Final reviw 3 Code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('merged_5s (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source IP</th>\n",
       "      <th>Source Port</th>\n",
       "      <th>Destination IP</th>\n",
       "      <th>Destination Port</th>\n",
       "      <th>Protocol</th>\n",
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       "      <th>Flow IAT Mean</th>\n",
       "      <th>Flow IAT Std</th>\n",
       "      <th>...</th>\n",
       "      <th>Bwd IAT Min</th>\n",
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       "      <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>10.0.2.15</td>\n",
       "      <td>53913</td>\n",
       "      <td>216.58.208.46</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4597.701149</td>\n",
       "      <td>435.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>nonTOR</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0.2.16</td>\n",
       "      <td>53913</td>\n",
       "      <td>216.58.208.46</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>259</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7722.007722</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>nonTOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0.2.17</td>\n",
       "      <td>53913</td>\n",
       "      <td>216.58.208.46</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>891</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2244.668911</td>\n",
       "      <td>891.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>nonTOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0.2.18</td>\n",
       "      <td>53913</td>\n",
       "      <td>216.58.208.46</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>1074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1862.197393</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>nonTOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.0.2.19</td>\n",
       "      <td>53913</td>\n",
       "      <td>216.58.208.46</td>\n",
       "      <td>80</td>\n",
       "      <td>6</td>\n",
       "      <td>315</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6349.206349</td>\n",
       "      <td>315.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>nonTOR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source IP  Source Port Destination IP  Destination Port  Protocol  \\\n",
       "0  10.0.2.15        53913  216.58.208.46                80         6   \n",
       "1  10.0.2.16        53913  216.58.208.46                80         6   \n",
       "2  10.0.2.17        53913  216.58.208.46                80         6   \n",
       "3  10.0.2.18        53913  216.58.208.46                80         6   \n",
       "4  10.0.2.19        53913  216.58.208.46                80         6   \n",
       "\n",
       "   Flow Duration  Flow Bytes/s  Flow Packets/s  Flow IAT Mean  Flow IAT Std  \\\n",
       "0            435           0.0     4597.701149          435.0           0.0   \n",
       "1            259           0.0     7722.007722          259.0           0.0   \n",
       "2            891           0.0     2244.668911          891.0           0.0   \n",
       "3           1074           0.0     1862.197393         1074.0           0.0   \n",
       "4            315           0.0     6349.206349          315.0           0.0   \n",
       "\n",
       "   ...  Bwd IAT Min  Active Mean  Active Std  Active Max  Active Min  \\\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",
       "   Idle Mean  Idle Std  Idle Max  Idle Min   label  \n",
       "0          0         0         0         0  nonTOR  \n",
       "1          0         0         0         0  nonTOR  \n",
       "2          0         0         0         0  nonTOR  \n",
       "3          0         0         0         0  nonTOR  \n",
       "4          0         0         0         0  nonTOR  \n",
       "\n",
       "[5 rows x 29 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>84185</th>\n",
       "      <td>131.202.240.64</td>\n",
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       "      <td>131.202.240.157</td>\n",
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      "text/plain": [
       "             Source IP  Source Port   Destination IP  Destination Port  \\\n",
       "84182   131.202.240.64          138  131.202.243.255               138   \n",
       "84183   131.202.240.64          138  131.202.243.255               138   \n",
       "84184   131.202.240.64          138  131.202.243.255               138   \n",
       "84185   131.202.240.64          138  131.202.243.255               138   \n",
       "84186  131.202.240.157           68  255.255.255.255                67   \n",
       "\n",
       "       Protocol  Flow Duration  Flow Bytes/s  Flow Packets/s  Flow IAT Mean  \\\n",
       "84182        17              1   475000000.0       2000000.0            1.0   \n",
       "84183        17              1   475000000.0       2000000.0            1.0   \n",
       "84184        17              1   475000000.0       2000000.0            1.0   \n",
       "84185        17              1   475000000.0       2000000.0            1.0   \n",
       "84186        17              1   600000000.0       2000000.0            1.0   \n",
       "\n",
       "       Flow IAT Std  ...  Bwd IAT Min  Active Mean  Active Std  Active Max  \\\n",
       "84182           0.0  ...            0            0           0           0   \n",
       "84183           0.0  ...            0            0           0           0   \n",
       "84184           0.0  ...            0            0           0           0   \n",
       "84185           0.0  ...            0            0           0           0   \n",
       "84186           0.0  ...            0            0           0           0   \n",
       "\n",
       "       Active Min  Idle Mean  Idle Std  Idle Max  Idle Min   label  \n",
       "84182           0          0         0         0         0  nonTOR  \n",
       "84183           0          0         0         0         0  nonTOR  \n",
       "84184           0          0         0         0         0  nonTOR  \n",
       "84185           0          0         0         0         0  nonTOR  \n",
       "84186           0          0         0         0         0  nonTOR  \n",
       "\n",
       "[5 rows x 29 columns]"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>Source Port</th>\n",
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       "      <th>Protocol</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>84187.000000</td>\n",
       "      <td>84187.000000</td>\n",
       "      <td>84187.000000</td>\n",
       "      <td>8.418700e+04</td>\n",
       "      <td>8.418700e+04</td>\n",
       "      <td>8.418700e+04</td>\n",
       "      <td>8.418700e+04</td>\n",
       "      <td>8.418700e+04</td>\n",
       "      <td>8.418700e+04</td>\n",
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       "      <td>8.418700e+04</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36550.228301</td>\n",
       "      <td>13160.176868</td>\n",
       "      <td>11.709254</td>\n",
       "      <td>1.976607e+06</td>\n",
       "      <td>2.693915e+05</td>\n",
       "      <td>2.767573e+03</td>\n",
       "      <td>2.346052e+05</td>\n",
       "      <td>1.060376e+05</td>\n",
       "      <td>5.085978e+05</td>\n",
       "      <td>1.692820e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.699241e+05</td>\n",
       "      <td>3.730559e+04</td>\n",
       "      <td>1.879872e+04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.879872e+04</td>\n",
       "      <td>1.879872e+04</td>\n",
       "      <td>1.585344e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.585344e+05</td>\n",
       "      <td>1.585344e+05</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21514.235930</td>\n",
       "      <td>19548.441624</td>\n",
       "      <td>5.496051</td>\n",
       "      <td>2.143445e+06</td>\n",
       "      <td>5.147994e+06</td>\n",
       "      <td>4.374423e+04</td>\n",
       "      <td>5.300590e+05</td>\n",
       "      <td>2.847779e+05</td>\n",
       "      <td>8.607961e+05</td>\n",
       "      <td>5.012896e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>6.769899e+05</td>\n",
       "      <td>2.461099e+05</td>\n",
       "      <td>1.437125e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.437125e+05</td>\n",
       "      <td>1.437125e+05</td>\n",
       "      <td>7.604078e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.604078e+05</td>\n",
       "      <td>7.604078e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.000000e-01</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-2.255000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.270000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15685.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.111290e+05</td>\n",
       "      <td>1.068137e+02</td>\n",
       "      <td>4.874708e+00</td>\n",
       "      <td>9.874521e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.543650e+04</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42382.000000</td>\n",
       "      <td>443.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.207650e+05</td>\n",
       "      <td>1.951995e+03</td>\n",
       "      <td>2.963630e+01</td>\n",
       "      <td>4.654067e+04</td>\n",
       "      <td>3.955367e+03</td>\n",
       "      <td>1.557300e+05</td>\n",
       "      <td>4.940000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54420.000000</td>\n",
       "      <td>19302.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.838640e+06</td>\n",
       "      <td>2.595854e+04</td>\n",
       "      <td>1.039026e+02</td>\n",
       "      <td>3.336732e+05</td>\n",
       "      <td>4.639167e+04</td>\n",
       "      <td>4.427965e+05</td>\n",
       "      <td>1.358960e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.216835e+05</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65534.000000</td>\n",
       "      <td>65514.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>6.000000e+08</td>\n",
       "      <td>3.000000e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>3.535258e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>4.998709e+06</td>\n",
       "      <td>4.998709e+06</td>\n",
       "      <td>2.447136e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.447136e+06</td>\n",
       "      <td>2.447136e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>5.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source Port  Destination Port      Protocol  Flow Duration  \\\n",
       "count  84187.000000      84187.000000  84187.000000   8.418700e+04   \n",
       "mean   36550.228301      13160.176868     11.709254   1.976607e+06   \n",
       "std    21514.235930      19548.441624      5.496051   2.143445e+06   \n",
       "min       21.000000         21.000000      6.000000   1.000000e+00   \n",
       "25%    15685.000000        137.000000      6.000000   1.111290e+05   \n",
       "50%    42382.000000        443.000000     17.000000   4.207650e+05   \n",
       "75%    54420.000000      19302.000000     17.000000   4.838640e+06   \n",
       "max    65534.000000      65514.000000     17.000000   5.000000e+06   \n",
       "\n",
       "       Flow Bytes/s  Flow Packets/s  Flow IAT Mean  Flow IAT Std  \\\n",
       "count  8.418700e+04    8.418700e+04   8.418700e+04  8.418700e+04   \n",
       "mean   2.693915e+05    2.767573e+03   2.346052e+05  1.060376e+05   \n",
       "std    5.147994e+06    4.374423e+04   5.300590e+05  2.847779e+05   \n",
       "min    0.000000e+00    4.000000e-01   5.000000e-01  0.000000e+00   \n",
       "25%    1.068137e+02    4.874708e+00   9.874521e+03  0.000000e+00   \n",
       "50%    1.951995e+03    2.963630e+01   4.654067e+04  3.955367e+03   \n",
       "75%    2.595854e+04    1.039026e+02   3.336732e+05  4.639167e+04   \n",
       "max    6.000000e+08    3.000000e+06   5.000000e+06  3.535258e+06   \n",
       "\n",
       "       Flow IAT Max  Flow IAT Min  ...   Bwd IAT Max   Bwd IAT Min  \\\n",
       "count  8.418700e+04  8.418700e+04  ...  8.418700e+04  8.418700e+04   \n",
       "mean   5.085978e+05  1.692820e+05  ...  2.699241e+05  3.730559e+04   \n",
       "std    8.607961e+05  5.012896e+05  ...  6.769899e+05  2.461099e+05   \n",
       "min    1.000000e+00 -2.255000e+03  ...  0.000000e+00 -3.270000e+02   \n",
       "25%    3.543650e+04  1.900000e+01  ...  0.000000e+00  0.000000e+00   \n",
       "50%    1.557300e+05  4.940000e+02  ...  0.000000e+00  0.000000e+00   \n",
       "75%    4.427965e+05  1.358960e+05  ...  1.216835e+05  4.400000e+01   \n",
       "max    5.000000e+06  5.000000e+06  ...  4.998709e+06  4.998709e+06   \n",
       "\n",
       "        Active Mean  Active Std    Active Max    Active Min     Idle Mean  \\\n",
       "count  8.418700e+04     84187.0  8.418700e+04  8.418700e+04  8.418700e+04   \n",
       "mean   1.879872e+04         0.0  1.879872e+04  1.879872e+04  1.585344e+05   \n",
       "std    1.437125e+05         0.0  1.437125e+05  1.437125e+05  7.604078e+05   \n",
       "min    0.000000e+00         0.0  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    0.000000e+00         0.0  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%    0.000000e+00         0.0  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    0.000000e+00         0.0  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "max    2.447136e+06         0.0  2.447136e+06  2.447136e+06  5.000000e+06   \n",
       "\n",
       "       Idle Std      Idle Max      Idle Min  \n",
       "count   84187.0  8.418700e+04  8.418700e+04  \n",
       "mean        0.0  1.585344e+05  1.585344e+05  \n",
       "std         0.0  7.604078e+05  7.604078e+05  \n",
       "min         0.0  0.000000e+00  0.000000e+00  \n",
       "25%         0.0  0.000000e+00  0.000000e+00  \n",
       "50%         0.0  0.000000e+00  0.000000e+00  \n",
       "75%         0.0  0.000000e+00  0.000000e+00  \n",
       "max         0.0  5.000000e+06  5.000000e+06  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source IP            object\n",
       "Source Port           int64\n",
       "Destination IP       object\n",
       "Destination Port      int64\n",
       "Protocol              int64\n",
       "Flow Duration         int64\n",
       "Flow Bytes/s        float64\n",
       "Flow Packets/s      float64\n",
       "Flow IAT Mean       float64\n",
       "Flow IAT Std        float64\n",
       "Flow IAT Max          int64\n",
       "Flow IAT Min          int64\n",
       "Fwd IAT Mean        float64\n",
       "Fwd IAT Std         float64\n",
       "Fwd IAT Max           int64\n",
       "Fwd IAT Min           int64\n",
       "Bwd IAT Mean        float64\n",
       "Bwd IAT Std         float64\n",
       "Bwd IAT Max           int64\n",
       "Bwd IAT Min           int64\n",
       "Active Mean           int64\n",
       "Active Std            int64\n",
       "Active Max            int64\n",
       "Active Min            int64\n",
       "Idle Mean             int64\n",
       "Idle Std              int64\n",
       "Idle Max              int64\n",
       "Idle Min              int64\n",
       "label                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Flow Duration'] = df['Flow Duration'].astype(float)\n",
    "df['Flow IAT Max'] = df['Flow IAT Max'].astype(float)\n",
    "df['Flow IAT Min'] = df['Flow IAT Min'].astype(float)\n",
    "df['Fwd IAT Max'] = df['Fwd IAT Max'].astype(float)\n",
    "df['Fwd IAT Min'] = df['Fwd IAT Min'].astype(float)\n",
    "df['Bwd IAT Max'] = df['Bwd IAT Max'].astype(float)\n",
    "df['Bwd IAT Min'] = df['Bwd IAT Min'].astype(float)\n",
    "df['Active Mean'] = df['Active Mean'].astype(float)\n",
    "df['Active Mean'] = df['Active Mean'].astype(float)\n",
    "df['Active Std'] = df['Active Std'].astype(float)\n",
    "df['Active Max'] = df['Active Max'].astype(float)\n",
    "df['Active Min'] = df['Active Min'].astype(float)\n",
    "df['Idle Mean'] = df['Idle Mean'].astype(float)\n",
    "df['Idle Std'] = df['Idle Std'].astype(float)\n",
    "df['Idle Max'] = df['Idle Max'].astype(float)\n",
    "df['Idle Min'] = df['Idle Min'].astype(float)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Source IP            object\n",
       "Source Port           int64\n",
       "Destination IP       object\n",
       "Destination Port      int64\n",
       "Protocol              int64\n",
       "Flow Duration       float64\n",
       "Flow Bytes/s        float64\n",
       "Flow Packets/s      float64\n",
       "Flow IAT Mean       float64\n",
       "Flow IAT Std        float64\n",
       "Flow IAT Max        float64\n",
       "Flow IAT Min        float64\n",
       "Fwd IAT Mean        float64\n",
       "Fwd IAT Std         float64\n",
       "Fwd IAT Max         float64\n",
       "Fwd IAT Min         float64\n",
       "Bwd IAT Mean        float64\n",
       "Bwd IAT Std         float64\n",
       "Bwd IAT Max         float64\n",
       "Bwd IAT Min         float64\n",
       "Active Mean         float64\n",
       "Active Std          float64\n",
       "Active Max          float64\n",
       "Active Min          float64\n",
       "Idle Mean           float64\n",
       "Idle Std            float64\n",
       "Idle Max            float64\n",
       "Idle Min            float64\n",
       "label                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Source IP', 'Source Port', 'Destination IP', 'Destination Port',\n",
       "       'Protocol', 'Flow Duration', 'Flow Bytes/s', 'Flow Packets/s',\n",
       "       'Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max', 'Flow IAT Min',\n",
       "       'Fwd IAT Mean', 'Fwd IAT Std', 'Fwd IAT Max', 'Fwd IAT Min',\n",
       "       'Bwd IAT Mean', 'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min',\n",
       "       'Active Mean', 'Active Std', 'Active Max', 'Active Min', 'Idle Mean',\n",
       "       'Idle Std', 'Idle Max', 'Idle Min', 'label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max', 'Flow IAT Min',\n",
    "       'Fwd IAT Mean', 'Fwd IAT Std', 'Fwd IAT Max', 'Fwd IAT Min',\n",
    "       'Bwd IAT Mean', 'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min',\n",
    "       'Active Mean', 'Active Std', 'Active Max', 'Active Min', 'Idle Mean',\n",
    "       'Idle Std', 'Idle Max', 'Idle Min']\n",
    "y = ['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Flow IAT Mean', 'Flow IAT Std', 'Flow IAT Max', 'Flow IAT Min', 'Fwd IAT Mean', 'Fwd IAT Std', 'Fwd IAT Max', 'Fwd IAT Min', 'Bwd IAT Mean', 'Bwd IAT Std', 'Bwd IAT Max', 'Bwd IAT Min', 'Active Mean', 'Active Std', 'Active Max', 'Active Min', 'Idle Mean', 'Idle Std', 'Idle Max', 'Idle Min']\n",
      "['label']\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nonTOR    69680\n",
       "TOR       14507\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling data in x variable\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = df.select_dtypes(include=['float64']).columns\n",
    "df_train = scaler.fit_transform(df.select_dtypes(include=['float64']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_df = pd.DataFrame(df_train,columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding data in y variable\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "catdf = df.select_dtypes(include=['object']).copy()\n",
    "traincatdf = catdf.apply(encoder.fit_transform)\n",
    "\n",
    "#dropping label column from main data set\n",
    "enctrain = traincatdf.drop(['label'],axis=1)\n",
    "\n",
    "#copying the \"label\" column to variable\n",
    "cat_train = traincatdf[['label']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(84187, 25)\n",
      "(84187, 1)\n"
     ]
    }
   ],
   "source": [
    "new_x = pd.concat([sc_df,enctrain], axis=1)\n",
    "new_y = cat_train\n",
    "print(new_x.shape)\n",
    "print(new_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Flow Duration  Flow Bytes/s  Flow Packets/s  Flow IAT Mean  \\\n",
      "0          -0.921966     -0.052330        0.041837      -0.441784   \n",
      "1          -0.922048     -0.052330        0.113260      -0.442116   \n",
      "2          -0.921753     -0.052330       -0.011954      -0.440924   \n",
      "3          -0.921668     -0.052330       -0.020697      -0.440578   \n",
      "4          -0.922022     -0.052330        0.081877      -0.442010   \n",
      "...              ...           ...             ...            ...   \n",
      "84182      -0.922169     92.217163       45.657317      -0.442603   \n",
      "84183      -0.922169     92.217163       45.657317      -0.442603   \n",
      "84184      -0.922169     92.217163       45.657317      -0.442603   \n",
      "84185      -0.922169     92.217163       45.657317      -0.442603   \n",
      "84186      -0.922169    116.498608       45.657317      -0.442603   \n",
      "\n",
      "       Flow IAT Std  Flow IAT Max  Flow IAT Min  Fwd IAT Mean  Fwd IAT Std  \\\n",
      "0         -0.372354     -0.590344     -0.336827     -0.439783    -0.350452   \n",
      "1         -0.372354     -0.590548     -0.337178     -0.439783    -0.350452   \n",
      "2         -0.372354     -0.589814     -0.335918     -0.439783    -0.350452   \n",
      "3         -0.372354     -0.589602     -0.335552     -0.439783    -0.350452   \n",
      "4         -0.372354     -0.590483     -0.337067     -0.439783    -0.350452   \n",
      "...             ...           ...           ...           ...          ...   \n",
      "84182     -0.372354     -0.590848     -0.337693     -0.439781    -0.350452   \n",
      "84183     -0.372354     -0.590848     -0.337693     -0.439781    -0.350452   \n",
      "84184     -0.372354     -0.590848     -0.337693     -0.439781    -0.350452   \n",
      "84185     -0.372354     -0.590848     -0.337693     -0.439781    -0.350452   \n",
      "84186     -0.372354     -0.590848     -0.337693     -0.439781    -0.350452   \n",
      "\n",
      "       Fwd IAT Max  ...  Active Mean  Active Std  Active Max  Active Min  \\\n",
      "0        -0.560855  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "1        -0.560855  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "2        -0.560855  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "3        -0.560855  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "4        -0.560855  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "...            ...  ...          ...         ...         ...         ...   \n",
      "84182    -0.560854  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "84183    -0.560854  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "84184    -0.560854  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "84185    -0.560854  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "84186    -0.560854  ...    -0.130809         0.0   -0.130809   -0.130809   \n",
      "\n",
      "       Idle Mean  Idle Std  Idle Max  Idle Min  Source IP  Destination IP  \n",
      "0      -0.208487       0.0 -0.208487 -0.208487          4             869  \n",
      "1      -0.208487       0.0 -0.208487 -0.208487          5             869  \n",
      "2      -0.208487       0.0 -0.208487 -0.208487          6             869  \n",
      "3      -0.208487       0.0 -0.208487 -0.208487          7             869  \n",
      "4      -0.208487       0.0 -0.208487 -0.208487          8             869  \n",
      "...          ...       ...       ...       ...        ...             ...  \n",
      "84182  -0.208487       0.0 -0.208487 -0.208487        131             246  \n",
      "84183  -0.208487       0.0 -0.208487 -0.208487        131             246  \n",
      "84184  -0.208487       0.0 -0.208487 -0.208487        131             246  \n",
      "84185  -0.208487       0.0 -0.208487 -0.208487        131             246  \n",
      "84186  -0.208487       0.0 -0.208487 -0.208487        116            1014  \n",
      "\n",
      "[84187 rows x 25 columns]\n",
      "       label\n",
      "0          1\n",
      "1          1\n",
      "2          1\n",
      "3          1\n",
      "4          1\n",
      "...      ...\n",
      "84182      1\n",
      "84183      1\n",
      "84184      1\n",
      "84185      1\n",
      "84186      1\n",
      "\n",
      "[84187 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(new_x)\n",
    "print(new_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Undersampling the data so that majority class observations match with minority class \n",
    "non_tor = new_y.loc[new_y['label']==1].sample(frac = .75)\n",
    "tor = new_y.loc[new_y['label']==0]\n",
    "normalized_df = pd.concat([non_tor,tor])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       label\n",
      "26776      1\n",
      "32700      1\n",
      "19044      1\n",
      "20751      1\n",
      "37914      1\n",
      "...      ...\n",
      "44013      1\n",
      "41991      1\n",
      "20865      1\n",
      "7208       1\n",
      "7729       1\n",
      "\n",
      "[52260 rows x 1 columns]\n",
      "       label\n",
      "8407       0\n",
      "8408       0\n",
      "8409       0\n",
      "8410       0\n",
      "8411       0\n",
      "...      ...\n",
      "84105      0\n",
      "84111      0\n",
      "84114      0\n",
      "84117      0\n",
      "84122      0\n",
      "\n",
      "[14507 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "print(non_tor)\n",
    "print(tor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.countplot('label', data = normalized_df)\n",
    "plt.title('Balanced Labels')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(new_x, new_y, test_size=0.40, random_state=42, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.395501852035522\n"
     ]
    }
   ],
   "source": [
    "#using logistic regression\n",
    "starttime = time.time()\n",
    "LGR_classifier = LogisticRegression(n_jobs=-1, random_state=0, penalty='l2', solver='newton-cg')\n",
    "LGR_classifier.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87321853 0.87602079 0.8766147  0.87750557 0.87735709 0.87839644\n",
      " 0.87676318 0.88299926 0.88476388 0.88298188]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8732185273159145, 0.8786621310651883, 0.8847638847638848)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(LGR_classifier, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "655.6431016921997\n"
     ]
    }
   ],
   "source": [
    "#SVM with radial based kernel\n",
    "starttime = time.time()\n",
    "SVM_classifier1 = SVC(kernel='rbf', random_state=0, C=0.1, gamma =1)\n",
    "SVM_classifier1.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9827791  0.98144024 0.98485523 0.9830735  0.98262806 0.98426132\n",
      " 0.9857461  0.98396437 0.98440748 0.98292248]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9814402375649591, 0.983607788431625, 0.9857461024498887)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(SVM_classifier1, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.50536727905273\n"
     ]
    }
   ],
   "source": [
    "#SVM with sigmoid kernel\n",
    "starttime = time.time()\n",
    "SVM_classifier2 = SVC(kernel='sigmoid', random_state = 0, C=0.1, gamma=1)\n",
    "SVM_classifier2.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82883017 0.82895323 0.82895323 0.82895323 0.82895323 0.82895323\n",
      " 0.82895323 0.82895323 0.82892783 0.82892783]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8288301662707839, 0.8289358429917086, 0.8289532293986637)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(SVM_classifier2, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Logistic regression classifier Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.86913140311804\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 4116  1733]\n",
      " [ 2674 25152]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.70      0.65      5849\n",
      "           1       0.94      0.90      0.92     27826\n",
      "\n",
      "    accuracy                           0.87     33675\n",
      "   macro avg       0.77      0.80      0.79     33675\n",
      "weighted avg       0.88      0.87      0.87     33675\n",
      "\n",
      "\n",
      "------------ Support Vector Machine(Radial) Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.9994654788418709\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 5831    18]\n",
      " [    0 27826]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      5849\n",
      "           1       1.00      1.00      1.00     27826\n",
      "\n",
      "    accuracy                           1.00     33675\n",
      "   macro avg       1.00      1.00      1.00     33675\n",
      "weighted avg       1.00      1.00      1.00     33675\n",
      "\n",
      "\n",
      "------------ Support Vector Machine(Sigmoidal) Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.9994654788418709\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 5831    18]\n",
      " [    0 27826]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      5849\n",
      "           1       1.00      1.00      1.00     27826\n",
      "\n",
      "    accuracy                           1.00     33675\n",
      "   macro avg       1.00      1.00      1.00     33675\n",
      "weighted avg       1.00      1.00      1.00     33675\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "models = []\n",
    "models.append(('Logistic regression classifier', LGR_classifier))\n",
    "models.append(('Support Vector Machine(Radial)', SVM_classifier1))\n",
    "models.append(('Support Vector Machine(Sigmoidal)', SVM_classifier1)) \n",
    "\n",
    "\n",
    "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",
    "    \n",
    "    print('------------ {} Model Evaluation -----------'.format(i))\n",
    "    print()\n",
    "    print(\"Model Accuracy: \")\n",
    "    print(accuracy)\n",
    "    print()\n",
    "    print(\"Confusion Matrix: \")\n",
    "    print(confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification Report: \")\n",
    "    print(classification)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(new_x, new_y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4189887046813965\n"
     ]
    }
   ],
   "source": [
    "#using logistic regression\n",
    "starttime = time.time()\n",
    "LGR_classifier = LogisticRegression(n_jobs=-1, random_state=0)\n",
    "LGR_classifier.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86715304 0.86681371 0.87071598 0.86848804 0.86730019 0.87103343\n",
      " 0.87510606 0.87627291 0.88424983 0.8754243 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8668137088564642, 0.8722557485884718, 0.8842498302783435)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(LGR_classifier, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360.3948538303375\n"
     ]
    }
   ],
   "source": [
    "#SVM with radial based kernel\n",
    "starttime = time.time()\n",
    "SVM_classifier1 = SVC(kernel='rbf', random_state=0, C=1, gamma = 0.1)\n",
    "SVM_classifier1.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99807007 0.99836674 0.99910913 0.9986637  0.99806978 0.99910913\n",
      " 0.99896065 0.99836674 0.9992575  0.9986635 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9980697847067558, 0.9986636948912645, 0.9992574992574993)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(SVM_classifier1, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68.96492147445679\n"
     ]
    }
   ],
   "source": [
    "#SVM with sigmoid kernel\n",
    "starttime = time.time()\n",
    "SVM_classifier2 = SVC(kernel='sigmoid', random_state = 0, C=1, gamma = 0.1)\n",
    "SVM_classifier2.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.82883017 0.82895323 0.82895323 0.82895323 0.82895323 0.82895323\n",
      " 0.82895323 0.82895323 0.82892783 0.82892783]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8288301662707839, 0.8289358429917086, 0.8289532293986637)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(SVM_classifier2, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Logistic regression classifier Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.8671655382666191\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 3025  1385]\n",
      " [ 1970 18877]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.69      0.64      4410\n",
      "           1       0.93      0.91      0.92     20847\n",
      "\n",
      "    accuracy                           0.87     25257\n",
      "   macro avg       0.77      0.80      0.78     25257\n",
      "weighted avg       0.87      0.87      0.87     25257\n",
      "\n",
      "\n",
      "------------ Support Vector Machine(Radial) Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.9996832561270143\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 4402     8]\n",
      " [    0 20847]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      4410\n",
      "           1       1.00      1.00      1.00     20847\n",
      "\n",
      "    accuracy                           1.00     25257\n",
      "   macro avg       1.00      1.00      1.00     25257\n",
      "weighted avg       1.00      1.00      1.00     25257\n",
      "\n",
      "\n",
      "------------ Support Vector Machine(Sigmoidal) Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.9996832561270143\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 4402     8]\n",
      " [    0 20847]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      4410\n",
      "           1       1.00      1.00      1.00     20847\n",
      "\n",
      "    accuracy                           1.00     25257\n",
      "   macro avg       1.00      1.00      1.00     25257\n",
      "weighted avg       1.00      1.00      1.00     25257\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "models = []\n",
    "models.append(('Logistic regression classifier', LGR_classifier))\n",
    "models.append(('Support Vector Machine(Radial)', SVM_classifier1))\n",
    "models.append(('Support Vector Machine(Sigmoidal)', SVM_classifier1)) \n",
    "\n",
    "\n",
    "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",
    "    \n",
    "    print('------------ {} Model Evaluation -----------'.format(i))\n",
    "    print()\n",
    "    print(\"Model Accuracy: \")\n",
    "    print(accuracy)\n",
    "    print()\n",
    "    print(\"Confusion Matrix: \")\n",
    "    print(confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification Report: \")\n",
    "    print(classification)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(new_x, new_y, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7577550411224365\n"
     ]
    }
   ],
   "source": [
    "#using logistic regression\n",
    "starttime = time.time()\n",
    "LGR_classifier = LogisticRegression(n_jobs=-1, random_state=0)\n",
    "LGR_classifier.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.86668646 0.86740906 0.87290275 0.86859688 0.86740906 0.87156644\n",
      " 0.87126949 0.87706013 0.88030888 0.87436887]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.8666864608076009, 0.8717578023948607, 0.8803088803088803)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(LGR_classifier, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "199.19418787956238\n"
     ]
    }
   ],
   "source": [
    "#SVM with radial based kernel\n",
    "starttime = time.time()\n",
    "SVM_classifier1 = SVC(kernel='rbf', random_state=0, C=10, gamma=0.01)\n",
    "SVM_classifier1.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.99955463 0.99970304 0.99970304 0.99970304 0.99970304 1.\n",
      " 1.         0.99970304 0.999703   0.9998515 ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9995546318289786, 0.9997624350388676, 1.0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(SVM_classifier1, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.24199056625366\n"
     ]
    }
   ],
   "source": [
    "#SVM with sigmoid kernel\n",
    "starttime = time.time()\n",
    "SVM_classifier2 = SVC(kernel='sigmoid', random_state = 0,C=10, gamma=0.01)\n",
    "SVM_classifier2.fit(x_train,y_train)\n",
    "endtime = time.time()\n",
    "training_time = endtime-starttime\n",
    "print(training_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.80893705 0.8095026  0.80861173 0.81098738 0.80727543 0.80786934\n",
      " 0.81158129 0.81202673 0.80694981 0.80813781]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.806949806949807, 0.8091879161181954, 0.8120267260579065)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(SVM_classifier2, x_train, y_train, cv=10)\n",
    "print(scores)\n",
    "answers = pd.Series(scores)\n",
    "scores.min(), scores.mean(), scores.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------ Logistic regression classifier Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.8697588787266897\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 2077   909]\n",
      " [ 1284 12568]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.70      0.65      2986\n",
      "           1       0.93      0.91      0.92     13852\n",
      "\n",
      "    accuracy                           0.87     16838\n",
      "   macro avg       0.78      0.80      0.79     16838\n",
      "weighted avg       0.88      0.87      0.87     16838\n",
      "\n",
      "\n",
      "------------ Support Vector Machine(Radial) Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.9998812210476303\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 2984     2]\n",
      " [    0 13852]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      2986\n",
      "           1       1.00      1.00      1.00     13852\n",
      "\n",
      "    accuracy                           1.00     16838\n",
      "   macro avg       1.00      1.00      1.00     16838\n",
      "weighted avg       1.00      1.00      1.00     16838\n",
      "\n",
      "\n",
      "------------ Support Vector Machine(Sigmoidal) Model Evaluation -----------\n",
      "\n",
      "Model Accuracy: \n",
      "0.9998812210476303\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 2984     2]\n",
      " [    0 13852]]\n",
      "\n",
      "Classification Report: \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      2986\n",
      "           1       1.00      1.00      1.00     13852\n",
      "\n",
      "    accuracy                           1.00     16838\n",
      "   macro avg       1.00      1.00      1.00     16838\n",
      "weighted avg       1.00      1.00      1.00     16838\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "models = []\n",
    "models.append(('Logistic regression classifier', LGR_classifier))\n",
    "models.append(('Support Vector Machine(Radial)', SVM_classifier1))\n",
    "models.append(('Support Vector Machine(Sigmoidal)', SVM_classifier1)) \n",
    "\n",
    "\n",
    "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",
    "    \n",
    "    print('------------ {} Model Evaluation -----------'.format(i))\n",
    "    print()\n",
    "    print(\"Model Accuracy: \")\n",
    "    print(accuracy)\n",
    "    print()\n",
    "    print(\"Confusion Matrix: \")\n",
    "    print(confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification Report: \")\n",
    "    print(classification)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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