",
    "sc_traindf = dfkdd_train.select_dtypes(include=['float64','int64'])\n",
    "refclasscol = pd.concat([sc_traindf, enctrain], axis=1).columns\n",
    "refclass = np.concatenate((sc_train, enctrain.values), axis=1)\n",
    "X = refclass\n",
    "\n",
    "# reshape target column to 1D array shape  \n",
    "c, r = cat_Ytest.values.shape\n",
    "y_test = cat_Ytest.values.reshape(c,)\n",
    "\n",
    "c, r = cat_Ytrain.values.shape\n",
    "y = cat_Ytrain.values.reshape(c,)\n",
    "\n",
    "# apply the random over-sampling\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_res, y_res = ros.fit_resample(X, y)\n",
    "print('Original dataset shape {}'.format(Counter(y)))\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(max_depth=2, random_state=0);\n",
    "\n",
    "# fit random forest classifier on the training set\n",
    "rfc.fit(X_res, y_res);\n",
    "# extract important features\n",
    "score = np.round(rfc.feature_importances_,3)\n",
    "importances = pd.DataFrame({'feature':refclasscol,'importance':score})\n",
    "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
    "# plot importances\n",
    "plt.rcParams['figure.figsize'] = (11, 4)\n",
    "importances.plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "import itertools\n",
    "\n",
    "# create the RFE model and select 10 attributes\n",
    "rfe = RFE(rfc, n_features_to_select=10)\n",
    "rfe = rfe.fit(X_res, y_res)\n",
    "\n",
    "# summarize the selection of the attributes\n",
    "feature_map = [(i, v) for i, v in itertools.zip_longest(rfe.get_support(), refclasscol)]\n",
    "selected_features = [v for i, v in feature_map if i==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['src_bytes',\n",
       " 'dst_bytes',\n",
       " 'logged_in',\n",
       " 'root_shell',\n",
       " 'serror_rate',\n",
       " 'srv_serror_rate',\n",
       " 'dst_host_srv_count',\n",
       " 'dst_host_serror_rate',\n",
       " 'dst_host_srv_serror_rate',\n",
       " 'service']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22544, 41)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define columns to new dataframe\n",
    "newcol = list(refclasscol)\n",
    "newcol.append('attack_class')\n",
    "\n",
    "# add a dimension to target\n",
    "new_y_res = y_res[:, np.newaxis]\n",
    "\n",
    "# create a dataframe from sampled data\n",
    "res_arr = np.concatenate((X_res, new_y_res), axis=1)\n",
    "res_df = pd.DataFrame(res_arr, columns = newcol) \n",
    "\n",
    "# create test dataframe\n",
    "reftest = pd.concat([sc_testdf, testcat], axis=1)\n",
    "reftest['attack_class'] = reftest['attack_class'].astype(np.float64)\n",
    "reftest['protocol_type'] = reftest['protocol_type'].astype(np.float64)\n",
    "reftest['flag'] = reftest['flag'].astype(np.float64)\n",
    "reftest['service'] = reftest['service'].astype(np.float64)\n",
    "\n",
    "res_df.shape\n",
    "reftest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "classdict = defaultdict(list)\n",
    "\n",
    "# create two-target classes (normal class and an attack class)  \n",
    "attacklist = [('DoS', 0.0), ('Probe', 2.0), ('R2L', 3.0), ('U2R', 4.0)]\n",
    "normalclass = [('Normal', 1.0)]\n",
    "\n",
    "def create_classdict():\n",
    "    '''This function subdivides train and test dataset into two-class attack labels''' \n",
    "    for j, k in normalclass: \n",
    "        for i, v in attacklist: \n",
    "            restrain_set = res_df.loc[(res_df['attack_class'] == k) | (res_df['attack_class'] == v)]\n",
    "            classdict[j +'_' + i].append(restrain_set)\n",
    "            # test labels\n",
    "            reftest_set = reftest.loc[(reftest['attack_class'] == k) | (reftest['attack_class'] == v)]\n",
    "            classdict[j +'_' + i].append(reftest_set)\n",
    "        \n",
    "create_classdict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal_DoS\n",
      "Normal_Probe\n",
      "Normal_R2L\n",
      "Normal_U2R\n"
     ]
    }
   ],
   "source": [
    "for k, v in classdict.items():\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrain = classdict['Normal_DoS'][0]\n",
    "pretest = classdict['Normal_DoS'][1]\n",
    "grpclass = 'Normal_DoS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc = OneHotEncoder(handle_unknown='ignore')\n",
    "\n",
    "Xresdf = pretrain \n",
    "newtest = pretest\n",
    "\n",
    "Xresdfnew = Xresdf[selected_features]\n",
    "Xresdfnum = Xresdfnew.drop(['service'], axis=1)\n",
    "Xresdfcat = Xresdfnew[['service']].copy()\n",
    "\n",
    "Xtest_features = newtest[selected_features]\n",
    "Xtestdfnum = Xtest_features.drop(['service'], axis=1)\n",
    "Xtestcat = Xtest_features[['service']].copy()\n",
    "\n",
    "\n",
    "# Fit train data\n",
    "enc.fit(Xresdfcat)\n",
    "\n",
    "# Transform train data\n",
    "X_train_1hotenc = enc.transform(Xresdfcat).toarray()\n",
    "       \n",
    "# Transform test data\n",
    "X_test_1hotenc = enc.transform(Xtestcat).toarray()\n",
    "\n",
    "X_train = np.concatenate((Xresdfnum.values, X_train_1hotenc), axis=1)\n",
    "X_test = np.concatenate((Xtestdfnum.values, X_test_1hotenc), axis=1) \n",
    "\n",
    "y_train = Xresdf[['attack_class']].copy()\n",
    "c, r = y_train.values.shape\n",
    "Y_train = y_train.values.reshape(c,)\n",
    "\n",
    "y_test = newtest[['attack_class']].copy()\n",
    "c, r = y_test.values.shape\n",
    "Y_test = y_test.values.reshape(c,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC \n",
    "from sklearn.naive_bayes import BernoulliNB \n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Train KNeighborsClassifier Model\n",
    "KNN_Classifier = KNeighborsClassifier(n_jobs=-1)\n",
    "KNN_Classifier.fit(X_train, Y_train); \n",
    "\n",
    "# Train LogisticRegression Model\n",
    "LGR_Classifier = LogisticRegression(n_jobs=-1, random_state=0)\n",
    "LGR_Classifier.fit(X_train, Y_train);\n",
    "\n",
    "# Train Gaussian Naive Baye Model\n",
    "BNB_Classifier = BernoulliNB()\n",
    "BNB_Classifier.fit(X_train, Y_train)\n",
    "            \n",
    "# Train Decision Tree Model\n",
    "DTC_Classifier = tree.DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
    "DTC_Classifier.fit(X_train, Y_train);\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================== Normal_DoS Naive Baye Classifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.973776072139\n",
      "\n",
      "Model Accuracy:\n",
      " 0.973768617377\n",
      "\n",
      "Confusion matrix:\n",
      " [[65346  1997]\n",
      " [ 1536 65807]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       0.98     0.97      0.97    67343\n",
      "        1.0       0.97     0.98      0.97    67343\n",
      "\n",
      "avg / total       0.97     0.97      0.97   134686\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS Decision Tree Classifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.999769836897\n",
      "\n",
      "Model Accuracy:\n",
      " 0.999948027263\n",
      "\n",
      "Confusion matrix:\n",
      " [[67343     0]\n",
      " [    7 67336]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       1.00     1.00      1.00    67343\n",
      "        1.0       1.00     1.00      1.00    67343\n",
      "\n",
      "avg / total       1.00     1.00      1.00   134686\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS KNeighborsClassifier Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.996569816347\n",
      "\n",
      "Model Accuracy:\n",
      " 0.99775774765\n",
      "\n",
      "Confusion matrix:\n",
      " [[67287    56]\n",
      " [  246 67097]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       1.00     1.00      1.00    67343\n",
      "        1.0       1.00     1.00      1.00    67343\n",
      "\n",
      "avg / total       1.00     1.00      1.00   134686\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS LogisticRegression Model Evaluation ==============================\n",
      "\n",
      "Cross Validation Mean Score:\n",
      " 0.980822083372\n",
      "\n",
      "Model Accuracy:\n",
      " 0.980777512139\n",
      "\n",
      "Confusion matrix:\n",
      " [[65527  1816]\n",
      " [  773 66570]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       0.99     0.97      0.98    67343\n",
      "        1.0       0.97     0.99      0.98    67343\n",
      "\n",
      "avg / total       0.98     0.98      0.98   134686\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "models = []\n",
    "\n",
    "models.append(('Naive Baye Classifier', BNB_Classifier))\n",
    "models.append(('Decision Tree Classifier', DTC_Classifier))\n",
    "models.append(('KNeighborsClassifier', KNN_Classifier))\n",
    "models.append(('LogisticRegression', LGR_Classifier))\n",
    "\n",
    "for i, v in models:\n",
    "    scores = cross_val_score(v, X_train, Y_train, cv=10)\n",
    "    accuracy = metrics.accuracy_score(Y_train, v.predict(X_train))\n",
    "    confusion_matrix = metrics.confusion_matrix(Y_train, v.predict(X_train))\n",
    "    classification = metrics.classification_report(Y_train, v.predict(X_train))\n",
    "    print()\n",
    "    print('============================== {} {} Model Evaluation =============================='.format(grpclass, i))\n",
    "    print()\n",
    "    print (\"Cross Validation Mean Score:\" \"\\n\", scores.mean())\n",
    "    print()\n",
    "    print (\"Model Accuracy:\" \"\\n\", accuracy)\n",
    "    print()\n",
    "    print(\"Confusion matrix:\" \"\\n\", confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification report:\" \"\\n\", classification) \n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================== Normal_DoS Naive Baye Classifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.833653678141\n",
      "\n",
      "Confusion matrix:\n",
      " [[5487 1971]\n",
      " [ 885 8826]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       0.86     0.74      0.79     7458\n",
      "        1.0       0.82     0.91      0.86     9711\n",
      "\n",
      "avg / total       0.84     0.83      0.83    17169\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS Decision Tree Classifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.816588036578\n",
      "\n",
      "Confusion matrix:\n",
      " [[5591 1867]\n",
      " [1282 8429]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       0.81     0.75      0.78     7458\n",
      "        1.0       0.82     0.87      0.84     9711\n",
      "\n",
      "avg / total       0.82     0.82      0.82    17169\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS KNeighborsClassifier Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.866620071058\n",
      "\n",
      "Confusion matrix:\n",
      " [[5787 1671]\n",
      " [ 619 9092]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       0.90     0.78      0.83     7458\n",
      "        1.0       0.84     0.94      0.89     9711\n",
      "\n",
      "avg / total       0.87     0.87      0.86    17169\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "============================== Normal_DoS LogisticRegression Model Test Results ==============================\n",
      "\n",
      "Model Accuracy:\n",
      " 0.842157376667\n",
      "\n",
      "Confusion matrix:\n",
      " [[5963 1495]\n",
      " [1215 8496]]\n",
      "\n",
      "Classification report:\n",
      "              precision   recall  f1-score  support\n",
      "\n",
      "        0.0       0.83     0.80      0.81     7458\n",
      "        1.0       0.85     0.87      0.86     9711\n",
      "\n",
      "avg / total       0.84     0.84      0.84    17169\n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, v in models:\n",
    "    accuracy = metrics.accuracy_score(Y_test, v.predict(X_test))\n",
    "    confusion_matrix = metrics.confusion_matrix(Y_test, v.predict(X_test))\n",
    "    classification = metrics.classification_report(Y_test, v.predict(X_test))\n",
    "    print()\n",
    "    print('============================== {} {} Model Test Results =============================='.format(grpclass, i))\n",
    "    print()\n",
    "    print (\"Model Accuracy:\" \"\\n\", accuracy)\n",
    "    print()\n",
    "    print(\"Confusion matrix:\" \"\\n\", confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification report:\" \"\\n\", classification) \n",
    "    print()"
   ]
  }
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