Catching Illegal Phishing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=\"#00adb5\">👉👨‍💻 Catching Illegal Phishing ☠</font>\n",
    "* Phishing is a cybercrime in which a target or targets are contacted by email, telephone or text message by someone posing as a legitimate institution to lure individuals into providing sensitive data such as personally identifiable information, banking and credit card details, and passwords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "To toggle on/off output_stderr, click <a href=\"javascript:code_toggle_err()\">here</a>."
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     "execution_count": 1,
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   "source": [
    "from IPython.display import HTML\n",
    "HTML('''<script>\n",
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    " code_show_err = !code_show_err\n",
    "} \n",
    "$( document ).ready(code_toggle_err);\n",
    "</script>\n",
    "To toggle on/off output_stderr, click <a href=\"javascript:code_toggle_err()\">here</a>.''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "phishing_data = pd.read_csv('phishing_site_urls.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>URL</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nobell.it/70ffb52d079109dca5664cce6f317373782/...</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>serviciosbys.com/paypal.cgi.bin.get-into.herf....</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mail.printakid.com/www.online.americanexpress....</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>thewhiskeydregs.com/wp-content/themes/widescre...</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL Label\n",
       "0  nobell.it/70ffb52d079109dca5664cce6f317373782/...   bad\n",
       "1  www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...   bad\n",
       "2  serviciosbys.com/paypal.cgi.bin.get-into.herf....   bad\n",
       "3  mail.printakid.com/www.online.americanexpress....   bad\n",
       "4  thewhiskeydregs.com/wp-content/themes/widescre...   bad"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phishing_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>URL</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>549841</th>\n",
       "      <td>cam.ac.uk</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549842</th>\n",
       "      <td>over-blog-kiwi.com</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549843</th>\n",
       "      <td>merriam-webster.com</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549844</th>\n",
       "      <td>bp3.blogger.com</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549845</th>\n",
       "      <td>kinja.com</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        URL Label\n",
       "549841            cam.ac.uk  good\n",
       "549842   over-blog-kiwi.com  good\n",
       "549843  merriam-webster.com  good\n",
       "549844      bp3.blogger.com  good\n",
       "549845            kinja.com  good"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phishing_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#f21170\">📊 Regrading Dataset</font>\n",
    "* Data is containg 5,49,346 unique entries.\n",
    "* Label column is prediction col which has 2 categories:\n",
    "1. Good - which means the urls is not containing malicious stuff and this site is not a Phishing Site. \n",
    "2. Bad - which means the urls contains malicious stuffs and this site is a Phishing Site.\n",
    "* There is no missing value in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 549846 entries, 0 to 549845\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count   Dtype \n",
      "---  ------  --------------   ----- \n",
      " 0   URL     549846 non-null  object\n",
      " 1   Label   549846 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 8.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# To get the information about 'phishing_site_urls.csv':\n",
    "phishing_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "URL      0\n",
       "Label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check if there is any missing values in dataset:\n",
    "phishing_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we create a DataFrame of classes counts\n",
    "lbl_counts = pd.DataFrame(phishing_data.Label.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's visualize the target column by using seaborn:\n",
    "# use for high-level interface for drawing attractive and informative statistical graphics \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13af5423670>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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UJCIiYsqMv2x/6qmnsFgsF/3bb39xLiIiC9uMQbJ58+a5qkNERK5RMwbJmjVrgN/cEv6VV15hdHSUe+65hz/90z+dk+JEROTqd0XnSKqqqli6dCkffvghS5Ys4Vvf+la66xIRkWvEFQXJuXPnWL9+PTabjYKCAhbgfR5FROQSrviqrcHBQQD+93//l4wMXewlIiK/cUWJUF1dTVVVFe+99x67d+9m79696a5LRESuEVf0YCufz8eLL77Ixx9/zLJly8jMzEx3XSIico24oj2SH/3oR4RCIV566SU2bdrEG2+8ke66RETkGnFFeyTt7e3827/9G06nk/Pnz/ONb3yDBx54IN21iYjINeCKgiQrKwub7TdTFy1aNOOhrcnJSaqqqvj444+ZmJhg586d/Mmf/Al79+7FYrFw2223UVtbS0ZGBh0dHbS3t2Oz2di5cyfr1q1jfHyciooKzp49i8vloqGhgezsbHp7e9m/fz9Wq5VAIMCuXbsAaGlp4dixY9hsNqqqqsjLy5uFfxYREblSV3SLlLGxMR555BHy8/N57733WLRo0SXf8/rrr5OVlcXBgwf59NNPefjhh7n99tvZs2cPd911FzU1NRw5coRVq1bR2tpKZ2cniUSCUCjE2rVraWtrw+fzUVpayuHDhwmHw1RXV1NbW0tzczNLly5l+/bt9Pf3A3Dq1CkOHTrE6dOnKS0tpbOzc3b/hUREZEZf+hYpDz744IwbvP/++ykuLp56bbVa6e/vn/qVfFFRESdOnCAjI4PVq1fjcDhwOBx4vV4GBgaIRCJs27Ztam44HCYWizExMYHX6wUgEAjQ09ODw+EgEAhgsVjIzc0lmUwyNjZGdnb2l/tXEBGR39sV3SLl3LlzdHd3c+HCBQzD4MyZM1N/+10ulwv4zW1Vdu/ezZ49e2hoaJi6+aPL5SIajRKLxfB4PNPeF4vFpo1/ca7b7Z42d3h4GKfTSVZW1rTxaDR62SCxWi1kZd0w4xyR35fWliw0V3SOZPfu3Sxfvpxf/OIXOJ1OFi9ePOP806dP8+STTxIKhfj617/OwYMHp/4Wj8fJzMzE7XYTj8enjXs8nmnjM83NzMzEbrdfdBuXk0wanDt3/kpav6ScnMt/jixMZteWyNXqUt97V/wT9e985zvccsst/PM//zO//vWvLznvV7/6FVu3bqWiooL169cD8NWvfpWTJ08C0NXVhd/vJy8vj0gkQiKRIBqNMjg4iM/no6CggOPHj0/NLSwsxO12Y7fbGRoawjAMuru78fv9FBQU0N3dTSqVYmRkhFQqpcNaIiJz7Ir2SAASiQSff/45FouF8+cv/X9c3/ve9/jss88Ih8OEw2EAvvWtb/HMM8/Q1NTEihUrKC4uxmq1UlJSQigUwjAMysrKcDqdBINBKisrCQaD2O32qeee1NXVUV5eTjKZJBAIkJ+fD4Df72fTpk2kUilqamrM/FuIiMjvwWJcwR0Yf/rTn/LRRx9x44030tzczKpVq3j++efnor60mJxMzsqhrcKKV2epIrleRA5uYXQ0Ot9liKTFpQ5tXdEeyRevwrr//vv5u7/7u1kpSkRErn1f+ja+V3IyW0REFo7f637wl3qOu4iILDxX9Mv2LzIMg+Hh4bQWJSIi144v/cv2mcZFRGThuaJftouIiFyKnpkrIiKmKEhERMQUBYmIiJiiIBEREVMUJCIiYoqCRERETFGQiIiIKQoSERExRUEiIiKmKEhERMQUBYmIiJhyxY/aFZFrQ/ZX7Fgdi+a7DLnKJCfGGfv1ZFq2nbYgefvtt/nud79La2sr/f397Nixg+XLlwMQDAZ54IEH6OjooL29HZvNxs6dO1m3bh3j4+NUVFRw9uxZXC4XDQ0NZGdn09vby/79+7FarQQCAXbt2gVAS0sLx44dw2azUVVVRV5eXrpaErkmWB2LGPrOn813GXKV8da8A1xDQfLKK6/w+uuvs3jxYgDee+89HnvsMbZu3To1Z3R0lNbWVjo7O0kkEoRCIdauXUtbWxs+n4/S0lIOHz5MOBymurqa2tpampubWbp0Kdu3b6e/vx+AU6dOcejQIU6fPk1paSmdnZ3paElERC4hLedIvF4vzc3NU6/fffddjh07xqOPPkpVVRWxWIy+vj5Wr16Nw+HA4/Hg9XoZGBggEolw9913A1BUVERPTw+xWIyJiQm8Xi8Wi4VAIEBPTw+RSIRAIIDFYiE3N5dkMsnY2Fg6WhIRkUtIyx5JcXExv/zlL6de5+XlsWHDBlauXMmLL77ICy+8wO233z7t+e8ul4tYLEYsFpsad7lcRKNRYrEYbrd72tzh4WGcTidZWVnTxqPRKNnZ2TPWZ7VayMq6YbbaFZlGa0uuVulam3Nysv2+++4jMzNz6r/r6+vx+/3E4/GpOfF4HI/Hg9vtnhqPx+NkZmZOG/viuN1uv+g2LieZNDh37rypnnJyLv85sjCZXVtmaW3KpaTre29OLv99/PHH6evrA6Cnp4c777yTvLw8IpEIiUSCaDTK4OAgPp+PgoICjh8/DkBXVxeFhYW43W7sdjtDQ0MYhkF3dzd+v5+CggK6u7tJpVKMjIyQSqUuuzciIiKza072SL797W9TX1+P3W5nyZIl1NfX43a7KSkpIRQKYRgGZWVlOJ1OgsEglZWVBINB7HY7jY2NANTV1VFeXk4ymSQQCJCfnw+A3+9n06ZNpFIpampq5qIdERH5AothGMZ8FzHXJieTs7KLV1jx6ixVJNeLyMEtjI5G57WGnByPLv+V/4+35h3Ta3NeD22JiMj1S0EiIiKmKEhERMQUBYmIiJiiIBEREVMUJCIiYoqCRERETFGQiIiIKQoSERExRUEiIiKmKEhERMQUBYmIiJiiIBEREVMUJCIiYoqCRERETFGQiIiIKQoSERExRUEiIiKmKEhERMSUtAXJ22+/TUlJCQAfffQRwWCQUChEbW0tqVQKgI6ODh555BE2btzI0aNHARgfH6e0tJRQKMQTTzzB2NgYAL29vWzYsIHNmzfT0tIy9TktLS2sX7+ezZs309fXl652RETkEtISJK+88grV1dUkEgkADhw4wJ49e3jttdcwDIMjR44wOjpKa2sr7e3tfP/736epqYmJiQna2trw+Xy89tprPPTQQ4TDYQBqa2tpbGykra2Nt99+m/7+fvr7+zl16hSHDh2iqamJurq6dLQjIiIzsKVjo16vl+bmZr75zW8C0N/fz5o1awAoKirixIkTZGRksHr1ahwOBw6HA6/Xy8DAAJFIhG3btk3NDYfDxGIxJiYm8Hq9AAQCAXp6enA4HAQCASwWC7m5uSSTScbGxsjOzp6xPqvVQlbWDeloXURrS65a6VqbaQmS4uJifvnLX069NgwDi8UCgMvlIhqNEovF8Hg8U3NcLhexWGza+Bfnut3uaXOHh4dxOp1kZWVNG49Go5cNkmTS4Ny586Z6zMnxXH6SLEhm15ZZWptyKen63ktLkPyujIz/O4IWj8fJzMzE7XYTj8enjXs8nmnjM83NzMzEbrdfdBsiIjJ35uSqra9+9aucPHkSgK6uLvx+P3l5eUQiERKJBNFolMHBQXw+HwUFBRw/fnxqbmFhIW63G7vdztDQEIZh0N3djd/vp6CggO7ublKpFCMjI6RSqcvujYiIyOyakz2SyspK9u3bR1NTEytWrKC4uBir1UpJSQmhUAjDMCgrK8PpdBIMBqmsrCQYDGK322lsbASgrq6O8vJykskkgUCA/Px8APx+P5s2bSKVSlFTUzMX7YiIyBdYDMMw5ruIuTY5mZyVY4WFFa/OUkVyvYgc3MLoaHRea8jJ8TD0nT+b1xrk6uOtecf02rzUORL9IFFERExRkIiIiCkKEhERMUVBIiIipihIRETEFAWJiIiYoiARERFTFCQiImKKgkRERExRkIiIiCkKEhERMUVBIiIipihIRETEFAWJiIiYoiARERFTFCQiImKKgkRERExRkIiIiClz8sz233rooYfweH7zqMabb76ZHTt2sHfvXiwWC7fddhu1tbVkZGTQ0dFBe3s7NpuNnTt3sm7dOsbHx6moqODs2bO4XC4aGhrIzs6mt7eX/fv3Y7VaCQQC7Nq1ay5bEhFZ8OYsSBKJBACtra1TYzt27GDPnj3cdddd1NTUcOTIEVatWkVrayudnZ0kEglCoRBr166lra0Nn89HaWkphw8fJhwOU11dTW1tLc3NzSxdupTt27fT39/PnXfeOVdtiYgseHMWJAMDA3z++eds3bqVCxcu8NRTT9Hf38+aNWsAKCoq4sSJE2RkZLB69WocDgcOhwOv18vAwACRSIRt27ZNzQ2Hw8RiMSYmJvB6vQAEAgF6enouGyRWq4WsrBvS27AsWFpbcrVK19qcsyBZtGgRjz/+OBs2bODDDz/kiSeewDAMLBYLAC6Xi2g0SiwWmzr89dvxWCw2bfyLc91u97S5w8PDl60lmTQ4d+68qX5ycjyXnyQLktm1ZZbWplxKur735ixIbrnlFpYtW4bFYuGWW24hKyuL/v7+qb/H43EyMzNxu93E4/Fp4x6PZ9r4THMzMzPnqiUREWEOr9r60Y9+xHPPPQfAJ598QiwWY+3atZw8eRKArq4u/H4/eXl5RCIREokE0WiUwcFBfD4fBQUFHD9+fGpuYWEhbrcbu93O0NAQhmHQ3d2N3++fq5ZERIQ53CNZv349Tz/9NMFgEIvFwrPPPsuNN97Ivn37aGpqYsWKFRQXF2O1WikpKSEUCmEYBmVlZTidToLBIJWVlQSDQex2O42NjQDU1dVRXl5OMpkkEAiQn58/Vy2JiAhgMQzDmO8i5trkZHJWjhUWVrw6SxXJ9SJycAujo9F5rSEnx8PQd/5sXmuQq4+35h3Ta/NS50j0g0QRETFFQSIiIqYoSERExBQFiYiImKIgERERUxQkIiJiioJERERMUZCIiIgpChIRETFFQSIiIqYoSERExBQFiYiImKIgERERUxQkIiJiioJERERMUZCIiIgpChIRETFFQSIiIqbM2TPb0ymVSvHtb3+bn//85zgcDp555hmWLVs232WJiCwI18UeyZtvvsnExAQ//OEP+fu//3uee+65+S5JRGTBuC6CJBKJcPfddwOwatUq3n333XmuSERk4bguDm3FYjHcbvfUa6vVyoULF7DZLt6e3W4lJ8dj+nMjB7eY3oZcf2ZjbZnlrXlnvkuQq1C61uZ1sUfidruJx+NTr1Op1CVDREREZtd1ESQFBQV0dXUB0Nvbi8/nm+eKREQWDothGMZ8F2HWb6/a+sUvfoFhGDz77LPceuut812WiMiCcF0EiYiIzJ/r4tCWiIjMHwWJiIiYoiARERFTFCSSdoODg5SUlMx3GXId+vGPf8x3v/vdL/2+tWvXpqGahUtBIiIipuhXe3JR4+PjfPOb3+TMmTPcdNNNvPXWW7z88svU19djtVpxOp3U19eTm5vLP/3TP3H48GFsNht+v5+KigrOnDlDeXk5hmGQk5Mz3+3Iday3t5dvfOMbxGIxSktLGR8f5wc/+MHU3//xH/+Rr3zlK+zbt4///u//ZunSpUxMTMxjxdcfBYlc1A9/+ENuvvlmnn/+eQYHB3nwwQeprq5m//793HHHHbz55ps899xzPPnkk/z7v/877e3t2Gw2SktLOXr0KKdOneLBBx9k48aNvPHGG7S1tc13S3KdWrx4MS+//DJjY2Ns2LCBjRs38vLLL7N48WJqamro7u7G4/GQSCTo6OhgZGSEn/70p/Nd9nVFh7bkogYHBykoKADg1ltvJTs7mzNnznDHHXcA8Od//ue8//77/M///A/5+fnY7XYsFgt+v5/333+f999/n7y8PICp7YikQ2FhIRaLhT/4gz/A4/Fgs9morKzk6aef5uc//zkXLlyYth5zc3O56aab5rnq64uCRC7K5/Pxn//5nwAMDQ3x6aef8od/+IcMDAwA8NZbb7F8+XJWrFhBX18fFy5cwDAM3nrrLW655RZWrFgx9f533tENBCV9fru+RkdHiUaj/Mu//Av/8A//wDPPPIPT6cQwDFasWEFvby8An3zyCZ988sl8lnzd0aEtuaj169ezd+9eHn30UXJzc3E6nTzzzDPU1yzC5VAAAADVSURBVNdjGAZWq5Vnn32WpUuX8td//dcEg0FSqRSFhYXce++9/MVf/AVlZWW88cYb3HzzzfPdjlzHxsfH2bJlC+fPn2f//v20t7fz8MMPc8MNN5CZmcmZM2f427/9WyKRCBs2bCA3N5cbb7xxvsu+rugWKXJR//Ef/8H58+cJBAJ8+OGHbNu2jTfffHO+yxKRq5CCRC5qdHSUp556isnJSS5cuMDu3bspKiqa77JE5CqkIBEREVN0sl1ERExRkIiIiCkKEhERMUVBIiIipihIRETElP8HqR9ZT6XjHoUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "sns.barplot(lbl_counts.index, lbl_counts.Label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#ff8882\"> Preprocessing</font>\n",
    "* So, now that we have the data, we have to vectorize our URLs. So, I'm using CountVectorizer to gather words using tokenizer, since there are words in urls that are more important than other words such as:\n",
    "    1. -> Virus\n",
    "    2. -> .exe\n",
    "    3. -> .dat\n",
    "* Let's convert the URLs into a vector form."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#1597bb\">Regexp Tokenizer</font>\n",
    "* A tokenizer that splits a string using a regular expression, which matches either the tokens or the separators between tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use Regexp tokenizers to split words from text:\n",
    "from nltk.tokenize import RegexpTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this expression we are spliting only alphabets\n",
    "tokenizer = RegexpTokenizer(r'[A-Za-z]+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nobell.it/70ffb52d079109dca5664cce6f317373782/login.SkyPe.com/en/cgi-bin/verification/login/70ffb52d079109dca5664cce6f317373/index.php?cmd=_profile-ach&outdated_page_tmpl=p/gen/failed-to-load&nav=0.5.1&login_access=1322408526\n"
     ]
    }
   ],
   "source": [
    "'''For example here you can see lots of numbers, symbols, dots, etc which\n",
    "is not important to your data so we remove this and get only strings of alphabets.''' \n",
    "print(phishing_data.URL[0]) # This 0 is first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['nobell', 'it', 'ffb', 'd', 'dca', 'cce', 'f', 'login', 'SkyPe', 'com', 'en', 'cgi', 'bin', 'verification', 'login', 'ffb', 'd', 'dca', 'cce', 'f', 'index', 'php', 'cmd', 'profile', 'ach', 'outdated', 'page', 'tmpl', 'p', 'gen', 'failed', 'to', 'load', 'nav', 'login', 'access']\n"
     ]
    }
   ],
   "source": [
    "# This command will only pull all the alphabet strings present in URL:\n",
    "clean_text = tokenizer.tokenize(phishing_data.URL[0]) \n",
    "print(clean_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#ff1a75\">⏰ Time module</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time required to tokenize text is: \n",
      "3.04 sec\n"
     ]
    }
   ],
   "source": [
    "# So now we transform all the URLs to clean_text:\n",
    "# To calculate time of execution we import time\n",
    "import time\n",
    "start = time.time()\n",
    "phishing_data['text_tokenized'] = phishing_data.URL.map(lambda text: tokenizer.tokenize(text))\n",
    "end = time.time()\n",
    "time_req = end - start\n",
    "formatted_time = \"{:.2f}\".format(time_req)\n",
    "print(f\"Time required to tokenize text is: \\n{formatted_time} sec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301210</th>\n",
       "      <td>cduniverse.com/productinfo.asp?pid=7506614</td>\n",
       "      <td>good</td>\n",
       "      <td>[cduniverse, com, productinfo, asp, pid]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292666</th>\n",
       "      <td>blainejensenrv.com/travel-trailer-toyhauler/20...</td>\n",
       "      <td>good</td>\n",
       "      <td>[blainejensenrv, com, travel, trailer, toyhaul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54080</th>\n",
       "      <td>www.aim-users.com/3bbs.htm</td>\n",
       "      <td>good</td>\n",
       "      <td>[www, aim, users, com, bbs, htm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258791</th>\n",
       "      <td>wiki.answers.com/Q/What_is_the_Flintstones_nei...</td>\n",
       "      <td>good</td>\n",
       "      <td>[wiki, answers, com, Q, What, is, the, Flintst...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58596</th>\n",
       "      <td>www.dsi.unifi.it/neural</td>\n",
       "      <td>good</td>\n",
       "      <td>[www, dsi, unifi, it, neural]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493404</th>\n",
       "      <td>copy.com/s8w9tqqzVDaXIkcR/הריגתו של קצין ביטחו...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[copy, com, s, w, tqqzVDaXIkcR, rar, download]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518590</th>\n",
       "      <td>21oq88trjd.drefcvest.in/s1eji0kjw1\\n</td>\n",
       "      <td>bad</td>\n",
       "      <td>[oq, trjd, drefcvest, in, s, eji, kjw, n]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      URL Label  \\\n",
       "301210         cduniverse.com/productinfo.asp?pid=7506614  good   \n",
       "292666  blainejensenrv.com/travel-trailer-toyhauler/20...  good   \n",
       "54080                          www.aim-users.com/3bbs.htm  good   \n",
       "258791  wiki.answers.com/Q/What_is_the_Flintstones_nei...  good   \n",
       "58596                             www.dsi.unifi.it/neural  good   \n",
       "493404  copy.com/s8w9tqqzVDaXIkcR/הריגתו של קצין ביטחו...   bad   \n",
       "518590               21oq88trjd.drefcvest.in/s1eji0kjw1\\n   bad   \n",
       "\n",
       "                                           text_tokenized  \n",
       "301210           [cduniverse, com, productinfo, asp, pid]  \n",
       "292666  [blainejensenrv, com, travel, trailer, toyhaul...  \n",
       "54080                    [www, aim, users, com, bbs, htm]  \n",
       "258791  [wiki, answers, com, Q, What, is, the, Flintst...  \n",
       "58596                       [www, dsi, unifi, it, neural]  \n",
       "493404     [copy, com, s, w, tqqzVDaXIkcR, rar, download]  \n",
       "518590          [oq, trjd, drefcvest, in, s, eji, kjw, n]  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's check some sample results of URLs conversion to tokenize text:\n",
    "phishing_data.sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#f875aa\">Snowball Stemmer NLTK</font>\n",
    "* Snowball is a small string processing language designed for creating stemming algorithms for use in Information Retrieval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we use Snowball stemmer to get the root words out of tokenized text:\n",
    "from nltk.stem.snowball import SnowballStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "⏳ Time required for stemming all the tokenized text is: \n",
      "77.52 sec\n"
     ]
    }
   ],
   "source": [
    "# I am using english language for stemming purpose you can choose any language:\n",
    "sbs = SnowballStemmer(\"english\")\n",
    "\n",
    "# we will see the execution time to stem the tokenize text:\n",
    "start = time.time()\n",
    "phishing_data['text_stemmed'] = phishing_data['text_tokenized'].map(lambda text: [sbs.stem(word) for word in text])\n",
    "end = time.time()\n",
    "time_req = end - start\n",
    "formatted_time = \"{:.2f}\".format(time_req)\n",
    "print(f\"⏳ Time required for stemming all the tokenized text is: \\n{formatted_time} sec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118837</th>\n",
       "      <td>sphinxservices.com.au/mnm/index.htm</td>\n",
       "      <td>bad</td>\n",
       "      <td>[sphinxservices, com, au, mnm, index, htm]</td>\n",
       "      <td>[sphinxservic, com, au, mnm, index, htm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163367</th>\n",
       "      <td>dating.poptower.com/delroy-lindo-dating.htm</td>\n",
       "      <td>good</td>\n",
       "      <td>[dating, poptower, com, delroy, lindo, dating,...</td>\n",
       "      <td>[date, poptow, com, delroy, lindo, date, htm]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13586</th>\n",
       "      <td>serviceinformation.pakfreewebs.com/verifier/et...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[serviceinformation, pakfreewebs, com, verifie...</td>\n",
       "      <td>[serviceinform, pakfreeweb, com, verifi, etap,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286559</th>\n",
       "      <td>azchords.com/p/planetsmashers-tabs-12218.html</td>\n",
       "      <td>good</td>\n",
       "      <td>[azchords, com, p, planetsmashers, tabs, html]</td>\n",
       "      <td>[azchord, com, p, planetsmash, tab, html]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23051</th>\n",
       "      <td>alltomcasinospel.se/images/ind.html</td>\n",
       "      <td>bad</td>\n",
       "      <td>[alltomcasinospel, se, images, ind, html]</td>\n",
       "      <td>[alltomcasinospel, se, imag, ind, html]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495042</th>\n",
       "      <td>85.227.8.132/</td>\n",
       "      <td>bad</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265559</th>\n",
       "      <td>3dtotal.com/tutorial/maya/car_modeling_ford_gt...</td>\n",
       "      <td>good</td>\n",
       "      <td>[dtotal, com, tutorial, maya, car, modeling, f...</td>\n",
       "      <td>[dtotal, com, tutori, maya, car, model, ford, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      URL Label  \\\n",
       "118837                sphinxservices.com.au/mnm/index.htm   bad   \n",
       "163367        dating.poptower.com/delroy-lindo-dating.htm  good   \n",
       "13586   serviceinformation.pakfreewebs.com/verifier/et...   bad   \n",
       "286559      azchords.com/p/planetsmashers-tabs-12218.html  good   \n",
       "23051                 alltomcasinospel.se/images/ind.html   bad   \n",
       "495042                                      85.227.8.132/   bad   \n",
       "265559  3dtotal.com/tutorial/maya/car_modeling_ford_gt...  good   \n",
       "\n",
       "                                           text_tokenized  \\\n",
       "118837         [sphinxservices, com, au, mnm, index, htm]   \n",
       "163367  [dating, poptower, com, delroy, lindo, dating,...   \n",
       "13586   [serviceinformation, pakfreewebs, com, verifie...   \n",
       "286559     [azchords, com, p, planetsmashers, tabs, html]   \n",
       "23051           [alltomcasinospel, se, images, ind, html]   \n",
       "495042                                                 []   \n",
       "265559  [dtotal, com, tutorial, maya, car, modeling, f...   \n",
       "\n",
       "                                             text_stemmed  \n",
       "118837           [sphinxservic, com, au, mnm, index, htm]  \n",
       "163367      [date, poptow, com, delroy, lindo, date, htm]  \n",
       "13586   [serviceinform, pakfreeweb, com, verifi, etap,...  \n",
       "286559          [azchord, com, p, planetsmash, tab, html]  \n",
       "23051             [alltomcasinospel, se, imag, ind, html]  \n",
       "495042                                                 []  \n",
       "265559  [dtotal, com, tutori, maya, car, model, ford, ...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now let's see the sample stemmed text:\n",
    "phishing_data.sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time required for joining text to sentence is: \n",
      "0.38 sec\n"
     ]
    }
   ],
   "source": [
    "# So, now we join the stemmed words together as a sentence:\n",
    "start = time.time()\n",
    "phishing_data['text_to_sent'] = phishing_data['text_stemmed'].map(lambda text: ' '.join(text))\n",
    "end = time.time()\n",
    "time_req = end - start\n",
    "formatted_time = \"{:.2f}\".format(time_req)\n",
    "print(f\"Time required for joining text to sentence is: \\n{formatted_time} sec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "      <th>text_to_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>307937</th>\n",
       "      <td>collingwoodlifestyle.com/</td>\n",
       "      <td>good</td>\n",
       "      <td>[collingwoodlifestyle, com]</td>\n",
       "      <td>[collingwoodlifestyl, com]</td>\n",
       "      <td>collingwoodlifestyl com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84146</th>\n",
       "      <td>www.mywot.com/en/download/ie</td>\n",
       "      <td>good</td>\n",
       "      <td>[www, mywot, com, en, download, ie]</td>\n",
       "      <td>[www, mywot, com, en, download, ie]</td>\n",
       "      <td>www mywot com en download ie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218349</th>\n",
       "      <td>music.yahoo.com/norman-brown/</td>\n",
       "      <td>good</td>\n",
       "      <td>[music, yahoo, com, norman, brown]</td>\n",
       "      <td>[music, yahoo, com, norman, brown]</td>\n",
       "      <td>music yahoo com norman brown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525266</th>\n",
       "      <td>142.47.213.123/Ares13.zip</td>\n",
       "      <td>bad</td>\n",
       "      <td>[Ares, zip]</td>\n",
       "      <td>[are, zip]</td>\n",
       "      <td>are zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368778</th>\n",
       "      <td>kittykatrescue.com/</td>\n",
       "      <td>good</td>\n",
       "      <td>[kittykatrescue, com]</td>\n",
       "      <td>[kittykatrescu, com]</td>\n",
       "      <td>kittykatrescu com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505915</th>\n",
       "      <td>parroquiansg.org/8g74crec/</td>\n",
       "      <td>bad</td>\n",
       "      <td>[parroquiansg, org, g, crec]</td>\n",
       "      <td>[parroquiansg, org, g, crec]</td>\n",
       "      <td>parroquiansg org g crec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152997</th>\n",
       "      <td>braveastronaut.blogspot.com/</td>\n",
       "      <td>good</td>\n",
       "      <td>[braveastronaut, blogspot, com]</td>\n",
       "      <td>[braveastronaut, blogspot, com]</td>\n",
       "      <td>braveastronaut blogspot com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180905</th>\n",
       "      <td>en.wikipedia.org/wiki/Strictly_Come_Dancing_(s...</td>\n",
       "      <td>good</td>\n",
       "      <td>[en, wikipedia, org, wiki, Strictly, Come, Dan...</td>\n",
       "      <td>[en, wikipedia, org, wiki, strict, come, danc,...</td>\n",
       "      <td>en wikipedia org wiki strict come danc seri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105974</th>\n",
       "      <td>mediasmall.com/applicationload/index.html</td>\n",
       "      <td>bad</td>\n",
       "      <td>[mediasmall, com, applicationload, index, html]</td>\n",
       "      <td>[mediasmal, com, applicationload, index, html]</td>\n",
       "      <td>mediasmal com applicationload index html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284016</th>\n",
       "      <td>artnet.com/artists/charles+ragland-bunnell/bio...</td>\n",
       "      <td>good</td>\n",
       "      <td>[artnet, com, artists, charles, ragland, bunne...</td>\n",
       "      <td>[artnet, com, artist, charl, ragland, bunnel, ...</td>\n",
       "      <td>artnet com artist charl ragland bunnel biograp...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      URL Label  \\\n",
       "307937                          collingwoodlifestyle.com/  good   \n",
       "84146                        www.mywot.com/en/download/ie  good   \n",
       "218349                      music.yahoo.com/norman-brown/  good   \n",
       "525266                          142.47.213.123/Ares13.zip   bad   \n",
       "368778                                kittykatrescue.com/  good   \n",
       "505915                         parroquiansg.org/8g74crec/   bad   \n",
       "152997                       braveastronaut.blogspot.com/  good   \n",
       "180905  en.wikipedia.org/wiki/Strictly_Come_Dancing_(s...  good   \n",
       "105974          mediasmall.com/applicationload/index.html   bad   \n",
       "284016  artnet.com/artists/charles+ragland-bunnell/bio...  good   \n",
       "\n",
       "                                           text_tokenized  \\\n",
       "307937                        [collingwoodlifestyle, com]   \n",
       "84146                 [www, mywot, com, en, download, ie]   \n",
       "218349                 [music, yahoo, com, norman, brown]   \n",
       "525266                                        [Ares, zip]   \n",
       "368778                              [kittykatrescue, com]   \n",
       "505915                       [parroquiansg, org, g, crec]   \n",
       "152997                    [braveastronaut, blogspot, com]   \n",
       "180905  [en, wikipedia, org, wiki, Strictly, Come, Dan...   \n",
       "105974    [mediasmall, com, applicationload, index, html]   \n",
       "284016  [artnet, com, artists, charles, ragland, bunne...   \n",
       "\n",
       "                                             text_stemmed  \\\n",
       "307937                         [collingwoodlifestyl, com]   \n",
       "84146                 [www, mywot, com, en, download, ie]   \n",
       "218349                 [music, yahoo, com, norman, brown]   \n",
       "525266                                         [are, zip]   \n",
       "368778                               [kittykatrescu, com]   \n",
       "505915                       [parroquiansg, org, g, crec]   \n",
       "152997                    [braveastronaut, blogspot, com]   \n",
       "180905  [en, wikipedia, org, wiki, strict, come, danc,...   \n",
       "105974     [mediasmal, com, applicationload, index, html]   \n",
       "284016  [artnet, com, artist, charl, ragland, bunnel, ...   \n",
       "\n",
       "                                             text_to_sent  \n",
       "307937                            collingwoodlifestyl com  \n",
       "84146                        www mywot com en download ie  \n",
       "218349                       music yahoo com norman brown  \n",
       "525266                                            are zip  \n",
       "368778                                  kittykatrescu com  \n",
       "505915                            parroquiansg org g crec  \n",
       "152997                        braveastronaut blogspot com  \n",
       "180905        en wikipedia org wiki strict come danc seri  \n",
       "105974           mediasmal com applicationload index html  \n",
       "284016  artnet com artist charl ragland bunnel biograp...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see some sample results of joined text to sentences:\n",
    "phishing_data.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#845ec2\">Visualization Part</font>\n",
    "##### <font color=\"#3aa6c5\">It is very important to know your data and visualize it to understand it better.</font>\n",
    "    1.Let's visualize some important key words using Wordcloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we slice the classes as:\n",
    "phishing_sites = phishing_data[phishing_data.Label == 'bad']\n",
    "not_phishing_sites = phishing_data[phishing_data.Label == 'good']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "      <th>text_to_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nobell.it/70ffb52d079109dca5664cce6f317373782/...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[nobell, it, ffb, d, dca, cce, f, login, SkyPe...</td>\n",
       "      <td>[nobel, it, ffb, d, dca, cce, f, login, skype,...</td>\n",
       "      <td>nobel it ffb d dca cce f login skype com en cg...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[www, dghjdgf, com, paypal, co, uk, cycgi, bin...</td>\n",
       "      <td>[www, dghjdgf, com, paypal, co, uk, cycgi, bin...</td>\n",
       "      <td>www dghjdgf com paypal co uk cycgi bin webscrc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>serviciosbys.com/paypal.cgi.bin.get-into.herf....</td>\n",
       "      <td>bad</td>\n",
       "      <td>[serviciosbys, com, paypal, cgi, bin, get, int...</td>\n",
       "      <td>[serviciosbi, com, paypal, cgi, bin, get, into...</td>\n",
       "      <td>serviciosbi com paypal cgi bin get into herf s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mail.printakid.com/www.online.americanexpress....</td>\n",
       "      <td>bad</td>\n",
       "      <td>[mail, printakid, com, www, online, americanex...</td>\n",
       "      <td>[mail, printakid, com, www, onlin, americanexp...</td>\n",
       "      <td>mail printakid com www onlin americanexpress c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>thewhiskeydregs.com/wp-content/themes/widescre...</td>\n",
       "      <td>bad</td>\n",
       "      <td>[thewhiskeydregs, com, wp, content, themes, wi...</td>\n",
       "      <td>[thewhiskeydreg, com, wp, content, theme, wide...</td>\n",
       "      <td>thewhiskeydreg com wp content theme widescreen...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 URL Label  \\\n",
       "0  nobell.it/70ffb52d079109dca5664cce6f317373782/...   bad   \n",
       "1  www.dghjdgf.com/paypal.co.uk/cycgi-bin/webscrc...   bad   \n",
       "2  serviciosbys.com/paypal.cgi.bin.get-into.herf....   bad   \n",
       "3  mail.printakid.com/www.online.americanexpress....   bad   \n",
       "4  thewhiskeydregs.com/wp-content/themes/widescre...   bad   \n",
       "\n",
       "                                      text_tokenized  \\\n",
       "0  [nobell, it, ffb, d, dca, cce, f, login, SkyPe...   \n",
       "1  [www, dghjdgf, com, paypal, co, uk, cycgi, bin...   \n",
       "2  [serviciosbys, com, paypal, cgi, bin, get, int...   \n",
       "3  [mail, printakid, com, www, online, americanex...   \n",
       "4  [thewhiskeydregs, com, wp, content, themes, wi...   \n",
       "\n",
       "                                        text_stemmed  \\\n",
       "0  [nobel, it, ffb, d, dca, cce, f, login, skype,...   \n",
       "1  [www, dghjdgf, com, paypal, co, uk, cycgi, bin...   \n",
       "2  [serviciosbi, com, paypal, cgi, bin, get, into...   \n",
       "3  [mail, printakid, com, www, onlin, americanexp...   \n",
       "4  [thewhiskeydreg, com, wp, content, theme, wide...   \n",
       "\n",
       "                                        text_to_sent  \n",
       "0  nobel it ffb d dca cce f login skype com en cg...  \n",
       "1  www dghjdgf com paypal co uk cycgi bin webscrc...  \n",
       "2  serviciosbi com paypal cgi bin get into herf s...  \n",
       "3  mail printakid com www onlin americanexpress c...  \n",
       "4  thewhiskeydreg com wp content theme widescreen...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using head() function to see the top 5 URLs of phishing sites:\n",
    "phishing_sites.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL</th>\n",
       "      <th>Label</th>\n",
       "      <th>text_tokenized</th>\n",
       "      <th>text_stemmed</th>\n",
       "      <th>text_to_sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18231</th>\n",
       "      <td>esxcc.com/js/index.htm?us.battle.net/noghn/en/...</td>\n",
       "      <td>good</td>\n",
       "      <td>[esxcc, com, js, index, htm, us, battle, net, ...</td>\n",
       "      <td>[esxcc, com, js, index, htm, us, battl, net, n...</td>\n",
       "      <td>esxcc com js index htm us battl net noghn en r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18232</th>\n",
       "      <td>www\u000eeira¯&amp;nvinip¿ncH¯wVö%ÆåyDaHðû/ÏyEùu\u0003Ë\\nÓ\u00176...</td>\n",
       "      <td>good</td>\n",
       "      <td>[www, eira, nvinip, ncH, wV, yDaH, yE, u, rT, ...</td>\n",
       "      <td>[www, eira, nvinip, nch, wv, ydah, ye, u, rt, ...</td>\n",
       "      <td>www eira nvinip nch wv ydah ye u rt u g m i xz...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18233</th>\n",
       "      <td>'www.institutocgr.coo/web/media/syqvem/dk-\u000fóij...</td>\n",
       "      <td>good</td>\n",
       "      <td>[www, institutocgr, coo, web, media, syqvem, d...</td>\n",
       "      <td>[www, institutocgr, coo, web, media, syqvem, d...</td>\n",
       "      <td>www institutocgr coo web media syqvem dk ij r ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18234</th>\n",
       "      <td>\u0011\u0018Yìê\f",
       "koãÕ»Î§DéÎ\u0002l½ñ¡ââqtò¸/à; Í</td>\n",
       "      <td>good</td>\n",
       "      <td>[Y, ko, D, l, qt]</td>\n",
       "      <td>[y, ko, d, l, qt]</td>\n",
       "      <td>y ko d l qt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>ruta89fm.com/images/AS@Vies/1i75cf7b16vc&lt;F\u0015d16...</td>\n",
       "      <td>good</td>\n",
       "      <td>[ruta, fm, com, images, AS, Vies, i, cf, b, vc...</td>\n",
       "      <td>[ruta, fm, com, imag, as, vie, i, cf, b, vc, f...</td>\n",
       "      <td>ruta fm com imag as vie i cf b vc f d b g sd v...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     URL Label  \\\n",
       "18231  esxcc.com/js/index.htm?us.battle.net/noghn/en/...  good   \n",
       "18232  www\u000eeira¯&nvinip¿ncH¯wVö%ÆåyDaHðû/ÏyEùu\u0003Ë\\nÓ\u00176...  good   \n",
       "18233  'www.institutocgr.coo/web/media/syqvem/dk-\u000fóij...  good   \n",
       "18234                  \u0011\u0018Yìê\n",
       "koãÕ»Î§DéÎ\u0002l½ñ¡ââqtò¸/à; Í  good   \n",
       "18236  ruta89fm.com/images/AS@Vies/1i75cf7b16vc<F\u0015d16...  good   \n",
       "\n",
       "                                          text_tokenized  \\\n",
       "18231  [esxcc, com, js, index, htm, us, battle, net, ...   \n",
       "18232  [www, eira, nvinip, ncH, wV, yDaH, yE, u, rT, ...   \n",
       "18233  [www, institutocgr, coo, web, media, syqvem, d...   \n",
       "18234                                  [Y, ko, D, l, qt]   \n",
       "18236  [ruta, fm, com, images, AS, Vies, i, cf, b, vc...   \n",
       "\n",
       "                                            text_stemmed  \\\n",
       "18231  [esxcc, com, js, index, htm, us, battl, net, n...   \n",
       "18232  [www, eira, nvinip, nch, wv, ydah, ye, u, rt, ...   \n",
       "18233  [www, institutocgr, coo, web, media, syqvem, d...   \n",
       "18234                                  [y, ko, d, l, qt]   \n",
       "18236  [ruta, fm, com, imag, as, vie, i, cf, b, vc, f...   \n",
       "\n",
       "                                            text_to_sent  \n",
       "18231  esxcc com js index htm us battl net noghn en r...  \n",
       "18232  www eira nvinip nch wv ydah ye u rt u g m i xz...  \n",
       "18233  www institutocgr coo web media syqvem dk ij r ...  \n",
       "18234                                        y ko d l qt  \n",
       "18236  ruta fm com imag as vie i cf b vc f d b g sd v...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using head() function to see the top 5 URLs of Not phishing sites:\n",
    "not_phishing_sites.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from wordcloud import WordCloud\n",
    "from wordcloud import STOPWORDS\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now let's create a function to visualize the important words from URLs:\n",
    "\n",
    "def my_wordcloud(text, mask=None, max_words=500, max_font_size=70, figure_size=(8.0, 10.0),\n",
    "                title=None, title_size=70, image_color=False):\n",
    "    \n",
    "    stopwords = set(STOPWORDS)\n",
    "    my_stopwords = {'com', 'http'}\n",
    "    stopwords = stopwords.union(my_stopwords)\n",
    "    \n",
    "    wordcloud = WordCloud(background_color='#fff', \n",
    "                         stopwords = stopwords,\n",
    "                         max_words = max_words,\n",
    "                         random_state = 42,                            \n",
    "                         mask = mask)\n",
    "    \n",
    "    wordcloud.generate(text)\n",
    "    \n",
    "    plt.figure(figsize=figure_size)\n",
    "    \n",
    "    if image_color:\n",
    "        image_color = ImageColorGenerator(mask);\n",
    "        plt.imshow(wordcloud.recolor(color_func=image_colors),\n",
    "                  interpolation='bilinear');\n",
    "        \n",
    "        plt.title(title,\n",
    "                 fontdict={'size': title_size,\n",
    "                          'verticalalignment': 'bottom'})\n",
    "        \n",
    "    else:\n",
    "        plt.imshow(wordcloud);\n",
    "        plt.title(title,\n",
    "                 fontdict={'size': title_size,\n",
    "                          'color': '#ff3333',\n",
    "                          'verticalalignment': 'bottom'})\n",
    "\n",
    "    plt.axis('off');\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data = not_phishing_sites.text_to_sent\n",
    "my_data.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "not_phishing_common_text = str(my_data)\n",
    "common_mask = np.array(Image.open('idea.png'))\n",
    "my_wordcloud(not_phishing_common_text,\n",
    "               common_mask,\n",
    "               max_words=400, \n",
    "               max_font_size=50, \n",
    "               title = 'The Most common words use in not phishing URLs:',\n",
    "               title_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for phishing sites common words are:\n",
    "my_data = phishing_sites.text_to_sent\n",
    "my_data.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's create a wordcloud for phishing sites:\n",
    "phishing_common_words = str(my_data)\n",
    "common_mask = np.array(Image.open('target.png'))\n",
    "my_wordcloud(phishing_common_words, \n",
    "             common_mask,\n",
    "             max_words=500, \n",
    "             max_font_size=20, \n",
    "             title='The Most common words use in phishing URLs:', \n",
    "             title_size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#28b5b5\">To visualize internal links as it will show us all redirect links.</font>\n",
    "#### <font color=\"#ff8882\">To Scrape any website</font>\n",
    "   - Firtly, we nedd to setup the Chrome webdriver so we can scrape dynamic webpages.\n",
    "   \n",
    "#### <font color=\"#f0c929\">The Chrome webdriver</font>\n",
    "   - WebDriver tool use for automated testing of webapps across many browsers. It provides capabilities for navigating to web pages, user input and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: selenium in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (3.141.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
      "distutils: c:\\users\\ashhar\\anaconda3\\Include\\UNKNOWN\n",
      "sysconfig: c:\\users\\ashhar\\anaconda3\\Include\n",
      "WARNING: Additional context:\n",
      "user = False\n",
      "home = None\n",
      "root = None\n",
      "prefix = None\n",
      "WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
      "distutils: c:\\users\\ashhar\\anaconda3\\Include\\UNKNOWN\n",
      "sysconfig: c:\\users\\ashhar\\anaconda3\\Include\n",
      "WARNING: Additional context:\n",
      "user = False\n",
      "home = None\n",
      "root = None\n",
      "prefix = None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: urllib3 in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from selenium) (1.25.9)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
      "distutils: c:\\users\\ashhar\\anaconda3\\Include\\UNKNOWN\n",
      "sysconfig: c:\\users\\ashhar\\anaconda3\\Include\n",
      "WARNING: Additional context:\n",
      "user = False\n",
      "home = None\n",
      "root = None\n",
      "prefix = None\n",
      "WARNING: Value for scheme.headers does not match. Please report this to <https://github.com/pypa/pip/issues/9617>\n",
      "distutils: c:\\users\\ashhar\\anaconda3\\Include\\UNKNOWN\n",
      "sysconfig: c:\\users\\ashhar\\anaconda3\\Include\n",
      "WARNING: Additional context:\n",
      "user = False\n",
      "home = None\n",
      "root = None\n",
      "prefix = None\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: webdriver-manager in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (3.4.0)\n",
      "Requirement already satisfied: crayons in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from webdriver-manager) (0.4.0)\n",
      "Requirement already satisfied: configparser in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from webdriver-manager) (5.0.2)\n",
      "Requirement already satisfied: requests in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from webdriver-manager) (2.24.0)\n",
      "Requirement already satisfied: colorama in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from crayons->webdriver-manager) (0.4.3)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (1.25.9)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (2.10)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (3.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ashhar\\anaconda3\\lib\\site-packages (from requests->webdriver-manager) (2020.6.20)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "====== WebDriver manager ======\n",
      "Current google-chrome version is 90.0.4430\n",
      "Get LATEST driver version for 90.0.4430\n",
      "Driver [C:\\Users\\Ashhar\\.wdm\\drivers\\chromedriver\\win32\\90.0.4430.24\\chromedriver.exe] found in cache\n"
     ]
    }
   ],
   "source": [
    "# we need to install selenium to use webdriver:\n",
    "! pip install selenium\n",
    "! pip install webdriver-manager\n",
    "# used for automation chrome:\n",
    "from selenium import webdriver\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "\n",
    "driver = webdriver.Chrome(ChromeDriverManager().install())\n",
    "# This warning is not harmful per-se, and doesn't affect any installation \n",
    "# logic. You can safely use the current pip, ignoring this warning for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <font color=\"#ff3399\">so now we need to create two lists.</font>\n",
    "* First, named list_urls holds all the pages you’d like to scrape.\n",
    "* Secondcreate an empty list where you’ll append links from each page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So here we are taking 2 phishing sites in the list:\n",
    "urls_list = ['http://24fxtradetech.com.cp-16.webhostbox.net/', 'http://www.autoentsorgen.ch/sitemaps.html']\n",
    "links_with_text = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- <font color=\"#3333ff\">I took some phishing site to see were the hackers redirect(on different link) us.</font>\n",
    "* So, to extract only relevant hyperlinks for Google, i.e., links only with '<'a'>' tag with href attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#00cccc\">BeautifulSoup Library</font>\n",
    "* It is use for getting data out of HTML, XML, and other markup languages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# used for scraping the data from websites:\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for url in urls_list:\n",
    "    driver.get(url)\n",
    "    soup = BeautifulSoup(driver.page_source, \"html.parser\")\n",
    "    for line in soup.find_all('a'):\n",
    "        href = line.get('href')\n",
    "        links_with_text.append([url, href])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#ffb3b3\">Turn the URL's into a DataFrame</font>\n",
    "* After you get the list of your websites with hyperlinks turn them into a Pandas DataFrame with columns “from” (URL where the link resides) and “to” (link destination URL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(links_with_text, columns=[\"from\", \"to\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>Webhostbox.net</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Domain_Name_Regist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Top_10_Domain_Host...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Best_Web_Hosting_P...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Best_Forum_Hosting...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Hosting_Coupon_Cod...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Top_10_Web_Hosting...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Web_Hosting_Offers...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Domain_Name_Regist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>http://24fxtradetech.com.cp-16.webhostbox.net/</td>\n",
       "      <td>http://freeresultsguide.com/Top_10_Domain_Host...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             from  \\\n",
       "0  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "1  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "2  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "3  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "4  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "5  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "6  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "7  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "8  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "9  http://24fxtradetech.com.cp-16.webhostbox.net/   \n",
       "\n",
       "                                                  to  \n",
       "0                                     Webhostbox.net  \n",
       "1  http://freeresultsguide.com/Domain_Name_Regist...  \n",
       "2  http://freeresultsguide.com/Top_10_Domain_Host...  \n",
       "3  http://freeresultsguide.com/Best_Web_Hosting_P...  \n",
       "4  http://freeresultsguide.com/Best_Forum_Hosting...  \n",
       "5  http://freeresultsguide.com/Hosting_Coupon_Cod...  \n",
       "6  http://freeresultsguide.com/Top_10_Web_Hosting...  \n",
       "7  http://freeresultsguide.com/Web_Hosting_Offers...  \n",
       "8  http://freeresultsguide.com/Domain_Name_Regist...  \n",
       "9  http://freeresultsguide.com/Top_10_Domain_Host...  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#3399ff\">Draw a graph</font>\n",
    "* Finally, use the aforementioned DataFrame to visualize an internal link structure by feeding it to the Networkx method from_pandas_edgelist first and draw it by calling nx.draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ash = nx.from_pandas_edgelist(df, source='from', target='to')\n",
    "nx.draw(ash, with_labels=False, node_color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#ff3366\">Creating Model</font>\n",
    "* Using CountVectorizer is used to transform a corpora of text to a vector of term / token counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create sparse matrix of words using regexptokenizes: \n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we create a CV object:\n",
    "CV = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform all text which we tokenize and stemed:\n",
    "feature = CV.fit_transform(phishing_data.text_to_sent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert sparse matrix into array to print transformed features:\n",
    "feature[:5].toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting the data between feature and target:\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# gives whole report about metrics (e.g, recall,precision,f1_score,c_m):\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# gives info about actual and predicted:\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data:\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(feature, phishing_data.Label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#e6e600\">Logistic Regression</font>\n",
    "* Logistic Regression is a Machine Learning classification algorithm that is used to predict the probability of a categorical dependent variable. In logistic regression, the dependent variable is a binary variable that contains data coded as 1 (yes, success, etc.) or 0 (no, failure, etc.). In other words, the logistic regression model predicts P(Y=1) as a function of X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# algo use to predict not phishing site or phishing site: \n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an object for Logistic Regression()\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashhar\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9635608386317673"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we are calculating the score of tests:\n",
    "lr.score(test_X, test_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#00ffaa\">LR Score</font>\n",
    "* Logistic Regression is giving us 96% accuracy, Now we will store scores in dict to see which model perform best of all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Score_ml = {}\n",
    "Score_ml['Logistic Regression'] = np.round(lr.score(test_X, test_Y), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.9775064018002638\n",
      "Testing Accuracy:  0.9635608386317673\n",
      "\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Bad       0.90      0.97      0.93     36560\n",
      "        Good       0.99      0.96      0.97    100902\n",
      "\n",
      "    accuracy                           0.96    137462\n",
      "   macro avg       0.95      0.96      0.95    137462\n",
      "weighted avg       0.97      0.96      0.96    137462\n",
      "\n",
      "\n",
      "confusion Matrix: \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13aa9d98eb0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Training Accuracy: ',lr.score(train_X, train_Y))\n",
    "print('Testing Accuracy: ',lr.score(test_X, test_Y))\n",
    "# here we create confusion matrix:\n",
    "conf_mat = pd.DataFrame(confusion_matrix(lr.predict(test_X), test_Y),\n",
    "                       columns = ['Predicted: Phishing', 'Predicted: Not Phishing'],\n",
    "                       index = ['Actual: Phishing', 'Actual: Not Phishing'])\n",
    "\n",
    "print('\\nClassification Report: \\n')\n",
    "print(classification_report(lr.predict(test_X), test_Y,\n",
    "                           target_names = ['Bad', 'Good']))\n",
    "\n",
    "print('\\nconfusion Matrix: \\n')\n",
    "plt.figure(figsize = (6, 4))\n",
    "sns.heatmap(conf_mat, annot = True, fmt='d', cmap=\"RdYlBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#00bfff\">MultinomialNB</font>\n",
    "* Applying Multinomial Naive Bayes to NLP Problems. Naive Bayes Classifier Algorithm is a family of probabilistic algorithms based on applying Bayes' theorem with the “naive” assumption of conditional independence between every pair of a feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nlp algo use to predict not phishing site or phishing site:\n",
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create mnb object\n",
    "mnb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnb.fit(train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9569990251851421"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnb.score(test_X, test_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#ff5500\">MultinomialNB Score</font>\n",
    "* MultinomialNB is giving us 95% accuracy. So, now we will store scores in dict to see which model perform best of all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "Score_ml['MultinomialNB'] = np.round(mnb.score(test_X, test_Y), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy:  0.973949037790021\n",
      "Testing Accuracy:  0.9569990251851421\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Bad       0.91      0.93      0.92     38170\n",
      "        Good       0.97      0.97      0.97     99292\n",
      "\n",
      "    accuracy                           0.96    137462\n",
      "   macro avg       0.94      0.95      0.95    137462\n",
      "weighted avg       0.96      0.96      0.96    137462\n",
      "\n",
      "\n",
      "Confusion Matrix\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13aaf185fa0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Training Accuracy: ',mnb.score(train_X, train_Y))\n",
    "print('Testing Accuracy: ',mnb.score(test_X, test_Y))\n",
    "\n",
    "conf_mat = pd.DataFrame(confusion_matrix(mnb.predict(test_X), test_Y),\n",
    "                       columns = ['Predicted: Phishing', 'Predicted: Not Phishing'],\n",
    "                       index = ['Actual: Phishing', 'Actual: Not Phishing'])\n",
    "\n",
    "print('\\nClassification Report\\n')\n",
    "print(classification_report(mnb.predict(test_X), test_Y,\n",
    "                           target_names = ['Bad', 'Good']))\n",
    "\n",
    "print('\\nConfusion Matrix\\n')\n",
    "plt.figure(figsize = (6,4))\n",
    "sns.heatmap(conf_mat, annot = True, fmt='d', cmap='PuBuGn_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Score of Logistic Regression: \n",
      "0.9635608386317673\n",
      "\n",
      "Actual Score of MultinomialNB: \n",
      "0.9569990251851421\n",
      "\n",
      "Final Rounded Score: \n",
      "                     Accuracy\n",
      "Logistic Regression      0.96\n",
      "MultinomialNB            0.96\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13aaee240d0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame.from_dict(Score_ml, \n",
    "                                 orient = 'index', \n",
    "                                 columns = ['Accuracy'])\n",
    "\n",
    "print(f\"Actual Score of Logistic Regression: \\n{lr.score(test_X, test_Y)}\\n\")\n",
    "print(f\"Actual Score of MultinomialNB: \\n{mnb.score(test_X, test_Y)}\\n\")\n",
    "print(f\"Final Rounded Score: \\n{results}\")\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "sns.barplot(results.index, results.Accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"#ff5500\">Best fit Model</font>\n",
    "* In the above results we can clearly see that the Logistic Regression is the best fit model with actual score of 96%.\n",
    "* So, now we make sklearn pipeline using Logistic Regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Used for combining all preprocessor techniques and algorithms:\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a pipeline:\n",
    "pipeline_ls = make_pipeline(CountVectorizer(tokenizer = RegexpTokenizer(r'[A-Za-z]+').tokenize, stop_words='english'), LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_Y, test_Y = train_test_split(phishing_data.URL, phishing_data.Label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashhar\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('countvectorizer',\n",
       "                 CountVectorizer(stop_words='english',\n",
       "                                 tokenizer=<bound method RegexpTokenizer.tokenize of RegexpTokenizer(pattern='[A-Za-z]+', gaps=False, discard_empty=True, flags=re.UNICODE|re.MULTILINE|re.DOTALL)>)),\n",
       "                ('logisticregression', LogisticRegression())])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_ls.fit(train_X, train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9659760515633411"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_ls.score(test_X, test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traing Accuracy:  0.9807170016295491\n",
      "Testing Accuracy:  0.9659760515633411\n",
      "\n",
      "Classification Report \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Bad       0.91      0.97      0.94     36834\n",
      "        Good       0.99      0.97      0.98    100628\n",
      "\n",
      "    accuracy                           0.97    137462\n",
      "   macro avg       0.95      0.97      0.96    137462\n",
      "weighted avg       0.97      0.97      0.97    137462\n",
      "\n",
      "\n",
      "Confusion Matrix \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13aa60ecca0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Traing Accuracy: \",pipeline_ls.score(train_X, train_Y))\n",
    "print(\"Testing Accuracy: \",pipeline_ls.score(test_X, test_Y))\n",
    "\n",
    "conf_mat = pd.DataFrame(confusion_matrix(pipeline_ls.predict(test_X), test_Y), \n",
    "                       columns = [\"Predicted: Phishing\", \"Predicted: Not Phishing\"],\n",
    "                       index = [\"Actual: Phishing\", \"Actual: Not Phishing\"])\n",
    "\n",
    "print(\"\\nClassification Report \\n\")\n",
    "print(classification_report(pipeline_ls.predict(test_X), test_Y,\n",
    "                            target_names = ['Bad', 'Good']))\n",
    "\n",
    "print(\"\\nConfusion Matrix \\n\")\n",
    "plt.figure(figsize = (6,4))\n",
    "sns.heatmap(conf_mat, annot = True, fmt = 'd', cmap=\"Blues\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So now we dump our model using pickle:\n",
    "import pickle\n",
    "# Python's pickle module is for: it serializes objects so they can be saved to a file, and loaded in a program again later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_io.BufferedReader name='phishing.pkl'>\n",
      "\n",
      "Pipeline(steps=[('countvectorizer',\n",
      "                 CountVectorizer(stop_words='english',\n",
      "                                 tokenizer=<bound method RegexpTokenizer.tokenize of RegexpTokenizer(pattern='[A-Za-z]+', gaps=False, discard_empty=True, flags=re.UNICODE|re.MULTILINE|re.DOTALL)>)),\n",
      "                ('logisticregression', LogisticRegression())])\n"
     ]
    }
   ],
   "source": [
    "op = open('phishing.pkl', 'rb')\n",
    "# Here, the 'r' stands for read mode and the 'b' stands for binary mode.\n",
    "\n",
    "dicts = pickle.load(op, encoding = 'UTF8')\n",
    "op.close()\n",
    "print(f\"{op}\\n\")\n",
    "print(dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(pipeline_ls, open('phishing.pkl', 'wb'))\n",
    "# Here, The 'w' means that you'll be writing to the file, and 'b' refers to binary mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9659760515633411\n"
     ]
    }
   ],
   "source": [
    "loaded_model = pickle.load(open('phishing.pkl', 'rb'))\n",
    "result = loaded_model.score(test_X, test_Y)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=\"#009999\">🙌Conclusion</font>\n",
    "* That’s it. See, it's that simple yet so effective. We get an accuracy of 96%. That’s a very high value for a machine to be able to detect a malicious URL with. Want to test some links to see if the model gives good predictions? Sure. Let's do it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <font color=\"#ff0000\">❌ Bad Links</font> \n",
    "* __These are some Phishing links!__\n",
    "   1. www.yeniik.com.tr/wp-admin/js/login.alibaba.com/login.jsp.php\n",
    "   2. www.fazan-pacir.rs/temp/libraries/ipad\n",
    "   3. www.tubemoviez.exe\n",
    "   4. www.svision-online.de/mgfi/administrator/components/com_babackup/classes/fx29id1.txt\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### <font color=\"#77ff33\">✔ Good Links</font>\n",
    "* __These are some not Phishing links!__\n",
    "   1. www.youtube.com/\n",
    "   2. www.python.org/\n",
    "   3. www.google.com/\n",
    "   4. www.kaggle.com/\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_bad = ['www.yeniik.com.tr/wp-admin/js/login.alibaba.com/login.jsp.php',\n",
    "               'www.fazan-pacir.rs/temp/libraries/ipad',\n",
    "               'www.tubemoviez.exe/',\n",
    "               'www.svision-online.de/mgfi/administrator/components/com_babackup/classes/fx29id1.txt']\n",
    "\n",
    "predict_good = ['www.youtube.com/',\n",
    "                'www.python.org/',\n",
    "                'www.google.com/',\n",
    "                'www.kaggle.com/']\n",
    "\n",
    "loaded_model = pickle.load(open('phishing.pkl', 'rb'))\n",
    "#predict_bad = vectorizers.transform(predict_bad)\n",
    "# predict_good = vectorizer.transform(predict_good)\n",
    "\n",
    "result_1 = loaded_model.predict(predict_bad)\n",
    "result_2 = loaded_model.predict(predict_good)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bad' 'bad' 'bad' 'bad'] \n",
      " -------------------------- \n",
      "['good' 'good' 'good' 'good']\n"
     ]
    }
   ],
   "source": [
    "print(f\"{result_1} \\n {'-'*26} \\n{result_2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Project Terminated Here... 😎`"
   ]
  }
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