Phishing model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a589b1e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0740a5b9",
   "metadata": {
    "scrolled": true
   },
   "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>key</th>\n",
       "      <th>having_IP</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>Shortining_Service</th>\n",
       "      <th>having_At_Symbol</th>\n",
       "      <th>double_slash_redirecting</th>\n",
       "      <th>Prefix_Suffix</th>\n",
       "      <th>having_Sub_Domain</th>\n",
       "      <th>SSLfinal_State</th>\n",
       "      <th>Domain_registeration_length</th>\n",
       "      <th>...</th>\n",
       "      <th>popUpWidnow</th>\n",
       "      <th>Iframe</th>\n",
       "      <th>age_of_domain</th>\n",
       "      <th>DNSRecord</th>\n",
       "      <th>web_traffic</th>\n",
       "      <th>Page_Rank</th>\n",
       "      <th>Google_Index</th>\n",
       "      <th>Links_pointing_to_page</th>\n",
       "      <th>Statistical_report</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12344</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12345</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12346</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12347</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12348</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     key  having_IP  URL_Length  Shortining_Service  having_At_Symbol  \\\n",
       "0  12344         -1           1                   1                 1   \n",
       "1  12345          1           1                   1                 1   \n",
       "2  12346          1           0                   1                 1   \n",
       "3  12347          1           0                   1                 1   \n",
       "4  12348          1           0                  -1                 1   \n",
       "\n",
       "   double_slash_redirecting  Prefix_Suffix  having_Sub_Domain  SSLfinal_State  \\\n",
       "0                        -1             -1                 -1              -1   \n",
       "1                         1             -1                  0               1   \n",
       "2                         1             -1                 -1              -1   \n",
       "3                         1             -1                 -1              -1   \n",
       "4                         1             -1                  1               1   \n",
       "\n",
       "   Domain_registeration_length  ...  popUpWidnow  Iframe  age_of_domain  \\\n",
       "0                           -1  ...            1       1             -1   \n",
       "1                           -1  ...            1       1             -1   \n",
       "2                           -1  ...            1       1              1   \n",
       "3                            1  ...            1       1             -1   \n",
       "4                           -1  ...           -1       1             -1   \n",
       "\n",
       "   DNSRecord  web_traffic  Page_Rank  Google_Index  Links_pointing_to_page  \\\n",
       "0         -1           -1         -1             1                       1   \n",
       "1         -1            0         -1             1                       1   \n",
       "2         -1            1         -1             1                       0   \n",
       "3         -1            1         -1             1                      -1   \n",
       "4         -1            0         -1             1                       1   \n",
       "\n",
       "   Statistical_report  Result  \n",
       "0                  -1      -1  \n",
       "1                   1      -1  \n",
       "2                  -1      -1  \n",
       "3                   1      -1  \n",
       "4                   1       1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "df = pd.read_csv('Phising_Training_Dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca02fadc",
   "metadata": {},
   "source": [
    "## Data information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16a3b0b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data:  (8955, 32)\n",
      "\n",
      "Columns present in the data: \n",
      " Index(['key', 'having_IP', 'URL_Length', 'Shortining_Service',\n",
      "       'having_At_Symbol', 'double_slash_redirecting', 'Prefix_Suffix',\n",
      "       'having_Sub_Domain', 'SSLfinal_State', 'Domain_registeration_length',\n",
      "       'Favicon', 'port', 'HTTPS_token', 'Request_URL', 'URL_of_Anchor',\n",
      "       'Links_in_tags', 'SFH', 'Submitting_to_email', 'Abnormal_URL',\n",
      "       'Redirect', 'on_mouseover', 'RightClick', 'popUpWidnow', 'Iframe',\n",
      "       'age_of_domain', 'DNSRecord', 'web_traffic', 'Page_Rank',\n",
      "       'Google_Index', 'Links_pointing_to_page', 'Statistical_report',\n",
      "       'Result'],\n",
      "      dtype='object')\n",
      "\n",
      "Data informainformation: \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8955 entries, 0 to 8954\n",
      "Data columns (total 32 columns):\n",
      " #   Column                       Non-Null Count  Dtype\n",
      "---  ------                       --------------  -----\n",
      " 0   key                          8955 non-null   int64\n",
      " 1   having_IP                    8955 non-null   int64\n",
      " 2   URL_Length                   8955 non-null   int64\n",
      " 3   Shortining_Service           8955 non-null   int64\n",
      " 4   having_At_Symbol             8955 non-null   int64\n",
      " 5   double_slash_redirecting     8955 non-null   int64\n",
      " 6   Prefix_Suffix                8955 non-null   int64\n",
      " 7   having_Sub_Domain            8955 non-null   int64\n",
      " 8   SSLfinal_State               8955 non-null   int64\n",
      " 9   Domain_registeration_length  8955 non-null   int64\n",
      " 10  Favicon                      8955 non-null   int64\n",
      " 11  port                         8955 non-null   int64\n",
      " 12  HTTPS_token                  8955 non-null   int64\n",
      " 13  Request_URL                  8955 non-null   int64\n",
      " 14  URL_of_Anchor                8955 non-null   int64\n",
      " 15  Links_in_tags                8955 non-null   int64\n",
      " 16  SFH                          8955 non-null   int64\n",
      " 17  Submitting_to_email          8955 non-null   int64\n",
      " 18  Abnormal_URL                 8955 non-null   int64\n",
      " 19  Redirect                     8955 non-null   int64\n",
      " 20  on_mouseover                 8955 non-null   int64\n",
      " 21  RightClick                   8955 non-null   int64\n",
      " 22  popUpWidnow                  8955 non-null   int64\n",
      " 23  Iframe                       8955 non-null   int64\n",
      " 24  age_of_domain                8955 non-null   int64\n",
      " 25  DNSRecord                    8955 non-null   int64\n",
      " 26  web_traffic                  8955 non-null   int64\n",
      " 27  Page_Rank                    8955 non-null   int64\n",
      " 28  Google_Index                 8955 non-null   int64\n",
      " 29  Links_pointing_to_page       8955 non-null   int64\n",
      " 30  Statistical_report           8955 non-null   int64\n",
      " 31  Result                       8955 non-null   int64\n",
      "dtypes: int64(32)\n",
      "memory usage: 2.2 MB\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of data: \",df.shape)\n",
    "print(\"\")\n",
    "print(\"Columns present in the data: \\n\",df.columns)\n",
    "print(\"\")\n",
    "print(\"Data informainformation: \")\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd9ff5f",
   "metadata": {},
   "source": [
    "## Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92af95ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = df.corr()\n",
    "ax = sns.heatmap(\n",
    "    corr, \n",
    "    vmin=-1, vmax=1, center=0,\n",
    "    cmap=sns.diverging_palette(20, 220, n=200),\n",
    "    square=True\n",
    ")\n",
    "ax.set_xticklabels(\n",
    "    ax.get_xticklabels(),\n",
    "    rotation=45,\n",
    "    horizontalalignment='right'\n",
    ");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bba239",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c5bcad2",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>having_IP</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>Shortining_Service</th>\n",
       "      <th>having_At_Symbol</th>\n",
       "      <th>double_slash_redirecting</th>\n",
       "      <th>Prefix_Suffix</th>\n",
       "      <th>having_Sub_Domain</th>\n",
       "      <th>SSLfinal_State</th>\n",
       "      <th>Domain_registeration_length</th>\n",
       "      <th>...</th>\n",
       "      <th>popUpWidnow</th>\n",
       "      <th>Iframe</th>\n",
       "      <th>age_of_domain</th>\n",
       "      <th>DNSRecord</th>\n",
       "      <th>web_traffic</th>\n",
       "      <th>Page_Rank</th>\n",
       "      <th>Google_Index</th>\n",
       "      <th>Links_pointing_to_page</th>\n",
       "      <th>Statistical_report</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.00000</td>\n",
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       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "      <td>8955.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16821.000000</td>\n",
       "      <td>0.307203</td>\n",
       "      <td>-0.635734</td>\n",
       "      <td>0.740480</td>\n",
       "      <td>0.709436</td>\n",
       "      <td>0.740704</td>\n",
       "      <td>-0.735343</td>\n",
       "      <td>0.071803</td>\n",
       "      <td>0.264545</td>\n",
       "      <td>-0.33646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606700</td>\n",
       "      <td>0.829816</td>\n",
       "      <td>0.028922</td>\n",
       "      <td>0.371078</td>\n",
       "      <td>0.291792</td>\n",
       "      <td>-0.479397</td>\n",
       "      <td>0.712339</td>\n",
       "      <td>0.338582</td>\n",
       "      <td>0.728867</td>\n",
       "      <td>0.124288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2585.230164</td>\n",
       "      <td>0.951697</td>\n",
       "      <td>0.763660</td>\n",
       "      <td>0.672116</td>\n",
       "      <td>0.704809</td>\n",
       "      <td>0.671870</td>\n",
       "      <td>0.677733</td>\n",
       "      <td>0.817419</td>\n",
       "      <td>0.908003</td>\n",
       "      <td>0.94175</td>\n",
       "      <td>...</td>\n",
       "      <td>0.794975</td>\n",
       "      <td>0.558069</td>\n",
       "      <td>0.999637</td>\n",
       "      <td>0.928654</td>\n",
       "      <td>0.825557</td>\n",
       "      <td>0.877647</td>\n",
       "      <td>0.701874</td>\n",
       "      <td>0.576068</td>\n",
       "      <td>0.684694</td>\n",
       "      <td>0.992302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12344.000000</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",
       "      <td>-1.00000</td>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14582.500000</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",
       "      <td>-1.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16821.000000</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",
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       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19059.500000</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",
       "      <td>1.00000</td>\n",
       "      <td>...</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21298.000000</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",
       "      <td>1.00000</td>\n",
       "      <td>...</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",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
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      ],
      "text/plain": [
       "                key    having_IP   URL_Length  Shortining_Service  \\\n",
       "count   8955.000000  8955.000000  8955.000000         8955.000000   \n",
       "mean   16821.000000     0.307203    -0.635734            0.740480   \n",
       "std     2585.230164     0.951697     0.763660            0.672116   \n",
       "min    12344.000000    -1.000000    -1.000000           -1.000000   \n",
       "25%    14582.500000    -1.000000    -1.000000            1.000000   \n",
       "50%    16821.000000     1.000000    -1.000000            1.000000   \n",
       "75%    19059.500000     1.000000    -1.000000            1.000000   \n",
       "max    21298.000000     1.000000     1.000000            1.000000   \n",
       "\n",
       "       having_At_Symbol  double_slash_redirecting  Prefix_Suffix  \\\n",
       "count       8955.000000               8955.000000    8955.000000   \n",
       "mean           0.709436                  0.740704      -0.735343   \n",
       "std            0.704809                  0.671870       0.677733   \n",
       "min           -1.000000                 -1.000000      -1.000000   \n",
       "25%            1.000000                  1.000000      -1.000000   \n",
       "50%            1.000000                  1.000000      -1.000000   \n",
       "75%            1.000000                  1.000000      -1.000000   \n",
       "max            1.000000                  1.000000       1.000000   \n",
       "\n",
       "       having_Sub_Domain  SSLfinal_State  Domain_registeration_length  ...  \\\n",
       "count        8955.000000     8955.000000                   8955.00000  ...   \n",
       "mean            0.071803        0.264545                     -0.33646  ...   \n",
       "std             0.817419        0.908003                      0.94175  ...   \n",
       "min            -1.000000       -1.000000                     -1.00000  ...   \n",
       "25%            -1.000000       -1.000000                     -1.00000  ...   \n",
       "50%             0.000000        1.000000                     -1.00000  ...   \n",
       "75%             1.000000        1.000000                      1.00000  ...   \n",
       "max             1.000000        1.000000                      1.00000  ...   \n",
       "\n",
       "       popUpWidnow       Iframe  age_of_domain    DNSRecord  web_traffic  \\\n",
       "count  8955.000000  8955.000000    8955.000000  8955.000000  8955.000000   \n",
       "mean      0.606700     0.829816       0.028922     0.371078     0.291792   \n",
       "std       0.794975     0.558069       0.999637     0.928654     0.825557   \n",
       "min      -1.000000    -1.000000      -1.000000    -1.000000    -1.000000   \n",
       "25%       1.000000     1.000000      -1.000000    -1.000000     0.000000   \n",
       "50%       1.000000     1.000000       1.000000     1.000000     1.000000   \n",
       "75%       1.000000     1.000000       1.000000     1.000000     1.000000   \n",
       "max       1.000000     1.000000       1.000000     1.000000     1.000000   \n",
       "\n",
       "         Page_Rank  Google_Index  Links_pointing_to_page  Statistical_report  \\\n",
       "count  8955.000000   8955.000000             8955.000000         8955.000000   \n",
       "mean     -0.479397      0.712339                0.338582            0.728867   \n",
       "std       0.877647      0.701874                0.576068            0.684694   \n",
       "min      -1.000000     -1.000000               -1.000000           -1.000000   \n",
       "25%      -1.000000      1.000000                0.000000            1.000000   \n",
       "50%      -1.000000      1.000000                0.000000            1.000000   \n",
       "75%       1.000000      1.000000                1.000000            1.000000   \n",
       "max       1.000000      1.000000                1.000000            1.000000   \n",
       "\n",
       "            Result  \n",
       "count  8955.000000  \n",
       "mean      0.124288  \n",
       "std       0.992302  \n",
       "min      -1.000000  \n",
       "25%      -1.000000  \n",
       "50%       1.000000  \n",
       "75%       1.000000  \n",
       "max       1.000000  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03d25ed7",
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   having_IP  URL_Length  Shortining_Service  having_At_Symbol  \\\n",
       "0         -1           1                   1                 1   \n",
       "1          1           1                   1                 1   \n",
       "2          1           0                   1                 1   \n",
       "3          1           0                   1                 1   \n",
       "4          1           0                  -1                 1   \n",
       "\n",
       "   double_slash_redirecting  Prefix_Suffix  having_Sub_Domain  SSLfinal_State  \\\n",
       "0                        -1             -1                 -1              -1   \n",
       "1                         1             -1                  0               1   \n",
       "2                         1             -1                 -1              -1   \n",
       "3                         1             -1                 -1              -1   \n",
       "4                         1             -1                  1               1   \n",
       "\n",
       "   Domain_registeration_length  Favicon  ...  popUpWidnow  Iframe  \\\n",
       "0                           -1        1  ...            1       1   \n",
       "1                           -1        1  ...            1       1   \n",
       "2                           -1        1  ...            1       1   \n",
       "3                            1        1  ...            1       1   \n",
       "4                           -1        1  ...           -1       1   \n",
       "\n",
       "   age_of_domain  DNSRecord  web_traffic  Page_Rank  Google_Index  \\\n",
       "0             -1         -1           -1         -1             1   \n",
       "1             -1         -1            0         -1             1   \n",
       "2              1         -1            1         -1             1   \n",
       "3             -1         -1            1         -1             1   \n",
       "4             -1         -1            0         -1             1   \n",
       "\n",
       "   Links_pointing_to_page  Statistical_report  Result  \n",
       "0                       1                  -1      -1  \n",
       "1                       1                   1      -1  \n",
       "2                       0                  -1      -1  \n",
       "3                      -1                   1      -1  \n",
       "4                       1                   1       1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We dont need the key column for modelling\n",
    "df_train = df.drop(\"key\", axis = 1)\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fb5ed12a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key                            0\n",
       "having_IP                      0\n",
       "URL_Length                     0\n",
       "Shortining_Service             0\n",
       "having_At_Symbol               0\n",
       "double_slash_redirecting       0\n",
       "Prefix_Suffix                  0\n",
       "having_Sub_Domain              0\n",
       "SSLfinal_State                 0\n",
       "Domain_registeration_length    0\n",
       "Favicon                        0\n",
       "port                           0\n",
       "HTTPS_token                    0\n",
       "Request_URL                    0\n",
       "URL_of_Anchor                  0\n",
       "Links_in_tags                  0\n",
       "SFH                            0\n",
       "Submitting_to_email            0\n",
       "Abnormal_URL                   0\n",
       "Redirect                       0\n",
       "on_mouseover                   0\n",
       "RightClick                     0\n",
       "popUpWidnow                    0\n",
       "Iframe                         0\n",
       "age_of_domain                  0\n",
       "DNSRecord                      0\n",
       "web_traffic                    0\n",
       "Page_Rank                      0\n",
       "Google_Index                   0\n",
       "Links_pointing_to_page         0\n",
       "Statistical_report             0\n",
       "Result                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb7c9c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8955, 31), (8955,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Result']\n",
    "X = df.drop('Result',axis=1)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da43993a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5373, 31), (3582, 31))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into train and test sets: 80-20 split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 12)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e953652",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing packages\n",
    "from sklearn.metrics import accuracy_score\n",
    "# Creating holders to store the model performance results\n",
    "ML_Model = []\n",
    "acc_train = []\n",
    "acc_test = []\n",
    "\n",
    "#function to call for storing the results\n",
    "def storeResults(model, a,b):\n",
    "    ML_Model.append(model)\n",
    "    acc_train.append(round(a, 3))\n",
    "    acc_test.append(round(b, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a87c0754",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5cab539d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decision Tree model \n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# instantiate the model \n",
    "tree = DecisionTreeClassifier(max_depth = 5)\n",
    "# fit the model \n",
    "tree.fit(X_train, y_train)\n",
    "#predicting the target value from the model for the samples\n",
    "y_test_tree = tree.predict(X_test)\n",
    "y_train_tree = tree.predict(X_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "281416ec",
   "metadata": {},
   "source": [
    "### Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b74902c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree: Accuracy on training Data: 0.933\n",
      "Decision Tree: Accuracy on test Data: 0.916\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_tree = accuracy_score(y_train,y_train_tree)\n",
    "acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
    "\n",
    "print(\"Decision Tree: Accuracy on training Data: {:.3f}\".format(acc_train_tree))\n",
    "print(\"Decision Tree: Accuracy on test Data: {:.3f}\".format(acc_test_tree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01c2753c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the feature improtance in the model\n",
    "plt.figure(figsize=(9,7))\n",
    "n_features = X_train.shape[1]\n",
    "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), X_train.columns)\n",
    "plt.xlabel(\"Feature importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2adb0ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#storing the results. The below mentioned order of parameter passing is important.\n",
    "#Caution: Execute only once to avoid duplications.\n",
    "storeResults('Decision Tree', acc_train_tree, acc_test_tree)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "075d319f",
   "metadata": {},
   "source": [
    "## Random Forest Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cdeba534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# instantiate the model\n",
    "forest = RandomForestClassifier(max_depth=5)\n",
    "\n",
    "# fit the model \n",
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e65d13fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_forest = forest.predict(X_test)\n",
    "y_train_forest = forest.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "510af107",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random forest: Accuracy on training Data: 0.939\n",
      "Random forest: Accuracy on test Data: 0.923\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_forest = accuracy_score(y_train,y_train_forest)\n",
    "acc_test_forest = accuracy_score(y_test,y_test_forest)\n",
    "\n",
    "print(\"Random forest: Accuracy on training Data: {:.3f}\".format(acc_train_forest))\n",
    "print(\"Random forest: Accuracy on test Data: {:.3f}\".format(acc_test_forest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a1f74c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "storeResults('Random Forest', acc_train_forest, acc_test_forest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4458dd",
   "metadata": {},
   "source": [
    "## Multilayer Perceptrons (MLPs): Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56ba3231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Multilayer Perceptrons model\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# instantiate the model\n",
    "mlp = MLPClassifier(alpha=0.001, hidden_layer_sizes=([100,100,100]))\n",
    "\n",
    "# fit the model \n",
    "mlp.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "04f8d13c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilayer Perceptrons: Accuracy on training Data: 0.435\n",
      "Multilayer Perceptrons: Accuracy on test Data: 0.442\n"
     ]
    }
   ],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_mlp = mlp.predict(X_test)\n",
    "y_train_mlp = mlp.predict(X_train)\n",
    "#computing the accuracy of the model performance\n",
    "acc_train_mlp = accuracy_score(y_train,y_train_mlp)\n",
    "acc_test_mlp = accuracy_score(y_test,y_test_mlp)\n",
    "\n",
    "print(\"Multilayer Perceptrons: Accuracy on training Data: {:.3f}\".format(acc_train_mlp))\n",
    "print(\"Multilayer Perceptrons: Accuracy on test Data: {:.3f}\".format(acc_test_mlp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b2b9d531",
   "metadata": {},
   "outputs": [],
   "source": [
    "storeResults('Multilayer Perceptrons', acc_train_mlp, acc_test_mlp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3eb1b8",
   "metadata": {},
   "source": [
    "## XGBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c2e15645",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>key</th>\n",
       "      <th>having_IP</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>Shortining_Service</th>\n",
       "      <th>having_At_Symbol</th>\n",
       "      <th>double_slash_redirecting</th>\n",
       "      <th>Prefix_Suffix</th>\n",
       "      <th>having_Sub_Domain</th>\n",
       "      <th>SSLfinal_State</th>\n",
       "      <th>Domain_registeration_length</th>\n",
       "      <th>...</th>\n",
       "      <th>popUpWidnow</th>\n",
       "      <th>Iframe</th>\n",
       "      <th>age_of_domain</th>\n",
       "      <th>DNSRecord</th>\n",
       "      <th>web_traffic</th>\n",
       "      <th>Page_Rank</th>\n",
       "      <th>Google_Index</th>\n",
       "      <th>Links_pointing_to_page</th>\n",
       "      <th>Statistical_report</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12344</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12345</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12346</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12347</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12348</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     key  having_IP  URL_Length  Shortining_Service  having_At_Symbol  \\\n",
       "0  12344         -1           1                   1                 1   \n",
       "1  12345          1           1                   1                 1   \n",
       "2  12346          1           0                   1                 1   \n",
       "3  12347          1           0                   1                 1   \n",
       "4  12348          1           0                  -1                 1   \n",
       "\n",
       "   double_slash_redirecting  Prefix_Suffix  having_Sub_Domain  SSLfinal_State  \\\n",
       "0                        -1             -1                 -1              -1   \n",
       "1                         1             -1                  0               1   \n",
       "2                         1             -1                 -1              -1   \n",
       "3                         1             -1                 -1              -1   \n",
       "4                         1             -1                  1               1   \n",
       "\n",
       "   Domain_registeration_length  ...  popUpWidnow  Iframe  age_of_domain  \\\n",
       "0                           -1  ...            1       1             -1   \n",
       "1                           -1  ...            1       1             -1   \n",
       "2                           -1  ...            1       1              1   \n",
       "3                            1  ...            1       1             -1   \n",
       "4                           -1  ...           -1       1             -1   \n",
       "\n",
       "   DNSRecord  web_traffic  Page_Rank  Google_Index  Links_pointing_to_page  \\\n",
       "0         -1           -1         -1             1                       1   \n",
       "1         -1            0         -1             1                       1   \n",
       "2         -1            1         -1             1                       0   \n",
       "3         -1            1         -1             1                      -1   \n",
       "4         -1            0         -1             1                       1   \n",
       "\n",
       "   Statistical_report  Result  \n",
       "0                  -1       0  \n",
       "1                   1       0  \n",
       "2                  -1       0  \n",
       "3                   1       0  \n",
       "4                   1       1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the data\n",
    "df1 = pd.read_csv('Phising_Training_Dataset.csv')\n",
    "df1[\"Result\"].replace({-1: 0, 1: 1}, inplace=True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a79174d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8955, 31), (8955,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df1['Result']\n",
    "X = df1.drop('Result',axis=1)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72d44583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5373, 31), (3582, 31))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into train and test sets: 80-20 split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 12)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f20027d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost: Accuracy on training Data: 1.000\n",
      "XGBoost : Accuracy on test Data: 0.954\n"
     ]
    }
   ],
   "source": [
    "#XGBoost Classification model\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# instantiate the model\n",
    "xgb = XGBClassifier(learning_rate=0.4,max_depth=7)\n",
    "#fit the model\n",
    "xgb.fit(X_train, y_train)\n",
    "#predicting the target value from the model for the samples\n",
    "y_test_xgb = xgb.predict(X_test)\n",
    "y_train_xgb = xgb.predict(X_train)\n",
    "#computing the accuracy of the model performance\n",
    "acc_train_xgb = accuracy_score(y_train,y_train_xgb)\n",
    "acc_test_xgb = accuracy_score(y_test,y_test_xgb)\n",
    "storeResults('XGBoost', acc_train_xgb, acc_test_xgb)\n",
    "print(\"XGBoost: Accuracy on training Data: {:.3f}\".format(acc_train_xgb))\n",
    "print(\"XGBoost : Accuracy on test Data: {:.3f}\".format(acc_test_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5b26ecfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear', random_state=12)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Support vector machine model\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# instantiate the model\n",
    "svm = SVC(kernel='linear', C=1.0, random_state=12)\n",
    "#fit the model\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "687f1a93",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting the target value from the model for the samples\n",
    "y_test_svm = svm.predict(X_test)\n",
    "y_train_svm = svm.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93ca7d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM: Accuracy on training Data: 0.924\n",
      "SVM : Accuracy on test Data: 0.908\n"
     ]
    }
   ],
   "source": [
    "#computing the accuracy of the model performance\n",
    "acc_train_svm = accuracy_score(y_train,y_train_svm)\n",
    "acc_test_svm = accuracy_score(y_test,y_test_svm)\n",
    "storeResults('SVM', acc_train_svm, acc_test_svm)\n",
    "print(\"SVM: Accuracy on training Data: {:.3f}\".format(acc_train_svm))\n",
    "print(\"SVM : Accuracy on test Data: {:.3f}\".format(acc_test_svm))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43b58aac",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "03d5fc12",
   "metadata": {
    "scrolled": true
   },
   "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>ML Model</th>\n",
       "      <th>Train Accuracy</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Multilayer Perceptrons</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ML Model  Train Accuracy  Test Accuracy\n",
       "3                 XGBoost           1.000          0.954\n",
       "1           Random Forest           0.939          0.923\n",
       "0           Decision Tree           0.933          0.916\n",
       "4                     SVM           0.924          0.908\n",
       "2  Multilayer Perceptrons           0.435          0.442"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating dataframe\n",
    "results = pd.DataFrame({ 'ML Model': ML_Model,    \n",
    "    'Train Accuracy': acc_train,\n",
    "    'Test Accuracy': acc_test})\n",
    "#Sorting the datafram on accuracy\n",
    "results.sort_values(by=['Test Accuracy', 'Train Accuracy'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8926929f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save XGBoost model to file\n",
    "import pickle\n",
    "pickle.dump(xgb, open(\"XGBoostClassifier.pickle.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "57bdb99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pd.read_csv(\"Phising_Testing_Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ecb5435b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load model from file\n",
    "model =  pickle.load(open(\"XGBoostClassifier.pickle.dat\", \"rb\"))\n",
    "predicted_val = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5c04d69a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>key</th>\n",
       "      <th>having_IP</th>\n",
       "      <th>URL_Length</th>\n",
       "      <th>Shortining_Service</th>\n",
       "      <th>having_At_Symbol</th>\n",
       "      <th>double_slash_redirecting</th>\n",
       "      <th>Prefix_Suffix</th>\n",
       "      <th>having_Sub_Domain</th>\n",
       "      <th>SSLfinal_State</th>\n",
       "      <th>Domain_registeration_length</th>\n",
       "      <th>...</th>\n",
       "      <th>popUpWidnow</th>\n",
       "      <th>Iframe</th>\n",
       "      <th>age_of_domain</th>\n",
       "      <th>DNSRecord</th>\n",
       "      <th>web_traffic</th>\n",
       "      <th>Page_Rank</th>\n",
       "      <th>Google_Index</th>\n",
       "      <th>Links_pointing_to_page</th>\n",
       "      <th>Statistical_report</th>\n",
       "      <th>Results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21338</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21339</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21340</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21341</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21342</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     key  having_IP  URL_Length  Shortining_Service  having_At_Symbol  \\\n",
       "0  21338          1           1                   1                 1   \n",
       "1  21339          1          -1                   1                 1   \n",
       "2  21340          1          -1                   1                 1   \n",
       "3  21341         -1          -1                  -1                 1   \n",
       "4  21342          1          -1                   1                 1   \n",
       "\n",
       "   double_slash_redirecting  Prefix_Suffix  having_Sub_Domain  SSLfinal_State  \\\n",
       "0                         1              1                 -1               1   \n",
       "1                         1             -1                  0              -1   \n",
       "2                         1             -1                  0               0   \n",
       "3                        -1             -1                 -1              -1   \n",
       "4                         1             -1                  1               1   \n",
       "\n",
       "   Domain_registeration_length  ...  popUpWidnow  Iframe  age_of_domain  \\\n",
       "0                           -1  ...            1       1              1   \n",
       "1                            1  ...            1       1             -1   \n",
       "2                           -1  ...           -1      -1              1   \n",
       "3                            1  ...           -1      -1              1   \n",
       "4                           -1  ...            1       1              1   \n",
       "\n",
       "   DNSRecord  web_traffic  Page_Rank  Google_Index  Links_pointing_to_page  \\\n",
       "0          1            1          1            -1                       0   \n",
       "1         -1            0         -1            -1                       0   \n",
       "2         -1            1         -1             1                       1   \n",
       "3         -1            1         -1             1                       1   \n",
       "4          1            1         -1             1                       0   \n",
       "\n",
       "   Statistical_report  Results  \n",
       "0                   1        1  \n",
       "1                   1       -1  \n",
       "2                  -1       -1  \n",
       "3                  -1       -1  \n",
       "4                   1        1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[\"Results\"] = predicted_val\n",
    "X_test[\"Results\"].replace({0: -1, 1: 1}, inplace=True)\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b2d1f2c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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