main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phishing Website Detection by Machine Learning Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team Members"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sayantan Pal\n",
    "\n",
    "Saikat Dey\n",
    "\n",
    "Prajak Sen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common imports\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import sys\n",
    "import sklearn\n",
    "import numpy as np\n",
    "import os\n",
    "import tarfile\n",
    "import urllib.request\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.base import clone\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# CONSTANTS\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "VERSION = \"0.0.0\"\n",
    "ORG_TRAIN_DATASET_PATH = os.path.join(\n",
    "    PROJECT_ROOT_DIR, \"datasets\", \"Phising_Training_Dataset.csv\")\n",
    "\n",
    "ORG_TEST_DATASET_PATH = os.path.join(\n",
    "    PROJECT_ROOT_DIR, \"datasets\", \"Phising_Testing_Dataset.csv\")\n",
    "\n",
    "SUBMISSION_RESULTS_DIR = os.path.join(\n",
    "    PROJECT_ROOT_DIR, \"submission\")\n",
    "    \n",
    "SUBMISSION_RESULTS_PATH = os.path.join(\n",
    "    SUBMISSION_RESULTS_DIR, \"predictions.csv\")\n",
    "IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", VERSION)\n",
    "Y_LABEL = \"Result\"\n",
    "RANDOM_STATE: int = 42\n",
    "N_JOBS = 5\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "\n",
    "os.makedirs(IMAGES_PATH, exist_ok=True)\n",
    "os.makedirs(SUBMISSION_RESULTS_DIR, exist_ok=True)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "org_train_dataset = pd.read_csv(ORG_TRAIN_DATASET_PATH)\n",
    "org_test_dataset = pd.read_csv(ORG_TEST_DATASET_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 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",
       "\n",
       "   double_slash_redirecting  Prefix_Suffix  having_Sub_Domain  SSLfinal_State  \\\n",
       "0                        -1             -1                 -1              -1   \n",
       "1                         1             -1                  0               1   \n",
       "\n",
       "   Domain_registeration_length  ...  popUpWidnow  Iframe  age_of_domain  \\\n",
       "0                           -1  ...            1       1             -1   \n",
       "1                           -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",
       "\n",
       "   Statistical_report  Result  \n",
       "0                  -1      -1  \n",
       "1                   1      -1  \n",
       "\n",
       "[2 rows x 32 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_train_dataset.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</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>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",
       "      <td>...</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",
       "      <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",
       "      <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>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",
       "    </tr>\n",
       "    <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",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "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": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_train_dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>RightClick</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21338</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>21339</td>\n",
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       "      <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>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 31 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",
       "\n",
       "   double_slash_redirecting  Prefix_Suffix  having_Sub_Domain  SSLfinal_State  \\\n",
       "0                         1              1                 -1               1   \n",
       "1                         1             -1                  0              -1   \n",
       "\n",
       "   Domain_registeration_length  ...  RightClick  popUpWidnow  Iframe  \\\n",
       "0                           -1  ...           1            1       1   \n",
       "1                            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",
       "\n",
       "   Links_pointing_to_page  Statistical_report  \n",
       "0                       0                   1  \n",
       "1                       0                   1  \n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_test_dataset.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>RightClick</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22387.500000</td>\n",
       "      <td>0.341905</td>\n",
       "      <td>-0.622381</td>\n",
       "      <td>0.731429</td>\n",
       "      <td>0.662857</td>\n",
       "      <td>0.744762</td>\n",
       "      <td>-0.733333</td>\n",
       "      <td>0.030476</td>\n",
       "      <td>0.192857</td>\n",
       "      <td>-0.338095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.887619</td>\n",
       "      <td>0.641905</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.199048</td>\n",
       "      <td>0.402857</td>\n",
       "      <td>0.268095</td>\n",
       "      <td>-0.501905</td>\n",
       "      <td>0.760952</td>\n",
       "      <td>0.367143</td>\n",
       "      <td>0.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>606.362103</td>\n",
       "      <td>0.939958</td>\n",
       "      <td>0.776485</td>\n",
       "      <td>0.682080</td>\n",
       "      <td>0.748924</td>\n",
       "      <td>0.667489</td>\n",
       "      <td>0.680031</td>\n",
       "      <td>0.817289</td>\n",
       "      <td>0.926270</td>\n",
       "      <td>0.941336</td>\n",
       "      <td>...</td>\n",
       "      <td>0.460688</td>\n",
       "      <td>0.766967</td>\n",
       "      <td>0.647843</td>\n",
       "      <td>0.980223</td>\n",
       "      <td>0.915481</td>\n",
       "      <td>0.836877</td>\n",
       "      <td>0.865129</td>\n",
       "      <td>0.648962</td>\n",
       "      <td>0.542581</td>\n",
       "      <td>0.733387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21338.000000</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",
       "      <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>25%</th>\n",
       "      <td>21862.750000</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>...</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22387.500000</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</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>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22912.250000</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>...</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>23437.000000</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",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                key    having_IP   URL_Length  Shortining_Service  \\\n",
       "count   2100.000000  2100.000000  2100.000000         2100.000000   \n",
       "mean   22387.500000     0.341905    -0.622381            0.731429   \n",
       "std      606.362103     0.939958     0.776485            0.682080   \n",
       "min    21338.000000    -1.000000    -1.000000           -1.000000   \n",
       "25%    21862.750000    -1.000000    -1.000000            1.000000   \n",
       "50%    22387.500000     1.000000    -1.000000            1.000000   \n",
       "75%    22912.250000     1.000000    -1.000000            1.000000   \n",
       "max    23437.000000     1.000000     1.000000            1.000000   \n",
       "\n",
       "       having_At_Symbol  double_slash_redirecting  Prefix_Suffix  \\\n",
       "count       2100.000000               2100.000000    2100.000000   \n",
       "mean           0.662857                  0.744762      -0.733333   \n",
       "std            0.748924                  0.667489       0.680031   \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        2100.000000     2100.000000                  2100.000000  ...   \n",
       "mean            0.030476        0.192857                    -0.338095  ...   \n",
       "std             0.817289        0.926270                     0.941336  ...   \n",
       "min            -1.000000       -1.000000                    -1.000000  ...   \n",
       "25%            -1.000000       -1.000000                    -1.000000  ...   \n",
       "50%             0.000000        1.000000                    -1.000000  ...   \n",
       "75%             1.000000        1.000000                     1.000000  ...   \n",
       "max             1.000000        1.000000                     1.000000  ...   \n",
       "\n",
       "        RightClick  popUpWidnow       Iframe  age_of_domain    DNSRecord  \\\n",
       "count  2100.000000  2100.000000  2100.000000    2100.000000  2100.000000   \n",
       "mean      0.887619     0.641905     0.761905       0.199048     0.402857   \n",
       "std       0.460688     0.766967     0.647843       0.980223     0.915481   \n",
       "min      -1.000000    -1.000000    -1.000000      -1.000000    -1.000000   \n",
       "25%       1.000000     1.000000     1.000000      -1.000000    -1.000000   \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",
       "       web_traffic    Page_Rank  Google_Index  Links_pointing_to_page  \\\n",
       "count  2100.000000  2100.000000   2100.000000             2100.000000   \n",
       "mean      0.268095    -0.501905      0.760952                0.367143   \n",
       "std       0.836877     0.865129      0.648962                0.542581   \n",
       "min      -1.000000    -1.000000     -1.000000               -1.000000   \n",
       "25%      -1.000000    -1.000000      1.000000                0.000000   \n",
       "50%       1.000000    -1.000000      1.000000                0.000000   \n",
       "75%       1.000000    -1.000000      1.000000                1.000000   \n",
       "max       1.000000     1.000000      1.000000                1.000000   \n",
       "\n",
       "       Statistical_report  \n",
       "count         2100.000000  \n",
       "mean             0.680000  \n",
       "std              0.733387  \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 31 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_test_dataset.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Dataset for NULL values, or empty cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has null values:  False\n",
      "Testing set has null values:  False\n"
     ]
    }
   ],
   "source": [
    "org_train_has_null: bool = bool(org_train_dataset.isnull().values.sum())\n",
    "org_test_has_null: bool = bool(org_test_dataset.isnull().values.sum())\n",
    "\n",
    "print(\"Training set has null values: \", org_train_has_null)\n",
    "print(\"Testing set has null values: \", org_test_has_null)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Dataset Shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Train Dataset Shape:  (8955, 32)\n",
      "Original Test Dataset Shape:  (2100, 31)\n"
     ]
    }
   ],
   "source": [
    "org_train_set_shape = org_train_dataset.shape\n",
    "org_test_set_shape = org_test_dataset.shape\n",
    "\n",
    "print(\"Original Train Dataset Shape: \", org_train_set_shape)\n",
    "print(\"Original Test Dataset Shape: \", org_test_set_shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types of value of Y LABEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    5034\n",
       "-1    3921\n",
       "Name: Result, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "org_train_dataset[Y_LABEL].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics Vizualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix = org_train_dataset.corr()\n",
    "#plt.matshow(corr_matrix)\n",
    "#plt.show()\n",
    "plt.figure(figsize=(15, 13))\n",
    "sns.heatmap(corr_matrix)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Value Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saika\\AppData\\Local\\Temp\\ipykernel_15072\\277405862.py:8: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n",
      "  null_color='#f1f1f1').set_precision(2)\n"
     ]
    },
    {
     "data": {
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       "#T_e43d9_row0_col0, #T_e43d9_row0_col1, #T_e43d9_row0_col2, #T_e43d9_row0_col3, #T_e43d9_row0_col4, #T_e43d9_row0_col5, #T_e43d9_row0_col6, #T_e43d9_row0_col7, #T_e43d9_row0_col8, #T_e43d9_row0_col9, #T_e43d9_row0_col10, #T_e43d9_row0_col11, #T_e43d9_row0_col12, #T_e43d9_row0_col13, #T_e43d9_row0_col14, #T_e43d9_row0_col15, #T_e43d9_row0_col16, #T_e43d9_row0_col17, #T_e43d9_row0_col18, #T_e43d9_row0_col19, #T_e43d9_row0_col20, #T_e43d9_row0_col21, #T_e43d9_row0_col22, #T_e43d9_row0_col23, #T_e43d9_row0_col24, #T_e43d9_row0_col25, #T_e43d9_row0_col26, #T_e43d9_row0_col27, #T_e43d9_row0_col28, #T_e43d9_row0_col29, #T_e43d9_row0_col30, #T_e43d9_row0_col31, #T_e43d9_row1_col1, #T_e43d9_row1_col2, #T_e43d9_row1_col3, #T_e43d9_row1_col4, #T_e43d9_row1_col5, #T_e43d9_row1_col6, #T_e43d9_row1_col7, #T_e43d9_row1_col8, #T_e43d9_row1_col9, #T_e43d9_row1_col10, #T_e43d9_row1_col11, #T_e43d9_row1_col12, #T_e43d9_row1_col13, #T_e43d9_row1_col14, #T_e43d9_row1_col15, #T_e43d9_row1_col16, #T_e43d9_row1_col17, #T_e43d9_row1_col18, #T_e43d9_row1_col19, #T_e43d9_row1_col20, #T_e43d9_row1_col21, #T_e43d9_row1_col22, #T_e43d9_row1_col23, #T_e43d9_row1_col24, #T_e43d9_row1_col25, #T_e43d9_row1_col26, #T_e43d9_row1_col27, #T_e43d9_row1_col28, #T_e43d9_row1_col29, #T_e43d9_row1_col30, #T_e43d9_row1_col31, #T_e43d9_row2_col2, #T_e43d9_row2_col3, #T_e43d9_row2_col4, #T_e43d9_row2_col5, #T_e43d9_row2_col6, #T_e43d9_row2_col7, #T_e43d9_row2_col8, #T_e43d9_row2_col9, #T_e43d9_row2_col10, #T_e43d9_row2_col11, #T_e43d9_row2_col12, #T_e43d9_row2_col13, #T_e43d9_row2_col14, #T_e43d9_row2_col15, #T_e43d9_row2_col16, #T_e43d9_row2_col17, #T_e43d9_row2_col18, #T_e43d9_row2_col19, #T_e43d9_row2_col20, #T_e43d9_row2_col21, #T_e43d9_row2_col22, #T_e43d9_row2_col23, #T_e43d9_row2_col24, #T_e43d9_row2_col25, #T_e43d9_row2_col26, #T_e43d9_row2_col27, #T_e43d9_row2_col28, #T_e43d9_row2_col29, #T_e43d9_row2_col30, #T_e43d9_row2_col31, #T_e43d9_row3_col3, #T_e43d9_row3_col4, #T_e43d9_row3_col5, #T_e43d9_row3_col6, #T_e43d9_row3_col7, #T_e43d9_row3_col8, #T_e43d9_row3_col9, #T_e43d9_row3_col10, #T_e43d9_row3_col11, #T_e43d9_row3_col12, #T_e43d9_row3_col13, #T_e43d9_row3_col14, #T_e43d9_row3_col15, #T_e43d9_row3_col16, #T_e43d9_row3_col17, #T_e43d9_row3_col18, #T_e43d9_row3_col19, #T_e43d9_row3_col20, #T_e43d9_row3_col21, #T_e43d9_row3_col22, #T_e43d9_row3_col23, #T_e43d9_row3_col24, #T_e43d9_row3_col25, #T_e43d9_row3_col26, #T_e43d9_row3_col27, #T_e43d9_row3_col28, #T_e43d9_row3_col29, #T_e43d9_row3_col30, #T_e43d9_row3_col31, #T_e43d9_row4_col4, #T_e43d9_row4_col5, #T_e43d9_row4_col6, #T_e43d9_row4_col7, #T_e43d9_row4_col8, #T_e43d9_row4_col9, #T_e43d9_row4_col10, #T_e43d9_row4_col11, #T_e43d9_row4_col12, #T_e43d9_row4_col13, #T_e43d9_row4_col14, #T_e43d9_row4_col15, #T_e43d9_row4_col16, #T_e43d9_row4_col17, #T_e43d9_row4_col18, #T_e43d9_row4_col19, #T_e43d9_row4_col20, #T_e43d9_row4_col21, #T_e43d9_row4_col22, #T_e43d9_row4_col23, #T_e43d9_row4_col24, #T_e43d9_row4_col25, #T_e43d9_row4_col26, #T_e43d9_row4_col27, #T_e43d9_row4_col28, #T_e43d9_row4_col29, #T_e43d9_row4_col30, #T_e43d9_row4_col31, #T_e43d9_row5_col5, #T_e43d9_row5_col6, #T_e43d9_row5_col7, #T_e43d9_row5_col8, #T_e43d9_row5_col9, #T_e43d9_row5_col10, #T_e43d9_row5_col11, #T_e43d9_row5_col12, #T_e43d9_row5_col13, #T_e43d9_row5_col14, #T_e43d9_row5_col15, #T_e43d9_row5_col16, #T_e43d9_row5_col17, #T_e43d9_row5_col18, #T_e43d9_row5_col19, #T_e43d9_row5_col20, #T_e43d9_row5_col21, #T_e43d9_row5_col22, #T_e43d9_row5_col23, #T_e43d9_row5_col24, #T_e43d9_row5_col25, #T_e43d9_row5_col26, #T_e43d9_row5_col27, #T_e43d9_row5_col28, #T_e43d9_row5_col29, #T_e43d9_row5_col30, #T_e43d9_row5_col31, #T_e43d9_row6_col6, #T_e43d9_row6_col7, #T_e43d9_row6_col8, #T_e43d9_row6_col9, #T_e43d9_row6_col10, #T_e43d9_row6_col11, #T_e43d9_row6_col12, #T_e43d9_row6_col13, #T_e43d9_row6_col14, #T_e43d9_row6_col15, #T_e43d9_row6_col16, #T_e43d9_row6_col17, #T_e43d9_row6_col18, #T_e43d9_row6_col19, #T_e43d9_row6_col20, #T_e43d9_row6_col21, #T_e43d9_row6_col22, #T_e43d9_row6_col23, #T_e43d9_row6_col24, #T_e43d9_row6_col25, #T_e43d9_row6_col26, #T_e43d9_row6_col27, #T_e43d9_row6_col28, #T_e43d9_row6_col29, #T_e43d9_row6_col30, #T_e43d9_row6_col31, #T_e43d9_row7_col7, #T_e43d9_row7_col8, #T_e43d9_row7_col9, #T_e43d9_row7_col10, #T_e43d9_row7_col11, #T_e43d9_row7_col12, #T_e43d9_row7_col13, #T_e43d9_row7_col14, #T_e43d9_row7_col15, #T_e43d9_row7_col16, #T_e43d9_row7_col17, #T_e43d9_row7_col18, #T_e43d9_row7_col19, #T_e43d9_row7_col20, #T_e43d9_row7_col21, #T_e43d9_row7_col22, #T_e43d9_row7_col23, #T_e43d9_row7_col24, #T_e43d9_row7_col25, #T_e43d9_row7_col26, #T_e43d9_row7_col27, #T_e43d9_row7_col28, #T_e43d9_row7_col29, #T_e43d9_row7_col30, #T_e43d9_row7_col31, #T_e43d9_row8_col8, #T_e43d9_row8_col9, #T_e43d9_row8_col10, #T_e43d9_row8_col11, #T_e43d9_row8_col12, #T_e43d9_row8_col13, #T_e43d9_row8_col14, #T_e43d9_row8_col15, #T_e43d9_row8_col16, #T_e43d9_row8_col17, #T_e43d9_row8_col18, #T_e43d9_row8_col19, #T_e43d9_row8_col20, #T_e43d9_row8_col21, #T_e43d9_row8_col22, #T_e43d9_row8_col23, #T_e43d9_row8_col24, #T_e43d9_row8_col25, #T_e43d9_row8_col26, #T_e43d9_row8_col27, #T_e43d9_row8_col28, #T_e43d9_row8_col29, #T_e43d9_row8_col30, #T_e43d9_row8_col31, #T_e43d9_row9_col9, #T_e43d9_row9_col10, #T_e43d9_row9_col11, #T_e43d9_row9_col12, #T_e43d9_row9_col13, #T_e43d9_row9_col14, #T_e43d9_row9_col15, #T_e43d9_row9_col16, #T_e43d9_row9_col17, #T_e43d9_row9_col18, #T_e43d9_row9_col19, #T_e43d9_row9_col20, #T_e43d9_row9_col21, #T_e43d9_row9_col22, #T_e43d9_row9_col23, #T_e43d9_row9_col24, #T_e43d9_row9_col25, #T_e43d9_row9_col26, #T_e43d9_row9_col27, #T_e43d9_row9_col28, #T_e43d9_row9_col29, #T_e43d9_row9_col30, #T_e43d9_row9_col31, #T_e43d9_row10_col10, #T_e43d9_row10_col11, #T_e43d9_row10_col12, #T_e43d9_row10_col13, #T_e43d9_row10_col14, #T_e43d9_row10_col15, #T_e43d9_row10_col16, #T_e43d9_row10_col17, #T_e43d9_row10_col18, #T_e43d9_row10_col19, #T_e43d9_row10_col20, #T_e43d9_row10_col21, #T_e43d9_row10_col22, #T_e43d9_row10_col23, #T_e43d9_row10_col24, #T_e43d9_row10_col25, #T_e43d9_row10_col26, #T_e43d9_row10_col27, #T_e43d9_row10_col28, #T_e43d9_row10_col29, #T_e43d9_row10_col30, #T_e43d9_row10_col31, #T_e43d9_row11_col11, #T_e43d9_row11_col12, #T_e43d9_row11_col13, #T_e43d9_row11_col14, #T_e43d9_row11_col15, #T_e43d9_row11_col16, #T_e43d9_row11_col17, #T_e43d9_row11_col18, #T_e43d9_row11_col19, #T_e43d9_row11_col20, #T_e43d9_row11_col21, #T_e43d9_row11_col22, #T_e43d9_row11_col23, #T_e43d9_row11_col24, #T_e43d9_row11_col25, #T_e43d9_row11_col26, #T_e43d9_row11_col27, #T_e43d9_row11_col28, #T_e43d9_row11_col29, #T_e43d9_row11_col30, #T_e43d9_row11_col31, #T_e43d9_row12_col12, #T_e43d9_row12_col13, #T_e43d9_row12_col14, #T_e43d9_row12_col15, #T_e43d9_row12_col16, #T_e43d9_row12_col17, #T_e43d9_row12_col18, #T_e43d9_row12_col19, #T_e43d9_row12_col20, #T_e43d9_row12_col21, #T_e43d9_row12_col22, #T_e43d9_row12_col23, #T_e43d9_row12_col24, #T_e43d9_row12_col25, #T_e43d9_row12_col26, #T_e43d9_row12_col27, #T_e43d9_row12_col28, #T_e43d9_row12_col29, #T_e43d9_row12_col30, #T_e43d9_row12_col31, #T_e43d9_row13_col13, #T_e43d9_row13_col14, #T_e43d9_row13_col15, #T_e43d9_row13_col16, #T_e43d9_row13_col17, #T_e43d9_row13_col18, #T_e43d9_row13_col19, #T_e43d9_row13_col20, #T_e43d9_row13_col21, #T_e43d9_row13_col22, #T_e43d9_row13_col23, #T_e43d9_row13_col24, #T_e43d9_row13_col25, #T_e43d9_row13_col26, #T_e43d9_row13_col27, #T_e43d9_row13_col28, #T_e43d9_row13_col29, #T_e43d9_row13_col30, #T_e43d9_row13_col31, #T_e43d9_row14_col14, #T_e43d9_row14_col15, #T_e43d9_row14_col16, #T_e43d9_row14_col17, #T_e43d9_row14_col18, #T_e43d9_row14_col19, #T_e43d9_row14_col20, #T_e43d9_row14_col21, #T_e43d9_row14_col22, #T_e43d9_row14_col23, #T_e43d9_row14_col24, #T_e43d9_row14_col25, #T_e43d9_row14_col26, #T_e43d9_row14_col27, #T_e43d9_row14_col28, #T_e43d9_row14_col29, #T_e43d9_row14_col30, #T_e43d9_row14_col31, #T_e43d9_row15_col15, #T_e43d9_row15_col16, #T_e43d9_row15_col17, #T_e43d9_row15_col18, #T_e43d9_row15_col19, #T_e43d9_row15_col20, #T_e43d9_row15_col21, #T_e43d9_row15_col22, #T_e43d9_row15_col23, #T_e43d9_row15_col24, #T_e43d9_row15_col25, #T_e43d9_row15_col26, #T_e43d9_row15_col27, #T_e43d9_row15_col28, #T_e43d9_row15_col29, #T_e43d9_row15_col30, #T_e43d9_row15_col31, #T_e43d9_row16_col16, #T_e43d9_row16_col17, #T_e43d9_row16_col18, #T_e43d9_row16_col19, #T_e43d9_row16_col20, #T_e43d9_row16_col21, #T_e43d9_row16_col22, #T_e43d9_row16_col23, #T_e43d9_row16_col24, #T_e43d9_row16_col25, #T_e43d9_row16_col26, #T_e43d9_row16_col27, #T_e43d9_row16_col28, #T_e43d9_row16_col29, #T_e43d9_row16_col30, #T_e43d9_row16_col31, #T_e43d9_row17_col17, #T_e43d9_row17_col18, #T_e43d9_row17_col19, #T_e43d9_row17_col20, #T_e43d9_row17_col21, #T_e43d9_row17_col22, #T_e43d9_row17_col23, #T_e43d9_row17_col24, #T_e43d9_row17_col25, #T_e43d9_row17_col26, #T_e43d9_row17_col27, #T_e43d9_row17_col28, #T_e43d9_row17_col29, #T_e43d9_row17_col30, #T_e43d9_row17_col31, #T_e43d9_row18_col18, #T_e43d9_row18_col19, #T_e43d9_row18_col20, #T_e43d9_row18_col21, #T_e43d9_row18_col22, #T_e43d9_row18_col23, #T_e43d9_row18_col24, #T_e43d9_row18_col25, #T_e43d9_row18_col26, #T_e43d9_row18_col27, #T_e43d9_row18_col28, #T_e43d9_row18_col29, #T_e43d9_row18_col30, #T_e43d9_row18_col31, #T_e43d9_row19_col19, #T_e43d9_row19_col20, #T_e43d9_row19_col21, #T_e43d9_row19_col22, #T_e43d9_row19_col23, #T_e43d9_row19_col24, #T_e43d9_row19_col25, #T_e43d9_row19_col26, #T_e43d9_row19_col27, #T_e43d9_row19_col28, #T_e43d9_row19_col29, #T_e43d9_row19_col30, #T_e43d9_row19_col31, #T_e43d9_row20_col20, #T_e43d9_row20_col21, #T_e43d9_row20_col22, #T_e43d9_row20_col23, #T_e43d9_row20_col24, #T_e43d9_row20_col25, #T_e43d9_row20_col26, #T_e43d9_row20_col27, #T_e43d9_row20_col28, #T_e43d9_row20_col29, #T_e43d9_row20_col30, #T_e43d9_row20_col31, #T_e43d9_row21_col21, #T_e43d9_row21_col22, #T_e43d9_row21_col23, #T_e43d9_row21_col24, #T_e43d9_row21_col25, #T_e43d9_row21_col26, #T_e43d9_row21_col27, #T_e43d9_row21_col28, #T_e43d9_row21_col29, #T_e43d9_row21_col30, #T_e43d9_row21_col31, #T_e43d9_row22_col22, #T_e43d9_row22_col23, #T_e43d9_row22_col24, #T_e43d9_row22_col25, #T_e43d9_row22_col26, #T_e43d9_row22_col27, #T_e43d9_row22_col28, #T_e43d9_row22_col29, #T_e43d9_row22_col30, #T_e43d9_row22_col31, #T_e43d9_row23_col23, #T_e43d9_row23_col24, #T_e43d9_row23_col25, #T_e43d9_row23_col26, #T_e43d9_row23_col27, #T_e43d9_row23_col28, #T_e43d9_row23_col29, #T_e43d9_row23_col30, #T_e43d9_row23_col31, #T_e43d9_row24_col24, #T_e43d9_row24_col25, #T_e43d9_row24_col26, #T_e43d9_row24_col27, #T_e43d9_row24_col28, #T_e43d9_row24_col29, #T_e43d9_row24_col30, #T_e43d9_row24_col31, #T_e43d9_row25_col25, #T_e43d9_row25_col26, #T_e43d9_row25_col27, #T_e43d9_row25_col28, #T_e43d9_row25_col29, #T_e43d9_row25_col30, #T_e43d9_row25_col31, #T_e43d9_row26_col26, #T_e43d9_row26_col27, #T_e43d9_row26_col28, #T_e43d9_row26_col29, #T_e43d9_row26_col30, #T_e43d9_row26_col31, #T_e43d9_row27_col27, #T_e43d9_row27_col28, #T_e43d9_row27_col29, #T_e43d9_row27_col30, #T_e43d9_row27_col31, #T_e43d9_row28_col28, #T_e43d9_row28_col29, #T_e43d9_row28_col30, #T_e43d9_row28_col31, #T_e43d9_row29_col29, #T_e43d9_row29_col30, #T_e43d9_row29_col31, #T_e43d9_row30_col30, #T_e43d9_row30_col31, #T_e43d9_row31_col31 {\n",
       "  background-color: #000000;\n",
       "  color: #f1f1f1;\n",
       "  background-color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row1_col0 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row2_col0, #T_e43d9_row5_col0, #T_e43d9_row10_col3, #T_e43d9_row11_col7, #T_e43d9_row13_col12, #T_e43d9_row14_col0, #T_e43d9_row14_col3, #T_e43d9_row16_col6, #T_e43d9_row16_col11, #T_e43d9_row17_col0, #T_e43d9_row17_col8, #T_e43d9_row19_col13, #T_e43d9_row20_col13, #T_e43d9_row20_col16, #T_e43d9_row21_col8, #T_e43d9_row21_col16, #T_e43d9_row23_col7, #T_e43d9_row23_col16, #T_e43d9_row24_col20, #T_e43d9_row28_col2, #T_e43d9_row28_col11, #T_e43d9_row28_col20, #T_e43d9_row28_col21, #T_e43d9_row28_col23, #T_e43d9_row30_col9, #T_e43d9_row30_col26, #T_e43d9_row31_col21 {\n",
       "  background-color: #dddcdc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row2_col1, #T_e43d9_row16_col5, #T_e43d9_row17_col6, #T_e43d9_row20_col2, #T_e43d9_row24_col3, #T_e43d9_row24_col5, #T_e43d9_row26_col10, #T_e43d9_row27_col4, #T_e43d9_row27_col9, #T_e43d9_row29_col28 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row3_col0, #T_e43d9_row6_col0, #T_e43d9_row7_col2, #T_e43d9_row9_col0, #T_e43d9_row10_col6, #T_e43d9_row11_col2, #T_e43d9_row11_col3, #T_e43d9_row12_col11, #T_e43d9_row13_col0, #T_e43d9_row13_col10, #T_e43d9_row14_col5, #T_e43d9_row15_col1, #T_e43d9_row19_col9, #T_e43d9_row19_col14, #T_e43d9_row19_col17, #T_e43d9_row24_col10, #T_e43d9_row24_col11, #T_e43d9_row24_col17, #T_e43d9_row24_col21, #T_e43d9_row24_col22, #T_e43d9_row26_col7, #T_e43d9_row27_col16, #T_e43d9_row28_col17, #T_e43d9_row28_col22, #T_e43d9_row29_col7, #T_e43d9_row29_col8, #T_e43d9_row29_col16, #T_e43d9_row30_col29, #T_e43d9_row31_col0, #T_e43d9_row31_col10, #T_e43d9_row31_col22 {\n",
       "  background-color: #dcdddd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row3_col1, #T_e43d9_row16_col2, #T_e43d9_row21_col10, #T_e43d9_row22_col21 {\n",
       "  background-color: #f7ac8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row3_col2, #T_e43d9_row15_col12 {\n",
       "  background-color: #cedaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row4_col0 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row4_col1, #T_e43d9_row27_col2, #T_e43d9_row28_col25 {\n",
       "  background-color: #efcfbf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row4_col2, #T_e43d9_row23_col15, #T_e43d9_row30_col19, #T_e43d9_row31_col3 {\n",
       "  background-color: #d4dbe6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row4_col3, #T_e43d9_row16_col14, #T_e43d9_row20_col12, #T_e43d9_row24_col7, #T_e43d9_row26_col6, #T_e43d9_row30_col12 {\n",
       "  background-color: #ead4c8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row5_col1, #T_e43d9_row11_col4, #T_e43d9_row17_col4 {\n",
       "  background-color: #f7af91;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row5_col2, #T_e43d9_row7_col1, #T_e43d9_row15_col4 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row5_col3 {\n",
       "  background-color: #d0473d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row5_col4, #T_e43d9_row7_col6, #T_e43d9_row10_col1, #T_e43d9_row15_col7, #T_e43d9_row16_col7, #T_e43d9_row24_col0, #T_e43d9_row24_col13, #T_e43d9_row24_col14, #T_e43d9_row25_col10, #T_e43d9_row27_col14, #T_e43d9_row28_col8, #T_e43d9_row30_col7 {\n",
       "  background-color: #e8d6cc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row6_col1, #T_e43d9_row6_col4, #T_e43d9_row8_col0, #T_e43d9_row11_col0, #T_e43d9_row16_col1, #T_e43d9_row16_col4, #T_e43d9_row16_col12, #T_e43d9_row18_col14, #T_e43d9_row22_col6, #T_e43d9_row22_col13, #T_e43d9_row22_col16, #T_e43d9_row23_col8, #T_e43d9_row23_col19, #T_e43d9_row24_col4, #T_e43d9_row25_col6, #T_e43d9_row26_col17, #T_e43d9_row26_col21, #T_e43d9_row27_col6, #T_e43d9_row27_col15, #T_e43d9_row28_col0, #T_e43d9_row28_col10, #T_e43d9_row28_col26, #T_e43d9_row30_col6, #T_e43d9_row30_col24, #T_e43d9_row30_col28, #T_e43d9_row31_col23 {\n",
       "  background-color: #dbdcde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row6_col2, #T_e43d9_row8_col2, #T_e43d9_row12_col9, #T_e43d9_row15_col2, #T_e43d9_row17_col3, #T_e43d9_row18_col9, #T_e43d9_row18_col11, #T_e43d9_row25_col8, #T_e43d9_row27_col13, #T_e43d9_row28_col4, #T_e43d9_row30_col8, #T_e43d9_row31_col4 {\n",
       "  background-color: #e3d9d3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row6_col3, #T_e43d9_row6_col5, #T_e43d9_row9_col7, #T_e43d9_row18_col6, #T_e43d9_row27_col1, #T_e43d9_row30_col4, #T_e43d9_row30_col15 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row7_col0, #T_e43d9_row14_col7, #T_e43d9_row30_col18 {\n",
       "  background-color: #f3c8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row7_col3, #T_e43d9_row7_col5, #T_e43d9_row8_col5, #T_e43d9_row13_col3, #T_e43d9_row17_col15, #T_e43d9_row18_col16, #T_e43d9_row21_col0, #T_e43d9_row23_col0, #T_e43d9_row23_col6, #T_e43d9_row24_col18, #T_e43d9_row25_col15, #T_e43d9_row25_col24, #T_e43d9_row26_col20, #T_e43d9_row26_col22, #T_e43d9_row28_col9, #T_e43d9_row29_col27, #T_e43d9_row31_col5, #T_e43d9_row31_col12 {\n",
       "  background-color: #d8dce2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row7_col4, #T_e43d9_row8_col3, #T_e43d9_row15_col11, #T_e43d9_row22_col2, #T_e43d9_row25_col1, #T_e43d9_row26_col3, #T_e43d9_row26_col18, #T_e43d9_row31_col18 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row8_col1, #T_e43d9_row17_col1, #T_e43d9_row17_col12, #T_e43d9_row18_col10, #T_e43d9_row20_col14, #T_e43d9_row23_col1, #T_e43d9_row24_col6, #T_e43d9_row25_col20, #T_e43d9_row27_col0, #T_e43d9_row27_col8, #T_e43d9_row30_col14 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row8_col4, #T_e43d9_row10_col5, #T_e43d9_row13_col1, #T_e43d9_row14_col10, #T_e43d9_row14_col11, #T_e43d9_row21_col3, #T_e43d9_row22_col3, #T_e43d9_row25_col16, #T_e43d9_row26_col4, #T_e43d9_row27_col17, #T_e43d9_row27_col26, #T_e43d9_row28_col14, #T_e43d9_row28_col27, #T_e43d9_row29_col24, #T_e43d9_row31_col11, #T_e43d9_row31_col29 {\n",
       "  background-color: #e1dad6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row8_col6 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row8_col7, #T_e43d9_row26_col8, #T_e43d9_row30_col10 {\n",
       "  background-color: #f5c1a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row9_col1, #T_e43d9_row11_col6, #T_e43d9_row12_col7, #T_e43d9_row14_col2, #T_e43d9_row16_col3, #T_e43d9_row18_col7, #T_e43d9_row18_col13, #T_e43d9_row19_col4, #T_e43d9_row19_col8, #T_e43d9_row20_col7, #T_e43d9_row21_col6, #T_e43d9_row22_col7, #T_e43d9_row22_col19, #T_e43d9_row24_col19, #T_e43d9_row26_col0, #T_e43d9_row26_col11, #T_e43d9_row28_col24, #T_e43d9_row29_col17, #T_e43d9_row29_col20, #T_e43d9_row29_col26, #T_e43d9_row31_col19 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row9_col2, #T_e43d9_row31_col9 {\n",
       "  background-color: #bcd2f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row9_col3, #T_e43d9_row10_col9, #T_e43d9_row14_col4, #T_e43d9_row19_col16, #T_e43d9_row21_col1, #T_e43d9_row22_col5, #T_e43d9_row22_col9, #T_e43d9_row22_col12, #T_e43d9_row25_col17, #T_e43d9_row28_col6, #T_e43d9_row28_col7, #T_e43d9_row28_col19, #T_e43d9_row31_col2 {\n",
       "  background-color: #e4d9d2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row9_col4, #T_e43d9_row12_col0, #T_e43d9_row17_col13, #T_e43d9_row19_col0, #T_e43d9_row19_col6, #T_e43d9_row20_col6, #T_e43d9_row20_col8, #T_e43d9_row21_col5, #T_e43d9_row21_col14, #T_e43d9_row23_col3, #T_e43d9_row23_col12, #T_e43d9_row23_col18, #T_e43d9_row25_col13, #T_e43d9_row26_col2, #T_e43d9_row27_col11, #T_e43d9_row27_col18, #T_e43d9_row27_col21, #T_e43d9_row27_col23, #T_e43d9_row29_col0, #T_e43d9_row29_col14 {\n",
       "  background-color: #dfdbd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row9_col5, #T_e43d9_row12_col10, #T_e43d9_row17_col9, #T_e43d9_row19_col2, #T_e43d9_row19_col15, #T_e43d9_row22_col14, #T_e43d9_row25_col11, #T_e43d9_row26_col25, #T_e43d9_row27_col19, #T_e43d9_row28_col1, #T_e43d9_row28_col13, #T_e43d9_row28_col15, #T_e43d9_row30_col27, #T_e43d9_row31_col20 {\n",
       "  background-color: #e2dad5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row9_col6, #T_e43d9_row12_col2, #T_e43d9_row15_col9, #T_e43d9_row15_col10 {\n",
       "  background-color: #cfdaea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row9_col8, #T_e43d9_row29_col3 {\n",
       "  background-color: #c1d4f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row10_col0, #T_e43d9_row14_col12, #T_e43d9_row17_col7, #T_e43d9_row17_col16, #T_e43d9_row18_col0, #T_e43d9_row21_col7, #T_e43d9_row21_col12, #T_e43d9_row22_col0, #T_e43d9_row23_col5, #T_e43d9_row23_col9, #T_e43d9_row23_col13, #T_e43d9_row23_col14, #T_e43d9_row24_col1, #T_e43d9_row24_col23, #T_e43d9_row26_col1, #T_e43d9_row26_col19, #T_e43d9_row27_col5, #T_e43d9_row27_col10, #T_e43d9_row27_col20, #T_e43d9_row27_col22, #T_e43d9_row29_col15, #T_e43d9_row31_col17 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row10_col2, #T_e43d9_row18_col8, #T_e43d9_row20_col19, #T_e43d9_row21_col15, #T_e43d9_row24_col12, #T_e43d9_row25_col2, #T_e43d9_row25_col4, #T_e43d9_row26_col12 {\n",
       "  background-color: #d7dce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row10_col4, #T_e43d9_row18_col1 {\n",
       "  background-color: #f7b99e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row10_col7, #T_e43d9_row10_col8, #T_e43d9_row12_col8, #T_e43d9_row13_col5, #T_e43d9_row15_col0, #T_e43d9_row16_col10, #T_e43d9_row17_col2, #T_e43d9_row19_col10, #T_e43d9_row19_col11, #T_e43d9_row20_col0, #T_e43d9_row21_col2, #T_e43d9_row21_col13, #T_e43d9_row21_col19, #T_e43d9_row22_col8, #T_e43d9_row23_col2, #T_e43d9_row24_col16, #T_e43d9_row25_col9, #T_e43d9_row26_col23, #T_e43d9_row29_col2, #T_e43d9_row29_col4, #T_e43d9_row30_col1, #T_e43d9_row30_col16 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row11_col1, #T_e43d9_row15_col13, #T_e43d9_row16_col15, #T_e43d9_row20_col3, #T_e43d9_row26_col15, #T_e43d9_row26_col16, #T_e43d9_row29_col6, #T_e43d9_row31_col30 {\n",
       "  background-color: #e5d8d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row11_col5, #T_e43d9_row11_col8, #T_e43d9_row11_col9, #T_e43d9_row13_col4, #T_e43d9_row13_col11, #T_e43d9_row17_col5, #T_e43d9_row17_col14, #T_e43d9_row19_col7, #T_e43d9_row20_col9, #T_e43d9_row21_col9, #T_e43d9_row21_col18, #T_e43d9_row25_col21, #T_e43d9_row25_col23, #T_e43d9_row27_col3, #T_e43d9_row27_col12, #T_e43d9_row28_col16, #T_e43d9_row30_col13 {\n",
       "  background-color: #e0dbd8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row11_col10, #T_e43d9_row17_col11 {\n",
       "  background-color: #d75445;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row12_col1, #T_e43d9_row14_col6, #T_e43d9_row31_col6 {\n",
       "  background-color: #f7b599;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row12_col3, #T_e43d9_row12_col5 {\n",
       "  background-color: #dd5f4b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row12_col4, #T_e43d9_row13_col7, #T_e43d9_row14_col1, #T_e43d9_row31_col27 {\n",
       "  background-color: #ead5c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row12_col6, #T_e43d9_row20_col15, #T_e43d9_row24_col9, #T_e43d9_row26_col5, #T_e43d9_row29_col13, #T_e43d9_row30_col2 {\n",
       "  background-color: #d3dbe7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row13_col2 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row13_col6, #T_e43d9_row15_col6, #T_e43d9_row20_col1, #T_e43d9_row20_col5, #T_e43d9_row22_col1, #T_e43d9_row22_col18, #T_e43d9_row25_col14, #T_e43d9_row25_col22, #T_e43d9_row30_col3, #T_e43d9_row31_col1 {\n",
       "  background-color: #e9d5cb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row13_col8, #T_e43d9_row28_col3, #T_e43d9_row30_col23 {\n",
       "  background-color: #f1ccb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row13_col9 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row14_col8, #T_e43d9_row23_col17 {\n",
       "  background-color: #f29072;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row14_col9 {\n",
       "  background-color: #c6d6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row14_col13 {\n",
       "  background-color: #f0cdbb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row15_col3, #T_e43d9_row16_col9, #T_e43d9_row26_col9, #T_e43d9_row29_col11, #T_e43d9_row29_col23 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row15_col5 {\n",
       "  background-color: #cbd8ee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row15_col8, #T_e43d9_row16_col8, #T_e43d9_row24_col8, #T_e43d9_row26_col13 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row15_col14, #T_e43d9_row20_col18, #T_e43d9_row25_col7, #T_e43d9_row27_col7, #T_e43d9_row29_col9 {\n",
       "  background-color: #ecd3c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row16_col0, #T_e43d9_row24_col15, #T_e43d9_row26_col24, #T_e43d9_row30_col5, #T_e43d9_row31_col25 {\n",
       "  background-color: #e7d7ce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row16_col13, #T_e43d9_row31_col28 {\n",
       "  background-color: #ebd3c6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row17_col10 {\n",
       "  background-color: #e97a5f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row18_col2, #T_e43d9_row18_col15, #T_e43d9_row22_col15, #T_e43d9_row29_col12 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row18_col3 {\n",
       "  background-color: #df634e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row18_col4, #T_e43d9_row28_col5, #T_e43d9_row30_col20, #T_e43d9_row31_col16 {\n",
       "  background-color: #f2c9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row18_col5, #T_e43d9_row22_col20, #T_e43d9_row31_col8 {\n",
       "  background-color: #e26952;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row18_col12 {\n",
       "  background-color: #e36b54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row18_col17 {\n",
       "  background-color: #f2cab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row19_col1 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row19_col3 {\n",
       "  background-color: #8db0fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row19_col5 {\n",
       "  background-color: #84a7fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row19_col12, #T_e43d9_row19_col18 {\n",
       "  background-color: #9bbcff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row20_col4, #T_e43d9_row22_col4, #T_e43d9_row30_col11 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row20_col10 {\n",
       "  background-color: #e57058;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row20_col11 {\n",
       "  background-color: #ee8669;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row20_col17 {\n",
       "  background-color: #f49a7b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row21_col4, #T_e43d9_row30_col22, #T_e43d9_row31_col13 {\n",
       "  background-color: #f5c4ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row21_col11 {\n",
       "  background-color: #f5a081;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row21_col17 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row21_col20 {\n",
       "  background-color: #f6a283;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row22_col10 {\n",
       "  background-color: #c0282f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row22_col11 {\n",
       "  background-color: #e0654f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row22_col17, #T_e43d9_row23_col10, #T_e43d9_row23_col22 {\n",
       "  background-color: #ed8366;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row23_col4 {\n",
       "  background-color: #f7ba9f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row23_col11 {\n",
       "  background-color: #e9785d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row23_col20 {\n",
       "  background-color: #ec7f63;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row23_col21 {\n",
       "  background-color: #e8765c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row24_col2 {\n",
       "  background-color: #f2cbb7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row25_col0 {\n",
       "  background-color: #f7a98b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row25_col3 {\n",
       "  background-color: #f6a586;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row25_col5 {\n",
       "  background-color: #f7a688;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row25_col12 {\n",
       "  background-color: #f7ad90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row25_col18 {\n",
       "  background-color: #f7b194;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row25_col19, #T_e43d9_row29_col5 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row26_col14 {\n",
       "  background-color: #f7b79b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row27_col24 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row27_col25, #T_e43d9_row28_col12, #T_e43d9_row29_col19 {\n",
       "  background-color: #edd1c2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row28_col18, #T_e43d9_row30_col21 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row29_col1 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row29_col10, #T_e43d9_row29_col21, #T_e43d9_row29_col22 {\n",
       "  background-color: #ccd9ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row29_col18 {\n",
       "  background-color: #c7d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row29_col25 {\n",
       "  background-color: #b1cbfc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row30_col0 {\n",
       "  background-color: #f1cdba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row30_col17 {\n",
       "  background-color: #f7b89c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row30_col25, #T_e43d9_row31_col24 {\n",
       "  background-color: #edd2c3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row31_col7 {\n",
       "  background-color: #f6bea4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row31_col14 {\n",
       "  background-color: #e36c55;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_e43d9_row31_col15 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_e43d9_row31_col26 {\n",
       "  background-color: #f7b497;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e43d9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e43d9_level0_col0\" class=\"col_heading level0 col0\" >key</th>\n",
       "      <th id=\"T_e43d9_level0_col1\" class=\"col_heading level0 col1\" >having_IP</th>\n",
       "      <th id=\"T_e43d9_level0_col2\" class=\"col_heading level0 col2\" >URL_Length</th>\n",
       "      <th id=\"T_e43d9_level0_col3\" class=\"col_heading level0 col3\" >Shortining_Service</th>\n",
       "      <th id=\"T_e43d9_level0_col4\" class=\"col_heading level0 col4\" >having_At_Symbol</th>\n",
       "      <th id=\"T_e43d9_level0_col5\" class=\"col_heading level0 col5\" >double_slash_redirecting</th>\n",
       "      <th id=\"T_e43d9_level0_col6\" class=\"col_heading level0 col6\" >Prefix_Suffix</th>\n",
       "      <th id=\"T_e43d9_level0_col7\" class=\"col_heading level0 col7\" >having_Sub_Domain</th>\n",
       "      <th id=\"T_e43d9_level0_col8\" class=\"col_heading level0 col8\" >SSLfinal_State</th>\n",
       "      <th id=\"T_e43d9_level0_col9\" class=\"col_heading level0 col9\" >Domain_registeration_length</th>\n",
       "      <th id=\"T_e43d9_level0_col10\" class=\"col_heading level0 col10\" >Favicon</th>\n",
       "      <th id=\"T_e43d9_level0_col11\" class=\"col_heading level0 col11\" >port</th>\n",
       "      <th id=\"T_e43d9_level0_col12\" class=\"col_heading level0 col12\" >HTTPS_token</th>\n",
       "      <th id=\"T_e43d9_level0_col13\" class=\"col_heading level0 col13\" >Request_URL</th>\n",
       "      <th id=\"T_e43d9_level0_col14\" class=\"col_heading level0 col14\" >URL_of_Anchor</th>\n",
       "      <th id=\"T_e43d9_level0_col15\" class=\"col_heading level0 col15\" >Links_in_tags</th>\n",
       "      <th id=\"T_e43d9_level0_col16\" class=\"col_heading level0 col16\" >SFH</th>\n",
       "      <th id=\"T_e43d9_level0_col17\" class=\"col_heading level0 col17\" >Submitting_to_email</th>\n",
       "      <th id=\"T_e43d9_level0_col18\" class=\"col_heading level0 col18\" >Abnormal_URL</th>\n",
       "      <th id=\"T_e43d9_level0_col19\" class=\"col_heading level0 col19\" >Redirect</th>\n",
       "      <th id=\"T_e43d9_level0_col20\" class=\"col_heading level0 col20\" >on_mouseover</th>\n",
       "      <th id=\"T_e43d9_level0_col21\" class=\"col_heading level0 col21\" >RightClick</th>\n",
       "      <th id=\"T_e43d9_level0_col22\" class=\"col_heading level0 col22\" >popUpWidnow</th>\n",
       "      <th id=\"T_e43d9_level0_col23\" class=\"col_heading level0 col23\" >Iframe</th>\n",
       "      <th id=\"T_e43d9_level0_col24\" class=\"col_heading level0 col24\" >age_of_domain</th>\n",
       "      <th id=\"T_e43d9_level0_col25\" class=\"col_heading level0 col25\" >DNSRecord</th>\n",
       "      <th id=\"T_e43d9_level0_col26\" class=\"col_heading level0 col26\" >web_traffic</th>\n",
       "      <th id=\"T_e43d9_level0_col27\" class=\"col_heading level0 col27\" >Page_Rank</th>\n",
       "      <th id=\"T_e43d9_level0_col28\" class=\"col_heading level0 col28\" >Google_Index</th>\n",
       "      <th id=\"T_e43d9_level0_col29\" class=\"col_heading level0 col29\" >Links_pointing_to_page</th>\n",
       "      <th id=\"T_e43d9_level0_col30\" class=\"col_heading level0 col30\" >Statistical_report</th>\n",
       "      <th id=\"T_e43d9_level0_col31\" class=\"col_heading level0 col31\" >Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row0\" class=\"row_heading level0 row0\" >key</th>\n",
       "      <td id=\"T_e43d9_row0_col0\" class=\"data row0 col0\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col4\" class=\"data row0 col4\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col5\" class=\"data row0 col5\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col6\" class=\"data row0 col6\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col7\" class=\"data row0 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col8\" class=\"data row0 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col9\" class=\"data row0 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col10\" class=\"data row0 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col11\" class=\"data row0 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col12\" class=\"data row0 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col13\" class=\"data row0 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col15\" class=\"data row0 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col16\" class=\"data row0 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col17\" class=\"data row0 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col18\" class=\"data row0 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col19\" class=\"data row0 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col20\" class=\"data row0 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col21\" class=\"data row0 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col22\" class=\"data row0 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col23\" class=\"data row0 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col24\" class=\"data row0 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col25\" class=\"data row0 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col26\" class=\"data row0 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col27\" class=\"data row0 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col28\" class=\"data row0 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col29\" class=\"data row0 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col30\" class=\"data row0 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row0_col31\" class=\"data row0 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row1\" class=\"row_heading level0 row1\" >having_IP</th>\n",
       "      <td id=\"T_e43d9_row1_col0\" class=\"data row1 col0\" >-0.38</td>\n",
       "      <td id=\"T_e43d9_row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col4\" class=\"data row1 col4\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col5\" class=\"data row1 col5\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col6\" class=\"data row1 col6\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col7\" class=\"data row1 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col9\" class=\"data row1 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col10\" class=\"data row1 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col11\" class=\"data row1 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col12\" class=\"data row1 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col13\" class=\"data row1 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col15\" class=\"data row1 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col16\" class=\"data row1 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col17\" class=\"data row1 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col18\" class=\"data row1 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col19\" class=\"data row1 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col20\" class=\"data row1 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col21\" class=\"data row1 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col22\" class=\"data row1 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col23\" class=\"data row1 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col24\" class=\"data row1 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col25\" class=\"data row1 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col26\" class=\"data row1 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col27\" class=\"data row1 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col28\" class=\"data row1 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col29\" class=\"data row1 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col30\" class=\"data row1 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row1_col31\" class=\"data row1 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row2\" class=\"row_heading level0 row2\" >URL_Length</th>\n",
       "      <td id=\"T_e43d9_row2_col0\" class=\"data row2 col0\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row2_col1\" class=\"data row2 col1\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col4\" class=\"data row2 col4\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col5\" class=\"data row2 col5\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col6\" class=\"data row2 col6\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col7\" class=\"data row2 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col8\" class=\"data row2 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col9\" class=\"data row2 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col10\" class=\"data row2 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col11\" class=\"data row2 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col12\" class=\"data row2 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col13\" class=\"data row2 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col15\" class=\"data row2 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col16\" class=\"data row2 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col17\" class=\"data row2 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col18\" class=\"data row2 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col19\" class=\"data row2 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col20\" class=\"data row2 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col21\" class=\"data row2 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col22\" class=\"data row2 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col23\" class=\"data row2 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col24\" class=\"data row2 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col25\" class=\"data row2 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col26\" class=\"data row2 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col27\" class=\"data row2 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col28\" class=\"data row2 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col29\" class=\"data row2 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col30\" class=\"data row2 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row2_col31\" class=\"data row2 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row3\" class=\"row_heading level0 row3\" >Shortining_Service</th>\n",
       "      <td id=\"T_e43d9_row3_col0\" class=\"data row3 col0\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row3_col1\" class=\"data row3 col1\" >0.40</td>\n",
       "      <td id=\"T_e43d9_row3_col2\" class=\"data row3 col2\" >-0.10</td>\n",
       "      <td id=\"T_e43d9_row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col4\" class=\"data row3 col4\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col5\" class=\"data row3 col5\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col6\" class=\"data row3 col6\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col7\" class=\"data row3 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col8\" class=\"data row3 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col9\" class=\"data row3 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col10\" class=\"data row3 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col11\" class=\"data row3 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col12\" class=\"data row3 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col13\" class=\"data row3 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col15\" class=\"data row3 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col16\" class=\"data row3 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col17\" class=\"data row3 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col18\" class=\"data row3 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col19\" class=\"data row3 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col20\" class=\"data row3 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col21\" class=\"data row3 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col22\" class=\"data row3 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col23\" class=\"data row3 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col24\" class=\"data row3 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col25\" class=\"data row3 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col26\" class=\"data row3 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col27\" class=\"data row3 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col28\" class=\"data row3 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col29\" class=\"data row3 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col30\" class=\"data row3 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row3_col31\" class=\"data row3 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row4\" class=\"row_heading level0 row4\" >having_At_Symbol</th>\n",
       "      <td id=\"T_e43d9_row4_col0\" class=\"data row4 col0\" >-0.18</td>\n",
       "      <td id=\"T_e43d9_row4_col1\" class=\"data row4 col1\" >0.16</td>\n",
       "      <td id=\"T_e43d9_row4_col2\" class=\"data row4 col2\" >-0.07</td>\n",
       "      <td id=\"T_e43d9_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
       "      <td id=\"T_e43d9_row4_col4\" class=\"data row4 col4\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col5\" class=\"data row4 col5\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col6\" class=\"data row4 col6\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col7\" class=\"data row4 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col8\" class=\"data row4 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col9\" class=\"data row4 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col10\" class=\"data row4 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col11\" class=\"data row4 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col12\" class=\"data row4 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col13\" class=\"data row4 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col15\" class=\"data row4 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col16\" class=\"data row4 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col17\" class=\"data row4 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col18\" class=\"data row4 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col19\" class=\"data row4 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col20\" class=\"data row4 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col21\" class=\"data row4 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col22\" class=\"data row4 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col23\" class=\"data row4 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col24\" class=\"data row4 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col25\" class=\"data row4 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col26\" class=\"data row4 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col27\" class=\"data row4 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col28\" class=\"data row4 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col29\" class=\"data row4 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col30\" class=\"data row4 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row4_col31\" class=\"data row4 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row5\" class=\"row_heading level0 row5\" >double_slash_redirecting</th>\n",
       "      <td id=\"T_e43d9_row5_col0\" class=\"data row5 col0\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row5_col1\" class=\"data row5 col1\" >0.39</td>\n",
       "      <td id=\"T_e43d9_row5_col2\" class=\"data row5 col2\" >-0.08</td>\n",
       "      <td id=\"T_e43d9_row5_col3\" class=\"data row5 col3\" >0.84</td>\n",
       "      <td id=\"T_e43d9_row5_col4\" class=\"data row5 col4\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row5_col5\" class=\"data row5 col5\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col6\" class=\"data row5 col6\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col7\" class=\"data row5 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col8\" class=\"data row5 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col9\" class=\"data row5 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col10\" class=\"data row5 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col11\" class=\"data row5 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col12\" class=\"data row5 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col13\" class=\"data row5 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col14\" class=\"data row5 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col15\" class=\"data row5 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col16\" class=\"data row5 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col17\" class=\"data row5 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col18\" class=\"data row5 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col19\" class=\"data row5 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col20\" class=\"data row5 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col21\" class=\"data row5 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col22\" class=\"data row5 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col23\" class=\"data row5 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col24\" class=\"data row5 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col25\" class=\"data row5 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col26\" class=\"data row5 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col27\" class=\"data row5 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col28\" class=\"data row5 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col29\" class=\"data row5 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col30\" class=\"data row5 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row5_col31\" class=\"data row5 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row6\" class=\"row_heading level0 row6\" >Prefix_Suffix</th>\n",
       "      <td id=\"T_e43d9_row6_col0\" class=\"data row6 col0\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row6_col1\" class=\"data row6 col1\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row6_col2\" class=\"data row6 col2\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row6_col3\" class=\"data row6 col3\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row6_col4\" class=\"data row6 col4\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row6_col5\" class=\"data row6 col5\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row6_col6\" class=\"data row6 col6\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col7\" class=\"data row6 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col8\" class=\"data row6 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col9\" class=\"data row6 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col10\" class=\"data row6 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col11\" class=\"data row6 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col12\" class=\"data row6 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col13\" class=\"data row6 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col14\" class=\"data row6 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col15\" class=\"data row6 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col16\" class=\"data row6 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col17\" class=\"data row6 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col18\" class=\"data row6 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col19\" class=\"data row6 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col20\" class=\"data row6 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col21\" class=\"data row6 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col22\" class=\"data row6 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col23\" class=\"data row6 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col24\" class=\"data row6 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col25\" class=\"data row6 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col26\" class=\"data row6 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col27\" class=\"data row6 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col28\" class=\"data row6 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col29\" class=\"data row6 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col30\" class=\"data row6 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row6_col31\" class=\"data row6 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row7\" class=\"row_heading level0 row7\" >having_Sub_Domain</th>\n",
       "      <td id=\"T_e43d9_row7_col0\" class=\"data row7 col0\" >0.23</td>\n",
       "      <td id=\"T_e43d9_row7_col1\" class=\"data row7 col1\" >-0.08</td>\n",
       "      <td id=\"T_e43d9_row7_col2\" class=\"data row7 col2\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row7_col3\" class=\"data row7 col3\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row7_col4\" class=\"data row7 col4\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row7_col5\" class=\"data row7 col5\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row7_col6\" class=\"data row7 col6\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row7_col7\" class=\"data row7 col7\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col8\" class=\"data row7 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col9\" class=\"data row7 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col10\" class=\"data row7 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col11\" class=\"data row7 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col12\" class=\"data row7 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col13\" class=\"data row7 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col15\" class=\"data row7 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col16\" class=\"data row7 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col17\" class=\"data row7 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col18\" class=\"data row7 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col19\" class=\"data row7 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col20\" class=\"data row7 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col21\" class=\"data row7 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col22\" class=\"data row7 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col23\" class=\"data row7 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col24\" class=\"data row7 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col25\" class=\"data row7 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col26\" class=\"data row7 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col27\" class=\"data row7 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col28\" class=\"data row7 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col29\" class=\"data row7 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col30\" class=\"data row7 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row7_col31\" class=\"data row7 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row8\" class=\"row_heading level0 row8\" >SSLfinal_State</th>\n",
       "      <td id=\"T_e43d9_row8_col0\" class=\"data row8 col0\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row8_col1\" class=\"data row8 col1\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row8_col2\" class=\"data row8 col2\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row8_col3\" class=\"data row8 col3\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row8_col4\" class=\"data row8 col4\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row8_col5\" class=\"data row8 col5\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row8_col6\" class=\"data row8 col6\" >0.26</td>\n",
       "      <td id=\"T_e43d9_row8_col7\" class=\"data row8 col7\" >0.27</td>\n",
       "      <td id=\"T_e43d9_row8_col8\" class=\"data row8 col8\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col9\" class=\"data row8 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col10\" class=\"data row8 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col11\" class=\"data row8 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col12\" class=\"data row8 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col13\" class=\"data row8 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col14\" class=\"data row8 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col15\" class=\"data row8 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col16\" class=\"data row8 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col17\" class=\"data row8 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col18\" class=\"data row8 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col19\" class=\"data row8 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col20\" class=\"data row8 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col21\" class=\"data row8 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col22\" class=\"data row8 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col23\" class=\"data row8 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col24\" class=\"data row8 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col25\" class=\"data row8 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col26\" class=\"data row8 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col27\" class=\"data row8 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col28\" class=\"data row8 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col29\" class=\"data row8 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col30\" class=\"data row8 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row8_col31\" class=\"data row8 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row9\" class=\"row_heading level0 row9\" >Domain_registeration_length</th>\n",
       "      <td id=\"T_e43d9_row9_col0\" class=\"data row9 col0\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row9_col1\" class=\"data row9 col1\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row9_col2\" class=\"data row9 col2\" >-0.22</td>\n",
       "      <td id=\"T_e43d9_row9_col3\" class=\"data row9 col3\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row9_col4\" class=\"data row9 col4\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row9_col5\" class=\"data row9 col5\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row9_col6\" class=\"data row9 col6\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row9_col7\" class=\"data row9 col7\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row9_col8\" class=\"data row9 col8\" >-0.19</td>\n",
       "      <td id=\"T_e43d9_row9_col9\" class=\"data row9 col9\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col10\" class=\"data row9 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col11\" class=\"data row9 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col12\" class=\"data row9 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col13\" class=\"data row9 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col15\" class=\"data row9 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col16\" class=\"data row9 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col17\" class=\"data row9 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col18\" class=\"data row9 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col19\" class=\"data row9 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col20\" class=\"data row9 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col21\" class=\"data row9 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col22\" class=\"data row9 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col23\" class=\"data row9 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col24\" class=\"data row9 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col25\" class=\"data row9 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col26\" class=\"data row9 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col27\" class=\"data row9 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col28\" class=\"data row9 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col29\" class=\"data row9 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col30\" class=\"data row9 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row9_col31\" class=\"data row9 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row10\" class=\"row_heading level0 row10\" >Favicon</th>\n",
       "      <td id=\"T_e43d9_row10_col0\" class=\"data row10 col0\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row10_col1\" class=\"data row10 col1\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row10_col2\" class=\"data row10 col2\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row10_col3\" class=\"data row10 col3\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row10_col4\" class=\"data row10 col4\" >0.32</td>\n",
       "      <td id=\"T_e43d9_row10_col5\" class=\"data row10 col5\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row10_col6\" class=\"data row10 col6\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row10_col7\" class=\"data row10 col7\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row10_col8\" class=\"data row10 col8\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row10_col9\" class=\"data row10 col9\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row10_col10\" class=\"data row10 col10\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col11\" class=\"data row10 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col12\" class=\"data row10 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col13\" class=\"data row10 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col14\" class=\"data row10 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col15\" class=\"data row10 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col16\" class=\"data row10 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col17\" class=\"data row10 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col18\" class=\"data row10 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col19\" class=\"data row10 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col20\" class=\"data row10 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col21\" class=\"data row10 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col22\" class=\"data row10 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col23\" class=\"data row10 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col24\" class=\"data row10 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col25\" class=\"data row10 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col26\" class=\"data row10 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col27\" class=\"data row10 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col28\" class=\"data row10 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col29\" class=\"data row10 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col30\" class=\"data row10 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row10_col31\" class=\"data row10 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row11\" class=\"row_heading level0 row11\" >port</th>\n",
       "      <td id=\"T_e43d9_row11_col0\" class=\"data row11 col0\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row11_col1\" class=\"data row11 col1\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row11_col2\" class=\"data row11 col2\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row11_col3\" class=\"data row11 col3\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row11_col4\" class=\"data row11 col4\" >0.39</td>\n",
       "      <td id=\"T_e43d9_row11_col5\" class=\"data row11 col5\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row11_col6\" class=\"data row11 col6\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row11_col7\" class=\"data row11 col7\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row11_col8\" class=\"data row11 col8\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row11_col9\" class=\"data row11 col9\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row11_col10\" class=\"data row11 col10\" >0.79</td>\n",
       "      <td id=\"T_e43d9_row11_col11\" class=\"data row11 col11\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col12\" class=\"data row11 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col13\" class=\"data row11 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col14\" class=\"data row11 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col15\" class=\"data row11 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col16\" class=\"data row11 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col17\" class=\"data row11 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col18\" class=\"data row11 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col19\" class=\"data row11 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col20\" class=\"data row11 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col21\" class=\"data row11 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col22\" class=\"data row11 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col23\" class=\"data row11 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col24\" class=\"data row11 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col25\" class=\"data row11 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col26\" class=\"data row11 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col27\" class=\"data row11 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col28\" class=\"data row11 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col29\" class=\"data row11 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col30\" class=\"data row11 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row11_col31\" class=\"data row11 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row12\" class=\"row_heading level0 row12\" >HTTPS_token</th>\n",
       "      <td id=\"T_e43d9_row12_col0\" class=\"data row12 col0\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row12_col1\" class=\"data row12 col1\" >0.35</td>\n",
       "      <td id=\"T_e43d9_row12_col2\" class=\"data row12 col2\" >-0.10</td>\n",
       "      <td id=\"T_e43d9_row12_col3\" class=\"data row12 col3\" >0.75</td>\n",
       "      <td id=\"T_e43d9_row12_col4\" class=\"data row12 col4\" >0.11</td>\n",
       "      <td id=\"T_e43d9_row12_col5\" class=\"data row12 col5\" >0.75</td>\n",
       "      <td id=\"T_e43d9_row12_col6\" class=\"data row12 col6\" >-0.08</td>\n",
       "      <td id=\"T_e43d9_row12_col7\" class=\"data row12 col7\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row12_col8\" class=\"data row12 col8\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row12_col9\" class=\"data row12 col9\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row12_col10\" class=\"data row12 col10\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row12_col11\" class=\"data row12 col11\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row12_col12\" class=\"data row12 col12\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col13\" class=\"data row12 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col14\" class=\"data row12 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col15\" class=\"data row12 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col16\" class=\"data row12 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col17\" class=\"data row12 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col18\" class=\"data row12 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col19\" class=\"data row12 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col20\" class=\"data row12 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col21\" class=\"data row12 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col22\" class=\"data row12 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col23\" class=\"data row12 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col24\" class=\"data row12 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col25\" class=\"data row12 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col26\" class=\"data row12 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col27\" class=\"data row12 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col28\" class=\"data row12 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col29\" class=\"data row12 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col30\" class=\"data row12 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row12_col31\" class=\"data row12 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row13\" class=\"row_heading level0 row13\" >Request_URL</th>\n",
       "      <td id=\"T_e43d9_row13_col0\" class=\"data row13 col0\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row13_col1\" class=\"data row13 col1\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row13_col2\" class=\"data row13 col2\" >0.24</td>\n",
       "      <td id=\"T_e43d9_row13_col3\" class=\"data row13 col3\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row13_col4\" class=\"data row13 col4\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row13_col5\" class=\"data row13 col5\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row13_col6\" class=\"data row13 col6\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row13_col7\" class=\"data row13 col7\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row13_col8\" class=\"data row13 col8\" >0.19</td>\n",
       "      <td id=\"T_e43d9_row13_col9\" class=\"data row13 col9\" >-0.60</td>\n",
       "      <td id=\"T_e43d9_row13_col10\" class=\"data row13 col10\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row13_col11\" class=\"data row13 col11\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row13_col12\" class=\"data row13 col12\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row13_col13\" class=\"data row13 col13\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col14\" class=\"data row13 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col15\" class=\"data row13 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col16\" class=\"data row13 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col17\" class=\"data row13 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col18\" class=\"data row13 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col19\" class=\"data row13 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col20\" class=\"data row13 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col21\" class=\"data row13 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col22\" class=\"data row13 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col23\" class=\"data row13 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col24\" class=\"data row13 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col25\" class=\"data row13 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col26\" class=\"data row13 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col27\" class=\"data row13 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col28\" class=\"data row13 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col29\" class=\"data row13 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col30\" class=\"data row13 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row13_col31\" class=\"data row13 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row14\" class=\"row_heading level0 row14\" >URL_of_Anchor</th>\n",
       "      <td id=\"T_e43d9_row14_col0\" class=\"data row14 col0\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row14_col1\" class=\"data row14 col1\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row14_col2\" class=\"data row14 col2\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row14_col3\" class=\"data row14 col3\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row14_col4\" class=\"data row14 col4\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row14_col5\" class=\"data row14 col5\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row14_col6\" class=\"data row14 col6\" >0.35</td>\n",
       "      <td id=\"T_e43d9_row14_col7\" class=\"data row14 col7\" >0.23</td>\n",
       "      <td id=\"T_e43d9_row14_col8\" class=\"data row14 col8\" >0.54</td>\n",
       "      <td id=\"T_e43d9_row14_col9\" class=\"data row14 col9\" >-0.16</td>\n",
       "      <td id=\"T_e43d9_row14_col10\" class=\"data row14 col10\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row14_col11\" class=\"data row14 col11\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row14_col12\" class=\"data row14 col12\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row14_col13\" class=\"data row14 col13\" >0.18</td>\n",
       "      <td id=\"T_e43d9_row14_col14\" class=\"data row14 col14\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col15\" class=\"data row14 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col16\" class=\"data row14 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col17\" class=\"data row14 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col18\" class=\"data row14 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col19\" class=\"data row14 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col20\" class=\"data row14 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col21\" class=\"data row14 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col22\" class=\"data row14 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col23\" class=\"data row14 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col24\" class=\"data row14 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col25\" class=\"data row14 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col26\" class=\"data row14 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col27\" class=\"data row14 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col28\" class=\"data row14 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col29\" class=\"data row14 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col30\" class=\"data row14 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row14_col31\" class=\"data row14 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row15\" class=\"row_heading level0 row15\" >Links_in_tags</th>\n",
       "      <td id=\"T_e43d9_row15_col0\" class=\"data row15 col0\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row15_col1\" class=\"data row15 col1\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row15_col2\" class=\"data row15 col2\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row15_col3\" class=\"data row15 col3\" >-0.14</td>\n",
       "      <td id=\"T_e43d9_row15_col4\" class=\"data row15 col4\" >-0.08</td>\n",
       "      <td id=\"T_e43d9_row15_col5\" class=\"data row15 col5\" >-0.13</td>\n",
       "      <td id=\"T_e43d9_row15_col6\" class=\"data row15 col6\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row15_col7\" class=\"data row15 col7\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row15_col8\" class=\"data row15 col8\" >0.17</td>\n",
       "      <td id=\"T_e43d9_row15_col9\" class=\"data row15 col9\" >-0.10</td>\n",
       "      <td id=\"T_e43d9_row15_col10\" class=\"data row15 col10\" >-0.10</td>\n",
       "      <td id=\"T_e43d9_row15_col11\" class=\"data row15 col11\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row15_col12\" class=\"data row15 col12\" >-0.11</td>\n",
       "      <td id=\"T_e43d9_row15_col13\" class=\"data row15 col13\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row15_col14\" class=\"data row15 col14\" >0.13</td>\n",
       "      <td id=\"T_e43d9_row15_col15\" class=\"data row15 col15\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col16\" class=\"data row15 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col17\" class=\"data row15 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col18\" class=\"data row15 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col19\" class=\"data row15 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col20\" class=\"data row15 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col21\" class=\"data row15 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col22\" class=\"data row15 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col23\" class=\"data row15 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col24\" class=\"data row15 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col25\" class=\"data row15 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col26\" class=\"data row15 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col27\" class=\"data row15 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col28\" class=\"data row15 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col29\" class=\"data row15 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col30\" class=\"data row15 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row15_col31\" class=\"data row15 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row16\" class=\"row_heading level0 row16\" >SFH</th>\n",
       "      <td id=\"T_e43d9_row16_col0\" class=\"data row16 col0\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row16_col1\" class=\"data row16 col1\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row16_col2\" class=\"data row16 col2\" >0.40</td>\n",
       "      <td id=\"T_e43d9_row16_col3\" class=\"data row16 col3\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row16_col4\" class=\"data row16 col4\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row16_col5\" class=\"data row16 col5\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row16_col6\" class=\"data row16 col6\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row16_col7\" class=\"data row16 col7\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row16_col8\" class=\"data row16 col8\" >0.17</td>\n",
       "      <td id=\"T_e43d9_row16_col9\" class=\"data row16 col9\" >-0.13</td>\n",
       "      <td id=\"T_e43d9_row16_col10\" class=\"data row16 col10\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row16_col11\" class=\"data row16 col11\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row16_col12\" class=\"data row16 col12\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row16_col13\" class=\"data row16 col13\" >0.12</td>\n",
       "      <td id=\"T_e43d9_row16_col14\" class=\"data row16 col14\" >0.11</td>\n",
       "      <td id=\"T_e43d9_row16_col15\" class=\"data row16 col15\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row16_col16\" class=\"data row16 col16\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col17\" class=\"data row16 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col18\" class=\"data row16 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col19\" class=\"data row16 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col20\" class=\"data row16 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col21\" class=\"data row16 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col22\" class=\"data row16 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col23\" class=\"data row16 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col24\" class=\"data row16 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col25\" class=\"data row16 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col26\" class=\"data row16 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col27\" class=\"data row16 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col28\" class=\"data row16 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col29\" class=\"data row16 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col30\" class=\"data row16 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row16_col31\" class=\"data row16 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row17\" class=\"row_heading level0 row17\" >Submitting_to_email</th>\n",
       "      <td id=\"T_e43d9_row17_col0\" class=\"data row17 col0\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row17_col1\" class=\"data row17 col1\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row17_col2\" class=\"data row17 col2\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row17_col3\" class=\"data row17 col3\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row17_col4\" class=\"data row17 col4\" >0.39</td>\n",
       "      <td id=\"T_e43d9_row17_col5\" class=\"data row17 col5\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row17_col6\" class=\"data row17 col6\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row17_col7\" class=\"data row17 col7\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row17_col8\" class=\"data row17 col8\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row17_col9\" class=\"data row17 col9\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row17_col10\" class=\"data row17 col10\" >0.65</td>\n",
       "      <td id=\"T_e43d9_row17_col11\" class=\"data row17 col11\" >0.79</td>\n",
       "      <td id=\"T_e43d9_row17_col12\" class=\"data row17 col12\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row17_col13\" class=\"data row17 col13\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row17_col14\" class=\"data row17 col14\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row17_col15\" class=\"data row17 col15\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row17_col16\" class=\"data row17 col16\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row17_col17\" class=\"data row17 col17\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col18\" class=\"data row17 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col19\" class=\"data row17 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col20\" class=\"data row17 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col21\" class=\"data row17 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col22\" class=\"data row17 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col23\" class=\"data row17 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col24\" class=\"data row17 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col25\" class=\"data row17 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col26\" class=\"data row17 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col27\" class=\"data row17 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col28\" class=\"data row17 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col29\" class=\"data row17 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col30\" class=\"data row17 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row17_col31\" class=\"data row17 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row18\" class=\"row_heading level0 row18\" >Abnormal_URL</th>\n",
       "      <td id=\"T_e43d9_row18_col0\" class=\"data row18 col0\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row18_col1\" class=\"data row18 col1\" >0.32</td>\n",
       "      <td id=\"T_e43d9_row18_col2\" class=\"data row18 col2\" >-0.11</td>\n",
       "      <td id=\"T_e43d9_row18_col3\" class=\"data row18 col3\" >0.73</td>\n",
       "      <td id=\"T_e43d9_row18_col4\" class=\"data row18 col4\" >0.21</td>\n",
       "      <td id=\"T_e43d9_row18_col5\" class=\"data row18 col5\" >0.72</td>\n",
       "      <td id=\"T_e43d9_row18_col6\" class=\"data row18 col6\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row18_col7\" class=\"data row18 col7\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row18_col8\" class=\"data row18 col8\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row18_col9\" class=\"data row18 col9\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row18_col10\" class=\"data row18 col10\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row18_col11\" class=\"data row18 col11\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row18_col12\" class=\"data row18 col12\" >0.71</td>\n",
       "      <td id=\"T_e43d9_row18_col13\" class=\"data row18 col13\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row18_col14\" class=\"data row18 col14\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row18_col15\" class=\"data row18 col15\" >-0.12</td>\n",
       "      <td id=\"T_e43d9_row18_col16\" class=\"data row18 col16\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row18_col17\" class=\"data row18 col17\" >0.21</td>\n",
       "      <td id=\"T_e43d9_row18_col18\" class=\"data row18 col18\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col19\" class=\"data row18 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col20\" class=\"data row18 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col21\" class=\"data row18 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col22\" class=\"data row18 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col23\" class=\"data row18 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col24\" class=\"data row18 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col25\" class=\"data row18 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col26\" class=\"data row18 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col27\" class=\"data row18 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col28\" class=\"data row18 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col29\" class=\"data row18 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col30\" class=\"data row18 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row18_col31\" class=\"data row18 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row19\" class=\"row_heading level0 row19\" >Redirect</th>\n",
       "      <td id=\"T_e43d9_row19_col0\" class=\"data row19 col0\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row19_col1\" class=\"data row19 col1\" >-0.30</td>\n",
       "      <td id=\"T_e43d9_row19_col2\" class=\"data row19 col2\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row19_col3\" class=\"data row19 col3\" >-0.49</td>\n",
       "      <td id=\"T_e43d9_row19_col4\" class=\"data row19 col4\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row19_col5\" class=\"data row19 col5\" >-0.55</td>\n",
       "      <td id=\"T_e43d9_row19_col6\" class=\"data row19 col6\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row19_col7\" class=\"data row19 col7\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row19_col8\" class=\"data row19 col8\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row19_col9\" class=\"data row19 col9\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row19_col10\" class=\"data row19 col10\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row19_col11\" class=\"data row19 col11\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row19_col12\" class=\"data row19 col12\" >-0.42</td>\n",
       "      <td id=\"T_e43d9_row19_col13\" class=\"data row19 col13\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row19_col14\" class=\"data row19 col14\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row19_col15\" class=\"data row19 col15\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row19_col16\" class=\"data row19 col16\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row19_col17\" class=\"data row19 col17\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row19_col18\" class=\"data row19 col18\" >-0.42</td>\n",
       "      <td id=\"T_e43d9_row19_col19\" class=\"data row19 col19\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col20\" class=\"data row19 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col21\" class=\"data row19 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col22\" class=\"data row19 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col23\" class=\"data row19 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col24\" class=\"data row19 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col25\" class=\"data row19 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col26\" class=\"data row19 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col27\" class=\"data row19 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col28\" class=\"data row19 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col29\" class=\"data row19 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col30\" class=\"data row19 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row19_col31\" class=\"data row19 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row20\" class=\"row_heading level0 row20\" >on_mouseover</th>\n",
       "      <td id=\"T_e43d9_row20_col0\" class=\"data row20 col0\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row20_col2\" class=\"data row20 col2\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row20_col3\" class=\"data row20 col3\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row20_col4\" class=\"data row20 col4\" >0.31</td>\n",
       "      <td id=\"T_e43d9_row20_col5\" class=\"data row20 col5\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row20_col6\" class=\"data row20 col6\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row20_col7\" class=\"data row20 col7\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row20_col8\" class=\"data row20 col8\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row20_col9\" class=\"data row20 col9\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row20_col10\" class=\"data row20 col10\" >0.68</td>\n",
       "      <td id=\"T_e43d9_row20_col11\" class=\"data row20 col11\" >0.59</td>\n",
       "      <td id=\"T_e43d9_row20_col12\" class=\"data row20 col12\" >0.11</td>\n",
       "      <td id=\"T_e43d9_row20_col13\" class=\"data row20 col13\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row20_col14\" class=\"data row20 col14\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row20_col15\" class=\"data row20 col15\" >-0.07</td>\n",
       "      <td id=\"T_e43d9_row20_col16\" class=\"data row20 col16\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row20_col17\" class=\"data row20 col17\" >0.50</td>\n",
       "      <td id=\"T_e43d9_row20_col18\" class=\"data row20 col18\" >0.13</td>\n",
       "      <td id=\"T_e43d9_row20_col19\" class=\"data row20 col19\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row20_col20\" class=\"data row20 col20\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col21\" class=\"data row20 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col22\" class=\"data row20 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col23\" class=\"data row20 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col24\" class=\"data row20 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col25\" class=\"data row20 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col26\" class=\"data row20 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col27\" class=\"data row20 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col28\" class=\"data row20 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col29\" class=\"data row20 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col30\" class=\"data row20 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row20_col31\" class=\"data row20 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row21\" class=\"row_heading level0 row21\" >RightClick</th>\n",
       "      <td id=\"T_e43d9_row21_col0\" class=\"data row21 col0\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row21_col1\" class=\"data row21 col1\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row21_col2\" class=\"data row21 col2\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row21_col3\" class=\"data row21 col3\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row21_col4\" class=\"data row21 col4\" >0.26</td>\n",
       "      <td id=\"T_e43d9_row21_col5\" class=\"data row21 col5\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row21_col6\" class=\"data row21 col6\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row21_col7\" class=\"data row21 col7\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row21_col8\" class=\"data row21 col8\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row21_col9\" class=\"data row21 col9\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row21_col10\" class=\"data row21 col10\" >0.40</td>\n",
       "      <td id=\"T_e43d9_row21_col11\" class=\"data row21 col11\" >0.46</td>\n",
       "      <td id=\"T_e43d9_row21_col12\" class=\"data row21 col12\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row21_col13\" class=\"data row21 col13\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row21_col14\" class=\"data row21 col14\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row21_col15\" class=\"data row21 col15\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row21_col16\" class=\"data row21 col16\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row21_col17\" class=\"data row21 col17\" >0.38</td>\n",
       "      <td id=\"T_e43d9_row21_col18\" class=\"data row21 col18\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row21_col19\" class=\"data row21 col19\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row21_col20\" class=\"data row21 col20\" >0.45</td>\n",
       "      <td id=\"T_e43d9_row21_col21\" class=\"data row21 col21\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col22\" class=\"data row21 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col23\" class=\"data row21 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col24\" class=\"data row21 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col25\" class=\"data row21 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col26\" class=\"data row21 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col27\" class=\"data row21 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col28\" class=\"data row21 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col29\" class=\"data row21 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col30\" class=\"data row21 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row21_col31\" class=\"data row21 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row22\" class=\"row_heading level0 row22\" >popUpWidnow</th>\n",
       "      <td id=\"T_e43d9_row22_col0\" class=\"data row22 col0\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row22_col1\" class=\"data row22 col1\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row22_col2\" class=\"data row22 col2\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row22_col3\" class=\"data row22 col3\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row22_col4\" class=\"data row22 col4\" >0.31</td>\n",
       "      <td id=\"T_e43d9_row22_col5\" class=\"data row22 col5\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row22_col6\" class=\"data row22 col6\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row22_col7\" class=\"data row22 col7\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row22_col8\" class=\"data row22 col8\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row22_col9\" class=\"data row22 col9\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row22_col10\" class=\"data row22 col10\" >0.94</td>\n",
       "      <td id=\"T_e43d9_row22_col11\" class=\"data row22 col11\" >0.73</td>\n",
       "      <td id=\"T_e43d9_row22_col12\" class=\"data row22 col12\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row22_col13\" class=\"data row22 col13\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row22_col14\" class=\"data row22 col14\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row22_col15\" class=\"data row22 col15\" >-0.11</td>\n",
       "      <td id=\"T_e43d9_row22_col16\" class=\"data row22 col16\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
       "      <td id=\"T_e43d9_row22_col18\" class=\"data row22 col18\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row22_col19\" class=\"data row22 col19\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row22_col20\" class=\"data row22 col20\" >0.71</td>\n",
       "      <td id=\"T_e43d9_row22_col21\" class=\"data row22 col21\" >0.40</td>\n",
       "      <td id=\"T_e43d9_row22_col22\" class=\"data row22 col22\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col23\" class=\"data row22 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col24\" class=\"data row22 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col25\" class=\"data row22 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col26\" class=\"data row22 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col27\" class=\"data row22 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col28\" class=\"data row22 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col29\" class=\"data row22 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col30\" class=\"data row22 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row22_col31\" class=\"data row22 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row23\" class=\"row_heading level0 row23\" >Iframe</th>\n",
       "      <td id=\"T_e43d9_row23_col0\" class=\"data row23 col0\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row23_col1\" class=\"data row23 col1\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row23_col2\" class=\"data row23 col2\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row23_col3\" class=\"data row23 col3\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row23_col4\" class=\"data row23 col4\" >0.32</td>\n",
       "      <td id=\"T_e43d9_row23_col5\" class=\"data row23 col5\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row23_col6\" class=\"data row23 col6\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row23_col7\" class=\"data row23 col7\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row23_col8\" class=\"data row23 col8\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row23_col9\" class=\"data row23 col9\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row23_col10\" class=\"data row23 col10\" >0.60</td>\n",
       "      <td id=\"T_e43d9_row23_col11\" class=\"data row23 col11\" >0.65</td>\n",
       "      <td id=\"T_e43d9_row23_col12\" class=\"data row23 col12\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row23_col13\" class=\"data row23 col13\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row23_col14\" class=\"data row23 col14\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row23_col15\" class=\"data row23 col15\" >-0.07</td>\n",
       "      <td id=\"T_e43d9_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row23_col17\" class=\"data row23 col17\" >0.54</td>\n",
       "      <td id=\"T_e43d9_row23_col18\" class=\"data row23 col18\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row23_col19\" class=\"data row23 col19\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row23_col20\" class=\"data row23 col20\" >0.62</td>\n",
       "      <td id=\"T_e43d9_row23_col21\" class=\"data row23 col21\" >0.66</td>\n",
       "      <td id=\"T_e43d9_row23_col22\" class=\"data row23 col22\" >0.60</td>\n",
       "      <td id=\"T_e43d9_row23_col23\" class=\"data row23 col23\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col24\" class=\"data row23 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col25\" class=\"data row23 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col26\" class=\"data row23 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col27\" class=\"data row23 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col28\" class=\"data row23 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col29\" class=\"data row23 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col30\" class=\"data row23 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row23_col31\" class=\"data row23 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row24\" class=\"row_heading level0 row24\" >age_of_domain</th>\n",
       "      <td id=\"T_e43d9_row24_col0\" class=\"data row24 col0\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row24_col1\" class=\"data row24 col1\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row24_col2\" class=\"data row24 col2\" >0.20</td>\n",
       "      <td id=\"T_e43d9_row24_col3\" class=\"data row24 col3\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row24_col4\" class=\"data row24 col4\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row24_col5\" class=\"data row24 col5\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row24_col6\" class=\"data row24 col6\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row24_col7\" class=\"data row24 col7\" >0.11</td>\n",
       "      <td id=\"T_e43d9_row24_col8\" class=\"data row24 col8\" >0.17</td>\n",
       "      <td id=\"T_e43d9_row24_col9\" class=\"data row24 col9\" >-0.08</td>\n",
       "      <td id=\"T_e43d9_row24_col10\" class=\"data row24 col10\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row24_col11\" class=\"data row24 col11\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row24_col12\" class=\"data row24 col12\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row24_col13\" class=\"data row24 col13\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row24_col14\" class=\"data row24 col14\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row24_col15\" class=\"data row24 col15\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row24_col16\" class=\"data row24 col16\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row24_col17\" class=\"data row24 col17\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row24_col18\" class=\"data row24 col18\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row24_col19\" class=\"data row24 col19\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row24_col20\" class=\"data row24 col20\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row24_col21\" class=\"data row24 col21\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row24_col22\" class=\"data row24 col22\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row24_col23\" class=\"data row24 col23\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row24_col24\" class=\"data row24 col24\" >nan</td>\n",
       "      <td id=\"T_e43d9_row24_col25\" class=\"data row24 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row24_col26\" class=\"data row24 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row24_col27\" class=\"data row24 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row24_col28\" class=\"data row24 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row24_col29\" class=\"data row24 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row24_col30\" class=\"data row24 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row24_col31\" class=\"data row24 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row25\" class=\"row_heading level0 row25\" >DNSRecord</th>\n",
       "      <td id=\"T_e43d9_row25_col0\" class=\"data row25 col0\" >0.42</td>\n",
       "      <td id=\"T_e43d9_row25_col1\" class=\"data row25 col1\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row25_col2\" class=\"data row25 col2\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row25_col3\" class=\"data row25 col3\" >0.44</td>\n",
       "      <td id=\"T_e43d9_row25_col4\" class=\"data row25 col4\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row25_col5\" class=\"data row25 col5\" >0.43</td>\n",
       "      <td id=\"T_e43d9_row25_col6\" class=\"data row25 col6\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row25_col7\" class=\"data row25 col7\" >0.13</td>\n",
       "      <td id=\"T_e43d9_row25_col8\" class=\"data row25 col8\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row25_col9\" class=\"data row25 col9\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row25_col10\" class=\"data row25 col10\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row25_col11\" class=\"data row25 col11\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row25_col12\" class=\"data row25 col12\" >0.40</td>\n",
       "      <td id=\"T_e43d9_row25_col13\" class=\"data row25 col13\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row25_col14\" class=\"data row25 col14\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row25_col15\" class=\"data row25 col15\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row25_col16\" class=\"data row25 col16\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row25_col17\" class=\"data row25 col17\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row25_col18\" class=\"data row25 col18\" >0.37</td>\n",
       "      <td id=\"T_e43d9_row25_col19\" class=\"data row25 col19\" >-0.19</td>\n",
       "      <td id=\"T_e43d9_row25_col20\" class=\"data row25 col20\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row25_col21\" class=\"data row25 col21\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row25_col22\" class=\"data row25 col22\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row25_col23\" class=\"data row25 col23\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row25_col24\" class=\"data row25 col24\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row25_col25\" class=\"data row25 col25\" >nan</td>\n",
       "      <td id=\"T_e43d9_row25_col26\" class=\"data row25 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row25_col27\" class=\"data row25 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row25_col28\" class=\"data row25 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row25_col29\" class=\"data row25 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row25_col30\" class=\"data row25 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row25_col31\" class=\"data row25 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row26\" class=\"row_heading level0 row26\" >web_traffic</th>\n",
       "      <td id=\"T_e43d9_row26_col0\" class=\"data row26 col0\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row26_col1\" class=\"data row26 col1\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row26_col2\" class=\"data row26 col2\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row26_col3\" class=\"data row26 col3\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row26_col4\" class=\"data row26 col4\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row26_col5\" class=\"data row26 col5\" >-0.07</td>\n",
       "      <td id=\"T_e43d9_row26_col6\" class=\"data row26 col6\" >0.11</td>\n",
       "      <td id=\"T_e43d9_row26_col7\" class=\"data row26 col7\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row26_col8\" class=\"data row26 col8\" >0.27</td>\n",
       "      <td id=\"T_e43d9_row26_col9\" class=\"data row26 col9\" >-0.14</td>\n",
       "      <td id=\"T_e43d9_row26_col10\" class=\"data row26 col10\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row26_col11\" class=\"data row26 col11\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row26_col12\" class=\"data row26 col12\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row26_col13\" class=\"data row26 col13\" >0.16</td>\n",
       "      <td id=\"T_e43d9_row26_col14\" class=\"data row26 col14\" >0.34</td>\n",
       "      <td id=\"T_e43d9_row26_col15\" class=\"data row26 col15\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row26_col16\" class=\"data row26 col16\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row26_col17\" class=\"data row26 col17\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row26_col18\" class=\"data row26 col18\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row26_col19\" class=\"data row26 col19\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row26_col20\" class=\"data row26 col20\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row26_col21\" class=\"data row26 col21\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row26_col22\" class=\"data row26 col22\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row26_col23\" class=\"data row26 col23\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row26_col24\" class=\"data row26 col24\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row26_col25\" class=\"data row26 col25\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row26_col26\" class=\"data row26 col26\" >nan</td>\n",
       "      <td id=\"T_e43d9_row26_col27\" class=\"data row26 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row26_col28\" class=\"data row26 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row26_col29\" class=\"data row26 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row26_col30\" class=\"data row26 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row26_col31\" class=\"data row26 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row27\" class=\"row_heading level0 row27\" >Page_Rank</th>\n",
       "      <td id=\"T_e43d9_row27_col0\" class=\"data row27 col0\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row27_col1\" class=\"data row27 col1\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row27_col2\" class=\"data row27 col2\" >0.16</td>\n",
       "      <td id=\"T_e43d9_row27_col3\" class=\"data row27 col3\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row27_col4\" class=\"data row27 col4\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row27_col5\" class=\"data row27 col5\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row27_col6\" class=\"data row27 col6\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row27_col7\" class=\"data row27 col7\" >0.13</td>\n",
       "      <td id=\"T_e43d9_row27_col8\" class=\"data row27 col8\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row27_col9\" class=\"data row27 col9\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row27_col10\" class=\"data row27 col10\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row27_col11\" class=\"data row27 col11\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row27_col12\" class=\"data row27 col12\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row27_col13\" class=\"data row27 col13\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row27_col14\" class=\"data row27 col14\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row27_col15\" class=\"data row27 col15\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row27_col16\" class=\"data row27 col16\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row27_col17\" class=\"data row27 col17\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row27_col18\" class=\"data row27 col18\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row27_col19\" class=\"data row27 col19\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row27_col20\" class=\"data row27 col20\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row27_col21\" class=\"data row27 col21\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row27_col22\" class=\"data row27 col22\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row27_col23\" class=\"data row27 col23\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row27_col24\" class=\"data row27 col24\" >-0.14</td>\n",
       "      <td id=\"T_e43d9_row27_col25\" class=\"data row27 col25\" >0.15</td>\n",
       "      <td id=\"T_e43d9_row27_col26\" class=\"data row27 col26\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row27_col27\" class=\"data row27 col27\" >nan</td>\n",
       "      <td id=\"T_e43d9_row27_col28\" class=\"data row27 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row27_col29\" class=\"data row27 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row27_col30\" class=\"data row27 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row27_col31\" class=\"data row27 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row28\" class=\"row_heading level0 row28\" >Google_Index</th>\n",
       "      <td id=\"T_e43d9_row28_col0\" class=\"data row28 col0\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row28_col1\" class=\"data row28 col1\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row28_col2\" class=\"data row28 col2\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row28_col3\" class=\"data row28 col3\" >0.19</td>\n",
       "      <td id=\"T_e43d9_row28_col4\" class=\"data row28 col4\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row28_col5\" class=\"data row28 col5\" >0.22</td>\n",
       "      <td id=\"T_e43d9_row28_col6\" class=\"data row28 col6\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row28_col7\" class=\"data row28 col7\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row28_col8\" class=\"data row28 col8\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row28_col9\" class=\"data row28 col9\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row28_col10\" class=\"data row28 col10\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row28_col11\" class=\"data row28 col11\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row28_col12\" class=\"data row28 col12\" >0.14</td>\n",
       "      <td id=\"T_e43d9_row28_col13\" class=\"data row28 col13\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row28_col14\" class=\"data row28 col14\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row28_col15\" class=\"data row28 col15\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row28_col16\" class=\"data row28 col16\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row28_col17\" class=\"data row28 col17\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row28_col18\" class=\"data row28 col18\" >0.15</td>\n",
       "      <td id=\"T_e43d9_row28_col19\" class=\"data row28 col19\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row28_col20\" class=\"data row28 col20\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row28_col21\" class=\"data row28 col21\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row28_col22\" class=\"data row28 col22\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row28_col23\" class=\"data row28 col23\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row28_col24\" class=\"data row28 col24\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row28_col25\" class=\"data row28 col25\" >0.16</td>\n",
       "      <td id=\"T_e43d9_row28_col26\" class=\"data row28 col26\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row28_col27\" class=\"data row28 col27\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row28_col28\" class=\"data row28 col28\" >nan</td>\n",
       "      <td id=\"T_e43d9_row28_col29\" class=\"data row28 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row28_col30\" class=\"data row28 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row28_col31\" class=\"data row28 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row29\" class=\"row_heading level0 row29\" >Links_pointing_to_page</th>\n",
       "      <td id=\"T_e43d9_row29_col0\" class=\"data row29 col0\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row29_col1\" class=\"data row29 col1\" >-0.34</td>\n",
       "      <td id=\"T_e43d9_row29_col2\" class=\"data row29 col2\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row29_col3\" class=\"data row29 col3\" >-0.19</td>\n",
       "      <td id=\"T_e43d9_row29_col4\" class=\"data row29 col4\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row29_col5\" class=\"data row29 col5\" >-0.19</td>\n",
       "      <td id=\"T_e43d9_row29_col6\" class=\"data row29 col6\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row29_col7\" class=\"data row29 col7\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row29_col8\" class=\"data row29 col8\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row29_col9\" class=\"data row29 col9\" >0.13</td>\n",
       "      <td id=\"T_e43d9_row29_col10\" class=\"data row29 col10\" >-0.12</td>\n",
       "      <td id=\"T_e43d9_row29_col11\" class=\"data row29 col11\" >-0.13</td>\n",
       "      <td id=\"T_e43d9_row29_col12\" class=\"data row29 col12\" >-0.11</td>\n",
       "      <td id=\"T_e43d9_row29_col13\" class=\"data row29 col13\" >-0.07</td>\n",
       "      <td id=\"T_e43d9_row29_col14\" class=\"data row29 col14\" >0.02</td>\n",
       "      <td id=\"T_e43d9_row29_col15\" class=\"data row29 col15\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row29_col16\" class=\"data row29 col16\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row29_col17\" class=\"data row29 col17\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row29_col18\" class=\"data row29 col18\" >-0.15</td>\n",
       "      <td id=\"T_e43d9_row29_col19\" class=\"data row29 col19\" >0.14</td>\n",
       "      <td id=\"T_e43d9_row29_col20\" class=\"data row29 col20\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row29_col21\" class=\"data row29 col21\" >-0.12</td>\n",
       "      <td id=\"T_e43d9_row29_col22\" class=\"data row29 col22\" >-0.12</td>\n",
       "      <td id=\"T_e43d9_row29_col23\" class=\"data row29 col23\" >-0.14</td>\n",
       "      <td id=\"T_e43d9_row29_col24\" class=\"data row29 col24\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row29_col25\" class=\"data row29 col25\" >-0.29</td>\n",
       "      <td id=\"T_e43d9_row29_col26\" class=\"data row29 col26\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row29_col27\" class=\"data row29 col27\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row29_col28\" class=\"data row29 col28\" >-0.05</td>\n",
       "      <td id=\"T_e43d9_row29_col29\" class=\"data row29 col29\" >nan</td>\n",
       "      <td id=\"T_e43d9_row29_col30\" class=\"data row29 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row29_col31\" class=\"data row29 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row30\" class=\"row_heading level0 row30\" >Statistical_report</th>\n",
       "      <td id=\"T_e43d9_row30_col0\" class=\"data row30 col0\" >0.18</td>\n",
       "      <td id=\"T_e43d9_row30_col1\" class=\"data row30 col1\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row30_col2\" class=\"data row30 col2\" >-0.08</td>\n",
       "      <td id=\"T_e43d9_row30_col3\" class=\"data row30 col3\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row30_col4\" class=\"data row30 col4\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row30_col5\" class=\"data row30 col5\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row30_col6\" class=\"data row30 col6\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row30_col7\" class=\"data row30 col7\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row30_col8\" class=\"data row30 col8\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row30_col9\" class=\"data row30 col9\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row30_col10\" class=\"data row30 col10\" >0.27</td>\n",
       "      <td id=\"T_e43d9_row30_col11\" class=\"data row30 col11\" >0.31</td>\n",
       "      <td id=\"T_e43d9_row30_col12\" class=\"data row30 col12\" >0.11</td>\n",
       "      <td id=\"T_e43d9_row30_col13\" class=\"data row30 col13\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row30_col14\" class=\"data row30 col14\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row30_col15\" class=\"data row30 col15\" >-0.09</td>\n",
       "      <td id=\"T_e43d9_row30_col16\" class=\"data row30 col16\" >-0.02</td>\n",
       "      <td id=\"T_e43d9_row30_col17\" class=\"data row30 col17\" >0.33</td>\n",
       "      <td id=\"T_e43d9_row30_col18\" class=\"data row30 col18\" >0.22</td>\n",
       "      <td id=\"T_e43d9_row30_col19\" class=\"data row30 col19\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row30_col20\" class=\"data row30 col20\" >0.22</td>\n",
       "      <td id=\"T_e43d9_row30_col21\" class=\"data row30 col21\" >0.15</td>\n",
       "      <td id=\"T_e43d9_row30_col22\" class=\"data row30 col22\" >0.25</td>\n",
       "      <td id=\"T_e43d9_row30_col23\" class=\"data row30 col23\" >0.19</td>\n",
       "      <td id=\"T_e43d9_row30_col24\" class=\"data row30 col24\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row30_col25\" class=\"data row30 col25\" >0.14</td>\n",
       "      <td id=\"T_e43d9_row30_col26\" class=\"data row30 col26\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row30_col27\" class=\"data row30 col27\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row30_col28\" class=\"data row30 col28\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row30_col29\" class=\"data row30 col29\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row30_col30\" class=\"data row30 col30\" >nan</td>\n",
       "      <td id=\"T_e43d9_row30_col31\" class=\"data row30 col31\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e43d9_level0_row31\" class=\"row_heading level0 row31\" >Result</th>\n",
       "      <td id=\"T_e43d9_row31_col0\" class=\"data row31 col0\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row31_col1\" class=\"data row31 col1\" >0.09</td>\n",
       "      <td id=\"T_e43d9_row31_col2\" class=\"data row31 col2\" >0.06</td>\n",
       "      <td id=\"T_e43d9_row31_col3\" class=\"data row31 col3\" >-0.07</td>\n",
       "      <td id=\"T_e43d9_row31_col4\" class=\"data row31 col4\" >0.05</td>\n",
       "      <td id=\"T_e43d9_row31_col5\" class=\"data row31 col5\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row31_col6\" class=\"data row31 col6\" >0.34</td>\n",
       "      <td id=\"T_e43d9_row31_col7\" class=\"data row31 col7\" >0.30</td>\n",
       "      <td id=\"T_e43d9_row31_col8\" class=\"data row31 col8\" >0.71</td>\n",
       "      <td id=\"T_e43d9_row31_col9\" class=\"data row31 col9\" >-0.22</td>\n",
       "      <td id=\"T_e43d9_row31_col10\" class=\"data row31 col10\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row31_col11\" class=\"data row31 col11\" >0.03</td>\n",
       "      <td id=\"T_e43d9_row31_col12\" class=\"data row31 col12\" >-0.04</td>\n",
       "      <td id=\"T_e43d9_row31_col13\" class=\"data row31 col13\" >0.25</td>\n",
       "      <td id=\"T_e43d9_row31_col14\" class=\"data row31 col14\" >0.70</td>\n",
       "      <td id=\"T_e43d9_row31_col15\" class=\"data row31 col15\" >0.24</td>\n",
       "      <td id=\"T_e43d9_row31_col16\" class=\"data row31 col16\" >0.22</td>\n",
       "      <td id=\"T_e43d9_row31_col17\" class=\"data row31 col17\" >0.01</td>\n",
       "      <td id=\"T_e43d9_row31_col18\" class=\"data row31 col18\" >-0.06</td>\n",
       "      <td id=\"T_e43d9_row31_col19\" class=\"data row31 col19\" >-0.03</td>\n",
       "      <td id=\"T_e43d9_row31_col20\" class=\"data row31 col20\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row31_col21\" class=\"data row31 col21\" >0.00</td>\n",
       "      <td id=\"T_e43d9_row31_col22\" class=\"data row31 col22\" >-0.00</td>\n",
       "      <td id=\"T_e43d9_row31_col23\" class=\"data row31 col23\" >-0.01</td>\n",
       "      <td id=\"T_e43d9_row31_col24\" class=\"data row31 col24\" >0.14</td>\n",
       "      <td id=\"T_e43d9_row31_col25\" class=\"data row31 col25\" >0.08</td>\n",
       "      <td id=\"T_e43d9_row31_col26\" class=\"data row31 col26\" >0.36</td>\n",
       "      <td id=\"T_e43d9_row31_col27\" class=\"data row31 col27\" >0.10</td>\n",
       "      <td id=\"T_e43d9_row31_col28\" class=\"data row31 col28\" >0.12</td>\n",
       "      <td id=\"T_e43d9_row31_col29\" class=\"data row31 col29\" >0.04</td>\n",
       "      <td id=\"T_e43d9_row31_col30\" class=\"data row31 col30\" >0.07</td>\n",
       "      <td id=\"T_e43d9_row31_col31\" class=\"data row31 col31\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x15bc475afb0>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#corr_matrix.style.background_gradient(cmap='coolwarm').set_precision(2)\n",
    "# Fill diagonal and upper half with NaNs\n",
    "corr_matrix_temp = corr_matrix.copy()\n",
    "mask = np.zeros_like(corr_matrix_temp, dtype=bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "corr_matrix_temp[mask] = np.nan\n",
    "corr_matrix_temp.style.background_gradient(cmap='coolwarm', axis=None, vmin=-1, vmax=1).highlight_null(\n",
    "    null_color='#f1f1f1').set_precision(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation of Result with input features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Result                         1.000000\n",
       "SSLfinal_State                 0.712543\n",
       "URL_of_Anchor                  0.696394\n",
       "web_traffic                    0.355581\n",
       "Prefix_Suffix                  0.344660\n",
       "having_Sub_Domain              0.296728\n",
       "Request_URL                    0.254572\n",
       "Links_in_tags                  0.238802\n",
       "SFH                            0.217931\n",
       "age_of_domain                  0.135198\n",
       "Google_Index                   0.121896\n",
       "Page_Rank                      0.102920\n",
       "having_IP                      0.094027\n",
       "DNSRecord                      0.081927\n",
       "Statistical_report             0.068015\n",
       "URL_Length                     0.059331\n",
       "having_At_Symbol               0.050045\n",
       "on_mouseover                   0.042180\n",
       "Links_pointing_to_page         0.035003\n",
       "port                           0.033971\n",
       "Submitting_to_email            0.011981\n",
       "RightClick                     0.004880\n",
       "key                           -0.000854\n",
       "popUpWidnow                   -0.002868\n",
       "Favicon                       -0.003260\n",
       "Iframe                        -0.013429\n",
       "Redirect                      -0.027197\n",
       "HTTPS_token                   -0.036573\n",
       "double_slash_redirecting      -0.037758\n",
       "Abnormal_URL                  -0.059860\n",
       "Shortining_Service            -0.066170\n",
       "Domain_registeration_length   -0.219960\n",
       "Name: Result, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"Result\"].sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation for Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "org_train_dataset_features = org_train_dataset.iloc[:, 1:31].values\n",
    "org_train_dataset_labels = org_train_dataset.iloc[:, 31].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    org_train_dataset_features, org_train_dataset_labels, test_size=0.10, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Some Classifiers what we might use later (ToDO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "skfolds = StratifiedKFold(n_splits=25, shuffle=True, random_state=RANDOM_STATE)\n",
    "\n",
    "sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=RANDOM_STATE)\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "\n",
    "tree_clf = DecisionTreeClassifier(max_depth=5)\n",
    "\n",
    "forest_clf = RandomForestClassifier(max_depth=5)\n",
    "\n",
    "mlp_clf = MLPClassifier(alpha=0.001, hidden_layer_sizes=([100, 100, 100]))\n",
    "\n",
    "svm_clf = SVC(kernel='linear', C=1.0, random_state=RANDOM_STATE)\n",
    "\n",
    "# add naive bayes, autoencoder and xgboost\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training and Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Base Evaluater (Unused)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tuneEstimater(estimator, param_grid, cv, scoring, verbose, X_train, y_train):\n",
    "    estimator_cl1 = clone(estimator)\n",
    "    estimator_cl2 = clone(estimator)\n",
    "    grid = GridSearchCV(estimator=estimator_cl1, param_grid=param_grid, cv=cv, scoring=scoring,\n",
    "                        return_train_score=False, verbose=verbose)\n",
    "\n",
    "    grid_search = grid.fit(X_train, y_train)\n",
    "    accuracy = grid_search.best_score_ * 100\n",
    "    print(\"Accuracy of tuned {} : {:.2f}%\".format(estimator.__class__.__name__, accuracy))\n",
    "\n",
    "    return    #print(grid_search.best_params_)\n",
    "# will map a list of estimators and params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 25 folds for each of 14 candidates, totalling 350 fits\n",
      "{'n_neighbors': 6, 'weights': 'distance'}\n",
      "Accuracy of KNN with best tuned parameters : 95.72%\n"
     ]
    }
   ],
   "source": [
    "k_range = list(range(1, 15))\n",
    "param_grid = dict(n_neighbors=k_range, weights=['distance'])\n",
    "  \n",
    "grid = GridSearchCV(estimator=knn_clf, param_grid=param_grid, cv=skfolds, scoring='accuracy',\n",
    "                    return_train_score=False, verbose=1, n_jobs=N_JOBS)\n",
    "  \n",
    "grid_search=grid.fit(X_train, y_train)\n",
    "\n",
    "print(grid_search.best_params_)\n",
    "accuracy = grid_search.best_score_ * 100\n",
    "print(\n",
    "    \"Accuracy of KNN with best tuned parameters : {:.2f}%\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 25 folds for each of 15 candidates, totalling 375 fits\n",
      "{'C': 0.1, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "Accuracy of LgRegg with best tuned parameters : 92.88%\n"
     ]
    }
   ],
   "source": [
    "solvers = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "penalty = ['l2']\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
    "# define grid search\n",
    "param_grid = dict(solver=solvers, penalty=penalty, C=c_values)\n",
    "model = LogisticRegression()\n",
    "\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=skfolds, scoring='accuracy',\n",
    "                    return_train_score=False, verbose=1, n_jobs=N_JOBS)\n",
    "\n",
    "grid_search = grid.fit(X_train, y_train)\n",
    "\n",
    "print(grid_search.best_params_)\n",
    "accuracy = grid_search.best_score_ * 100\n",
    "print(\n",
    "    \"Accuracy of LgRegg with best tuned parameters : {:.2f}%\".format(accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 25 folds for each of 10 candidates, totalling 250 fits\n",
      "{'alpha': 0.1}\n",
      "Accuracy of Ridge Classifier with best tuned parameters : 92.14%\n"
     ]
    }
   ],
   "source": [
    "alpha = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
    "param_grid = dict(alpha=alpha)\n",
    "model = RidgeClassifier()\n",
    "\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=skfolds, scoring='accuracy',\n",
    "                    return_train_score=False, verbose=1, n_jobs=N_JOBS)\n",
    "\n",
    "grid_search = grid.fit(X_train, y_train)\n",
    "\n",
    "print(grid_search.best_params_)\n",
    "accuracy = grid_search.best_score_ * 100\n",
    "print(\n",
    "    \"Accuracy of Ridge Classifier with best tuned parameters : {:.2f}%\".format(accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 25 folds for each of 14 candidates, totalling 350 fits\n",
      "Best Params:  {'max_features': 'sqrt', 'n_estimators': 300}\n",
      "Accuracy of RandomForestClassifier with best tuned parameters : 96.98%\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "n_estimators = [10, 50, 100, 150, 200, 300, 400]\n",
    "max_features = ['sqrt', 'log2']\n",
    "param_grid = dict(n_estimators=n_estimators, max_features=max_features)\n",
    "\n",
    "grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=skfolds, scoring='accuracy',\n",
    "                    return_train_score=False, verbose=100, n_jobs=N_JOBS)\n",
    "\n",
    "grid_search = grid.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best Params: \", grid_search.best_params_)\n",
    "accuracy = grid_search.best_score_ * 100\n",
    "print(\n",
    "    \"Accuracy of RandomForestClassifier with best tuned parameters : {:.2f}%\".format(accuracy))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Prediction (With Output generation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned KNN Classifier(Unused)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "def knnFinal(): \n",
    "    tuned_knn = KNeighborsClassifier(**grid_search.best_params_)\n",
    "    final_y_preds = tuned_knn.fit(org_train_dataset_features, org_train_dataset_labels)\n",
    "\n",
    "    test_df = org_test_dataset.iloc[ : , 1:]\n",
    "\n",
    "    final_predictions = tuned_knn.predict(test_df.to_numpy())\n",
    "\n",
    "    results = pd.DataFrame({'Key': org_test_dataset.iloc[:, 0], 'Result': final_predictions})\n",
    "\n",
    "    return results\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomForestFinal():\n",
    "    tuned_forest = RandomForestClassifier(max_features='log2', n_estimators=100)\n",
    "    final_y_preds = tuned_forest.fit(\n",
    "        org_train_dataset_features, org_train_dataset_labels)\n",
    "\n",
    "    test_df = org_test_dataset.iloc[:, 1:]\n",
    "\n",
    "    final_predictions = tuned_forest.predict(test_df.to_numpy())\n",
    "\n",
    "    results = pd.DataFrame({'Key': org_test_dataset.iloc[:, 0], 'Result': final_predictions})\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the Original Test Dataset with Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = randomForestFinal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing the results to a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv(SUBMISSION_RESULTS_PATH, index=False,  header=True)"
   ]
  }
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