PhantomTroupe_BestSubmission.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3d964623",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn.preprocessing as preprocessing \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import sklearn.metrics as metrics \n",
    "import sklearn.svm as svm \n",
    "import sklearn.naive_bayes as naive_bayes\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.naive_bayes import GaussianNB, BernoulliNB\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "import seaborn as sns \n",
    "import json\n",
    "import nltk\n",
    "import string\n",
    "import re\n",
    "import xgboost as xgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0208e1c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df = pd.read_csv(\"train_data.csv\")\n",
    "test_df = pd.read_csv(\"test_data.csv\")\n",
    "\n",
    "main_df = main_df.append(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab7c4c4",
   "metadata": {},
   "source": [
    "# 1. PreProcessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa1c721c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>webpageDescription</th>\n",
       "      <th>alchemy_category</th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
       "      <th>AvglinkWithOneCommonWord</th>\n",
       "      <th>AvglinkWithTwoCommonWord</th>\n",
       "      <th>AvglinkWithThreeCommonWord</th>\n",
       "      <th>AvglinkWithFourCommonWord</th>\n",
       "      <th>redundancyMeasure</th>\n",
       "      <th>...</th>\n",
       "      <th>lengthyDomain</th>\n",
       "      <th>hyperlinkToAllWordsRatio</th>\n",
       "      <th>isFrontPageNews</th>\n",
       "      <th>alphanumCharCount</th>\n",
       "      <th>linksCount</th>\n",
       "      <th>wordCount</th>\n",
       "      <th>parametrizedLinkRatio</th>\n",
       "      <th>spellingErrorsRatio</th>\n",
       "      <th>label</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.washingtonpost.com/blogs/early-lead...</td>\n",
       "      <td>{\"title\":\"Nebraska California State Bakersfiel...</td>\n",
       "      <td>science_technology</td>\n",
       "      <td>0.58916</td>\n",
       "      <td>2.055838</td>\n",
       "      <td>0.570962</td>\n",
       "      <td>0.285481</td>\n",
       "      <td>0.094617</td>\n",
       "      <td>0.048940</td>\n",
       "      <td>0.468646</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>4362</td>\n",
       "      <td>613</td>\n",
       "      <td>15</td>\n",
       "      <td>0.101142</td>\n",
       "      <td>0.062878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.holiday-rentals.co.uk/info/holiday-...</td>\n",
       "      <td>{\"title\":\"Travel Cookbook Discover holiday hom...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2.097222</td>\n",
       "      <td>0.697368</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494215</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>?</td>\n",
       "      <td>1662</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.107623</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://www.huffingtonpost.com/2011/10/27/lefto...</td>\n",
       "      <td>{\"title\":\"5 Ways With Leftover Rice 5 ways wit...</td>\n",
       "      <td>business</td>\n",
       "      <td>0.237643</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3075</td>\n",
       "      <td>375</td>\n",
       "      <td>3</td>\n",
       "      <td>0.197333</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://www.sun-sentinel.com/</td>\n",
       "      <td>{\"title\":\"South Florida Broward Palm Beach and...</td>\n",
       "      <td>?</td>\n",
       "      <td>?</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2538</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0.358452</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://socoting.com/protecting-and-serving/</td>\n",
       "      <td>{\"url\":\"socoting protecting and serving\",\"titl...</td>\n",
       "      <td>law_crime</td>\n",
       "      <td>0.245723</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625323</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>468</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.109756</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  http://www.washingtonpost.com/blogs/early-lead...   \n",
       "1  http://www.holiday-rentals.co.uk/info/holiday-...   \n",
       "2  http://www.huffingtonpost.com/2011/10/27/lefto...   \n",
       "3                       http://www.sun-sentinel.com/   \n",
       "4        http://socoting.com/protecting-and-serving/   \n",
       "\n",
       "                                  webpageDescription    alchemy_category  \\\n",
       "0  {\"title\":\"Nebraska California State Bakersfiel...  science_technology   \n",
       "1  {\"title\":\"Travel Cookbook Discover holiday hom...                   ?   \n",
       "2  {\"title\":\"5 Ways With Leftover Rice 5 ways wit...            business   \n",
       "3  {\"title\":\"South Florida Broward Palm Beach and...                   ?   \n",
       "4  {\"url\":\"socoting protecting and serving\",\"titl...           law_crime   \n",
       "\n",
       "  alchemy_category_score  avgLinkWordLength  AvglinkWithOneCommonWord  \\\n",
       "0                0.58916           2.055838                  0.570962   \n",
       "1                      ?           2.097222                  0.697368   \n",
       "2               0.237643           2.483680                  0.781333   \n",
       "3                      ?           2.919255                  0.629328   \n",
       "4               0.245723           1.571429                  0.380952   \n",
       "\n",
       "   AvglinkWithTwoCommonWord  AvglinkWithThreeCommonWord  \\\n",
       "0                  0.285481                    0.094617   \n",
       "1                  0.236842                    0.078947   \n",
       "2                  0.341333                    0.138667   \n",
       "3                  0.323829                    0.142566   \n",
       "4                  0.285714                    0.000000   \n",
       "\n",
       "   AvglinkWithFourCommonWord  redundancyMeasure  ...  lengthyDomain  \\\n",
       "0                   0.048940           0.468646  ...              1   \n",
       "1                   0.000000           0.494215  ...              0   \n",
       "2                   0.066667           0.390071  ...              1   \n",
       "3                   0.095723           0.505976  ...              1   \n",
       "4                   0.000000           0.625323  ...              0   \n",
       "\n",
       "   hyperlinkToAllWordsRatio  isFrontPageNews  alphanumCharCount  linksCount  \\\n",
       "0                        60                0               4362         613   \n",
       "1                        30                ?               1662          76   \n",
       "2                        58                0               3075         375   \n",
       "3                        76                1               2538         491   \n",
       "4                        23                0                468          21   \n",
       "\n",
       "   wordCount parametrizedLinkRatio  spellingErrorsRatio  label    id  \n",
       "0         15              0.101142             0.062878    0.0  1773  \n",
       "1          5              0.000000             0.107623    1.0  1558  \n",
       "2          3              0.197333             0.078125    1.0  7148  \n",
       "3          0              0.358452             0.017442    0.0   565  \n",
       "4          3              0.047619             0.109756    1.0  2606  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d150565",
   "metadata": {},
   "source": [
    "## 1.1 Checking for NULL and \"?\" values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4792a3b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                              0\n",
       "webpageDescription               0\n",
       "alchemy_category                 0\n",
       "alchemy_category_score           0\n",
       "avgLinkWordLength                0\n",
       "AvglinkWithOneCommonWord         0\n",
       "AvglinkWithTwoCommonWord         0\n",
       "AvglinkWithThreeCommonWord       0\n",
       "AvglinkWithFourCommonWord        0\n",
       "redundancyMeasure                0\n",
       "embedRatio                       0\n",
       "framebased                       0\n",
       "frameTagRatio                    0\n",
       "domainLink                       0\n",
       "tagRatio                         0\n",
       "imageTagRatio                    0\n",
       "isNews                           0\n",
       "lengthyDomain                    0\n",
       "hyperlinkToAllWordsRatio         0\n",
       "isFrontPageNews                  0\n",
       "alphanumCharCount                0\n",
       "linksCount                       0\n",
       "wordCount                        0\n",
       "parametrizedLinkRatio            0\n",
       "spellingErrorsRatio              0\n",
       "label                         1479\n",
       "id                               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102d7d26",
   "metadata": {},
   "source": [
    "No Null Values exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8608e5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                              0\n",
       "webpageDescription               0\n",
       "alchemy_category              2318\n",
       "alchemy_category_score        2318\n",
       "avgLinkWordLength                0\n",
       "AvglinkWithOneCommonWord         0\n",
       "AvglinkWithTwoCommonWord         0\n",
       "AvglinkWithThreeCommonWord       0\n",
       "AvglinkWithFourCommonWord        0\n",
       "redundancyMeasure                0\n",
       "embedRatio                       0\n",
       "framebased                       0\n",
       "frameTagRatio                    0\n",
       "domainLink                       0\n",
       "tagRatio                         0\n",
       "imageTagRatio                    0\n",
       "isNews                        2854\n",
       "lengthyDomain                    0\n",
       "hyperlinkToAllWordsRatio         0\n",
       "isFrontPageNews               1227\n",
       "alphanumCharCount                0\n",
       "linksCount                       0\n",
       "wordCount                        0\n",
       "parametrizedLinkRatio            0\n",
       "spellingErrorsRatio              0\n",
       "label                            0\n",
       "id                               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(main_df == '?').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fcf1c7cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2854"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(main_df[\"isNews\"] == '?').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84b7d2b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4541\n",
       "?    2854\n",
       "Name: isNews, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"isNews\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5fbd28",
   "metadata": {},
   "source": [
    "Assuming the other 2843 rows are not news. Making the '?' values to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b72fcaf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df[\"isNews\"].replace(\"?\", 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bd1e418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4541\n",
       "0    2854\n",
       "Name: isNews, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"isNews\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "442d357d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1227"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(main_df[\"isFrontPageNews\"] == '?').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98bd660f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5873\n",
       "?    1227\n",
       "1     295\n",
       "Name: isFrontPageNews, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"isFrontPageNews\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa09fed1",
   "metadata": {},
   "source": [
    "Assuming they are not front page news. Changing the \"?\" to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f72bfbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df[\"isFrontPageNews\"].replace(\"?\", 0, inplace = True)\n",
    "main_df[\"isFrontPageNews\"] = pd.to_numeric(main_df[\"isFrontPageNews\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00de72db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7100\n",
       "1     295\n",
       "Name: isFrontPageNews, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"isFrontPageNews\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83581cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2318"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(main_df[\"alchemy_category_score\"] == '?').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "647979c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "?            2318\n",
       "0.85           95\n",
       "0.0784091      43\n",
       "0.849999       12\n",
       "0.70677         8\n",
       "             ... \n",
       "0.730541        1\n",
       "0.759351        1\n",
       "0.382677        1\n",
       "0.511828        1\n",
       "0.853168        1\n",
       "Name: alchemy_category_score, Length: 4039, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"alchemy_category_score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bee56e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5077.000000\n",
       "mean        0.604722\n",
       "std         0.213726\n",
       "min         0.070833\n",
       "25%         0.452251\n",
       "50%         0.630952\n",
       "75%         0.782189\n",
       "max         0.999426\n",
       "Name: alchemy_category_score, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"alchemy_category_score\"] = main_df[\"alchemy_category_score\"].replace('?', np.nan)\n",
    "main_df[\"alchemy_category_score\"] = pd.to_numeric(main_df[\"alchemy_category_score\"])\n",
    "main_df[\"alchemy_category_score\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a4d226b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2318"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"alchemy_category_score\"].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8ac839b",
   "metadata": {},
   "source": [
    "Mean and Median are almost same. So, replacing nan values with Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74c7f478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7395.000000\n",
       "mean        0.612944\n",
       "std         0.177502\n",
       "min         0.070833\n",
       "25%         0.540952\n",
       "50%         0.630952\n",
       "75%         0.712322\n",
       "max         0.999426\n",
       "Name: alchemy_category_score, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"alchemy_category_score\"].fillna(main_df[\"alchemy_category_score\"].median(), inplace=True)\n",
    "main_df[\"alchemy_category_score\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "db785161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     7395\n",
       "unique      14\n",
       "top          ?\n",
       "freq      2318\n",
       "Name: alchemy_category, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"alchemy_category\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3da35c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "?                     2318\n",
       "recreation            1198\n",
       "arts_entertainment     962\n",
       "business               873\n",
       "health                 527\n",
       "sports                 387\n",
       "culture_politics       349\n",
       "computer_internet      293\n",
       "science_technology     293\n",
       "gaming                  79\n",
       "religion                72\n",
       "law_crime               33\n",
       "unknown                  6\n",
       "weather                  5\n",
       "Name: alchemy_category, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"alchemy_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8becfa0",
   "metadata": {},
   "source": [
    "Changing '?' to a new unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ff32c045",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "main_df[\"alchemy_category\"].replace(\"?\", \"unknown\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f033d4d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                           0\n",
       "webpageDescription            0\n",
       "alchemy_category              0\n",
       "alchemy_category_score        0\n",
       "avgLinkWordLength             0\n",
       "AvglinkWithOneCommonWord      0\n",
       "AvglinkWithTwoCommonWord      0\n",
       "AvglinkWithThreeCommonWord    0\n",
       "AvglinkWithFourCommonWord     0\n",
       "redundancyMeasure             0\n",
       "embedRatio                    0\n",
       "framebased                    0\n",
       "frameTagRatio                 0\n",
       "domainLink                    0\n",
       "tagRatio                      0\n",
       "imageTagRatio                 0\n",
       "isNews                        0\n",
       "lengthyDomain                 0\n",
       "hyperlinkToAllWordsRatio      0\n",
       "isFrontPageNews               0\n",
       "alphanumCharCount             0\n",
       "linksCount                    0\n",
       "wordCount                     0\n",
       "parametrizedLinkRatio         0\n",
       "spellingErrorsRatio           0\n",
       "label                         0\n",
       "id                            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(main_df == '?').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b8a231",
   "metadata": {},
   "source": [
    "----------------- all '?' and null values are removed ---------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84aee38",
   "metadata": {},
   "source": [
    "## 1.2 Removing useless columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "460464c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "sns.heatmap(main_df.corr(), vmin=-1, cmap=\"coolwarm\", annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23a7e264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7100\n",
       "1     295\n",
       "Name: isFrontPageNews, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"isFrontPageNews\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c54ec933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7395\n",
       "Name: framebased, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"framebased\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13e54e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7228\n",
       "1     167\n",
       "Name: domainLink, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"domainLink\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "396d4c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_be_deleted = [\"framebased\", \"domainLink\"]\n",
    "main_df.drop(axis=\"columns\", labels=columns_to_be_deleted, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b49d12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>webpageDescription</th>\n",
       "      <th>alchemy_category</th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
       "      <th>AvglinkWithOneCommonWord</th>\n",
       "      <th>AvglinkWithTwoCommonWord</th>\n",
       "      <th>AvglinkWithThreeCommonWord</th>\n",
       "      <th>AvglinkWithFourCommonWord</th>\n",
       "      <th>redundancyMeasure</th>\n",
       "      <th>...</th>\n",
       "      <th>lengthyDomain</th>\n",
       "      <th>hyperlinkToAllWordsRatio</th>\n",
       "      <th>isFrontPageNews</th>\n",
       "      <th>alphanumCharCount</th>\n",
       "      <th>linksCount</th>\n",
       "      <th>wordCount</th>\n",
       "      <th>parametrizedLinkRatio</th>\n",
       "      <th>spellingErrorsRatio</th>\n",
       "      <th>label</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.washingtonpost.com/blogs/early-lead...</td>\n",
       "      <td>{\"title\":\"Nebraska California State Bakersfiel...</td>\n",
       "      <td>science_technology</td>\n",
       "      <td>0.589160</td>\n",
       "      <td>2.055838</td>\n",
       "      <td>0.570962</td>\n",
       "      <td>0.285481</td>\n",
       "      <td>0.094617</td>\n",
       "      <td>0.048940</td>\n",
       "      <td>0.468646</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>4362</td>\n",
       "      <td>613</td>\n",
       "      <td>15</td>\n",
       "      <td>0.101142</td>\n",
       "      <td>0.062878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.holiday-rentals.co.uk/info/holiday-...</td>\n",
       "      <td>{\"title\":\"Travel Cookbook Discover holiday hom...</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.097222</td>\n",
       "      <td>0.697368</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494215</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1662</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.107623</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://www.huffingtonpost.com/2011/10/27/lefto...</td>\n",
       "      <td>{\"title\":\"5 Ways With Leftover Rice 5 ways wit...</td>\n",
       "      <td>business</td>\n",
       "      <td>0.237643</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3075</td>\n",
       "      <td>375</td>\n",
       "      <td>3</td>\n",
       "      <td>0.197333</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://www.sun-sentinel.com/</td>\n",
       "      <td>{\"title\":\"South Florida Broward Palm Beach and...</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2538</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0.358452</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://socoting.com/protecting-and-serving/</td>\n",
       "      <td>{\"url\":\"socoting protecting and serving\",\"titl...</td>\n",
       "      <td>law_crime</td>\n",
       "      <td>0.245723</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625323</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>468</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.109756</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  http://www.washingtonpost.com/blogs/early-lead...   \n",
       "1  http://www.holiday-rentals.co.uk/info/holiday-...   \n",
       "2  http://www.huffingtonpost.com/2011/10/27/lefto...   \n",
       "3                       http://www.sun-sentinel.com/   \n",
       "4        http://socoting.com/protecting-and-serving/   \n",
       "\n",
       "                                  webpageDescription    alchemy_category  \\\n",
       "0  {\"title\":\"Nebraska California State Bakersfiel...  science_technology   \n",
       "1  {\"title\":\"Travel Cookbook Discover holiday hom...             unknown   \n",
       "2  {\"title\":\"5 Ways With Leftover Rice 5 ways wit...            business   \n",
       "3  {\"title\":\"South Florida Broward Palm Beach and...             unknown   \n",
       "4  {\"url\":\"socoting protecting and serving\",\"titl...           law_crime   \n",
       "\n",
       "   alchemy_category_score  avgLinkWordLength  AvglinkWithOneCommonWord  \\\n",
       "0                0.589160           2.055838                  0.570962   \n",
       "1                0.630952           2.097222                  0.697368   \n",
       "2                0.237643           2.483680                  0.781333   \n",
       "3                0.630952           2.919255                  0.629328   \n",
       "4                0.245723           1.571429                  0.380952   \n",
       "\n",
       "   AvglinkWithTwoCommonWord  AvglinkWithThreeCommonWord  \\\n",
       "0                  0.285481                    0.094617   \n",
       "1                  0.236842                    0.078947   \n",
       "2                  0.341333                    0.138667   \n",
       "3                  0.323829                    0.142566   \n",
       "4                  0.285714                    0.000000   \n",
       "\n",
       "   AvglinkWithFourCommonWord  redundancyMeasure  ...  lengthyDomain  \\\n",
       "0                   0.048940           0.468646  ...              1   \n",
       "1                   0.000000           0.494215  ...              0   \n",
       "2                   0.066667           0.390071  ...              1   \n",
       "3                   0.095723           0.505976  ...              1   \n",
       "4                   0.000000           0.625323  ...              0   \n",
       "\n",
       "   hyperlinkToAllWordsRatio  isFrontPageNews  alphanumCharCount linksCount  \\\n",
       "0                        60                0               4362        613   \n",
       "1                        30                0               1662         76   \n",
       "2                        58                0               3075        375   \n",
       "3                        76                1               2538        491   \n",
       "4                        23                0                468         21   \n",
       "\n",
       "   wordCount  parametrizedLinkRatio  spellingErrorsRatio  label    id  \n",
       "0         15               0.101142             0.062878    0.0  1773  \n",
       "1          5               0.000000             0.107623    1.0  1558  \n",
       "2          3               0.197333             0.078125    1.0  7148  \n",
       "3          0               0.358452             0.017442    0.0   565  \n",
       "4          3               0.047619             0.109756    1.0  2606  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2b8b768d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>webpageDescription</th>\n",
       "      <th>alchemy_category</th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
       "      <th>AvglinkWithOneCommonWord</th>\n",
       "      <th>AvglinkWithTwoCommonWord</th>\n",
       "      <th>AvglinkWithThreeCommonWord</th>\n",
       "      <th>AvglinkWithFourCommonWord</th>\n",
       "      <th>redundancyMeasure</th>\n",
       "      <th>...</th>\n",
       "      <th>lengthyDomain</th>\n",
       "      <th>hyperlinkToAllWordsRatio</th>\n",
       "      <th>isFrontPageNews</th>\n",
       "      <th>alphanumCharCount</th>\n",
       "      <th>linksCount</th>\n",
       "      <th>wordCount</th>\n",
       "      <th>parametrizedLinkRatio</th>\n",
       "      <th>spellingErrorsRatio</th>\n",
       "      <th>label</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.washingtonpost.com/blogs/early-lead...</td>\n",
       "      <td>{\"title\":\"Nebraska California State Bakersfiel...</td>\n",
       "      <td>science_technology</td>\n",
       "      <td>0.589160</td>\n",
       "      <td>2.055838</td>\n",
       "      <td>0.570962</td>\n",
       "      <td>0.285481</td>\n",
       "      <td>0.094617</td>\n",
       "      <td>0.048940</td>\n",
       "      <td>0.468646</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>4362</td>\n",
       "      <td>613</td>\n",
       "      <td>15</td>\n",
       "      <td>0.101142</td>\n",
       "      <td>0.062878</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.holiday-rentals.co.uk/info/holiday-...</td>\n",
       "      <td>{\"title\":\"Travel Cookbook Discover holiday hom...</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.097222</td>\n",
       "      <td>0.697368</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494215</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1662</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.107623</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://www.huffingtonpost.com/2011/10/27/lefto...</td>\n",
       "      <td>{\"title\":\"5 Ways With Leftover Rice 5 ways wit...</td>\n",
       "      <td>business</td>\n",
       "      <td>0.237643</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>3075</td>\n",
       "      <td>375</td>\n",
       "      <td>3</td>\n",
       "      <td>0.197333</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://www.sun-sentinel.com/</td>\n",
       "      <td>{\"title\":\"South Florida Broward Palm Beach and...</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>2538</td>\n",
       "      <td>491</td>\n",
       "      <td>0</td>\n",
       "      <td>0.358452</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>0.0</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://socoting.com/protecting-and-serving/</td>\n",
       "      <td>{\"url\":\"socoting protecting and serving\",\"titl...</td>\n",
       "      <td>law_crime</td>\n",
       "      <td>0.245723</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625323</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>468</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.109756</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  http://www.washingtonpost.com/blogs/early-lead...   \n",
       "1  http://www.holiday-rentals.co.uk/info/holiday-...   \n",
       "2  http://www.huffingtonpost.com/2011/10/27/lefto...   \n",
       "3                       http://www.sun-sentinel.com/   \n",
       "4        http://socoting.com/protecting-and-serving/   \n",
       "\n",
       "                                  webpageDescription    alchemy_category  \\\n",
       "0  {\"title\":\"Nebraska California State Bakersfiel...  science_technology   \n",
       "1  {\"title\":\"Travel Cookbook Discover holiday hom...             unknown   \n",
       "2  {\"title\":\"5 Ways With Leftover Rice 5 ways wit...            business   \n",
       "3  {\"title\":\"South Florida Broward Palm Beach and...             unknown   \n",
       "4  {\"url\":\"socoting protecting and serving\",\"titl...           law_crime   \n",
       "\n",
       "   alchemy_category_score  avgLinkWordLength  AvglinkWithOneCommonWord  \\\n",
       "0                0.589160           2.055838                  0.570962   \n",
       "1                0.630952           2.097222                  0.697368   \n",
       "2                0.237643           2.483680                  0.781333   \n",
       "3                0.630952           2.919255                  0.629328   \n",
       "4                0.245723           1.571429                  0.380952   \n",
       "\n",
       "   AvglinkWithTwoCommonWord  AvglinkWithThreeCommonWord  \\\n",
       "0                  0.285481                    0.094617   \n",
       "1                  0.236842                    0.078947   \n",
       "2                  0.341333                    0.138667   \n",
       "3                  0.323829                    0.142566   \n",
       "4                  0.285714                    0.000000   \n",
       "\n",
       "   AvglinkWithFourCommonWord  redundancyMeasure  ...  lengthyDomain  \\\n",
       "0                   0.048940           0.468646  ...              1   \n",
       "1                   0.000000           0.494215  ...              0   \n",
       "2                   0.066667           0.390071  ...              1   \n",
       "3                   0.095723           0.505976  ...              1   \n",
       "4                   0.000000           0.625323  ...              0   \n",
       "\n",
       "   hyperlinkToAllWordsRatio  isFrontPageNews  alphanumCharCount linksCount  \\\n",
       "0                        60                0               4362        613   \n",
       "1                        30                0               1662         76   \n",
       "2                        58                0               3075        375   \n",
       "3                        76                1               2538        491   \n",
       "4                        23                0                468         21   \n",
       "\n",
       "   wordCount  parametrizedLinkRatio  spellingErrorsRatio  label    id  \n",
       "0         15               0.101142             0.062878    0.0  1773  \n",
       "1          5               0.000000             0.107623    1.0  1558  \n",
       "2          3               0.197333             0.078125    1.0  7148  \n",
       "3          0               0.358452             0.017442    0.0   565  \n",
       "4          3               0.047619             0.109756    1.0  2606  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc4b38a8",
   "metadata": {},
   "source": [
    "## 1.3 Outlier removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "72c1ca97",
   "metadata": {},
   "outputs": [],
   "source": [
    "class OutlierRemoval: \n",
    "    def __init__(self, lower_quartile, upper_quartile):\n",
    "        self.lower_whisker = lower_quartile - 1.5*(upper_quartile - lower_quartile)\n",
    "        self.upper_whisker = upper_quartile + 1.5*(upper_quartile - lower_quartile)\n",
    "    def removeOutlier(self, x):\n",
    "        return (x if x <= self.upper_whisker and x >= self.lower_whisker else (self.lower_whisker if x < self.lower_whisker else (self.upper_whisker)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "837c7d8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['url', 'webpageDescription', 'alchemy_category',\n",
      "       'alchemy_category_score', 'avgLinkWordLength',\n",
      "       'AvglinkWithOneCommonWord', 'AvglinkWithTwoCommonWord',\n",
      "       'AvglinkWithThreeCommonWord', 'AvglinkWithFourCommonWord',\n",
      "       'redundancyMeasure', 'embedRatio', 'frameTagRatio', 'tagRatio',\n",
      "       'imageTagRatio', 'isNews', 'lengthyDomain', 'hyperlinkToAllWordsRatio',\n",
      "       'isFrontPageNews', 'alphanumCharCount', 'linksCount', 'wordCount',\n",
      "       'parametrizedLinkRatio', 'spellingErrorsRatio', 'label', 'id'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(main_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "96321819",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"alchemy_category_score\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9ede0c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_remover = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"alchemy_category_score\"] = numeric_col.apply(outlier_remover.removeOutlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae52d1af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=main_df[\"alchemy_category_score\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "18ad5184",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAANyUlEQVR4nO3de4xcdRnG8edhW0IRlEixwiIuZokVNVYs0KJBJAoEVAiiYKIGMcFLXGqMEjHKzT+MmhhxNdF6xSiIkavcUUEj1ErLHQs6VomtKEWw0LRAWl7/OL8t02G3c3a7c94p/X6STWdnTue8+8vstzNnu2ccEQIANG+n7AEAYEdFgAEgCQEGgCQEGACSEGAASDJjMhvPnj07hoaGejQKALwwLV++/NGI2Kvz+kkFeGhoSMuWLZu+qQBgB2D7ofGu5xAEACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAkkm9Jxx2bKOjo2q1WtljbLZ69WpJ0uDgYPIkExseHtbIyEj2GOhTBBi1tVot3XXfCm3a9aXZo0iSBtavlST9++n+fBgPrH8sewT0uf585KJvbdr1pdow99jsMSRJsx64VpL6Zp5OY/MBE+EYMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACRpJMCjo6MaHR1tYlcAMK162a8ZPbnXDq1Wq4ndAMC062W/OAQBAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASWY0sZPVq1drw4YNWrRoURO7Q4+0Wi3t9Exkj7Hd2OmpJ9RqPcnjfjvXarU0a9asntx312fAtk+3vcz2sjVr1vRkCADYEXV9BhwRiyUtlqT58+dP6enP4OCgJOmCCy6Yyl9Hn1i0aJGWr/xP9hjbjWd3ebGGXzWHx/12rpevYDgGDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkmdHEToaHh5vYDQBMu172q5EAj4yMNLEbAJh2vewXhyAAIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEgyI3sAbF8G1j+mWQ9cmz2GJGlg/X8lqW/m6TSw/jFJc7LHQB8jwKhteHg4e4QtrF69UZI0ONivkZvTd2uG/kKAUdvIyEj2CMALCseAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEjiiKi/sb1G0kNdNpst6dFtGaoH+nEmqT/nYqb6+nGufpxJ6s+5mpzplRGxV+eVkwpwHbaXRcT8ab3TbdSPM0n9ORcz1dePc/XjTFJ/ztUPM3EIAgCSEGAASNKLAC/uwX1uq36cSerPuZipvn6cqx9nkvpzrvSZpv0YMACgHg5BAEASAgwASaYUYNvH2H7Qdsv258a53ba/WW6/x/ZB2z7qtMx1hO21tu8qH2c3MNMPbT9i+74Jbm98rWrMlLFOr7B9s+0Vtu+3vWicbTLWqs5cja6X7V1s/8n23WWm88bZptG1qjlT44+rst8B23favnqc21JatVlETOpD0oCkv0l6laSdJd0t6cCObY6VdJ0kS1ogaelk99OjuY6QdHWvZ+nY5+GSDpJ03wS3Z6xVt5ky1mlvSQeVy7tL+kufPK7qzNXoepWvf7dyeaakpZIWZK5VzZkaf1yV/X5a0kXj7TvjMdX+MZVnwIdIakXEyoh4RtLPJR3fsc3xkn4SlT9K2sP23lPY13TP1biI+L2kx7aySeNrVWOmxkXEwxFxR7n8pKQVkgY7NstYqzpzNap8/evKpzPLR+dP0xtdq5ozNc72vpKOk/T9CTbJaNVmUwnwoKR/tn2+Ss9/QNbZZrrV3efC8jLpOtuv7fFMdWSsVR1p62R7SNIbVT2Lape6VluZS2p4vcrL6rskPSLppohIX6saM0nNP66+IelMSc9OcHvqY2oqAfY413X+S1dnm+lWZ593qPqd7DdIGpV0RY9nqiNjrbpJWyfbu0m6VNKnIuKJzpvH+SuNrFWXuRpfr4jYFBHzJO0r6RDbr+vYpPG1qjFTo+tk+52SHomI5VvbbJzrGvv+m0qAV0l6Rdvn+0r61xS2mW5d9xkRT4y9TIqIayXNtD27x3N1k7FWW5W1TrZnqorczyLisnE2SVmrbnNlPq4i4n+SbpF0TMdNaY+riWZKWKc3S3q37X+oOiR5pO2fdmyT+v03lQDfLukA2/vb3lnSKZKu6tjmKkkfKj9hXCBpbUQ8vI2zbvNctl9u2+XyIaq+/v/2eK5uMtZqqzLWqezvB5JWRMTXJ9is8bWqM1fT62V7L9t7lMuzJL1d0gMdmzW6VnVmanqdIuKsiNg3IoZU9eC3EfGBjs1Sv/9mTPYvRMRG25+UdIOq/3nww4i43/bHyu3fkXStqp8utiStl/Th6Rt5m+Y6SdLHbW+UtEHSKVF+FNorti9W9dPf2bZXSTpH1Q8o0taqxkyNr5OqZysflHRvOY4oSZ+XtF/bXI2vVc25ml6vvSVdaHtAVcR+ERFXJ38P1pkp43H1PNmt2mKWhK8fACB+Ew4A0hBgAEhCgAEgCQEGgCQEGACSEGBMG9s/tn1Sx3X72P5ljb+7bpzrLrd9QtvnD9r+Qtvnl9o+cYqznmr7W+XyubY/M5X7qbmvebaPbfu8p/vD9oMAo6ci4l8RcVL3Lcd1m6TDJMn2npLWSVrYdvvCsk1X5f+nZpmn6v+aAlsgwJAk2b7C9nJX53I93fbHbX+17fZTbY+Wy1+0/YDtm2xfvLVnc7aHXM47XO7jMtvX2/5r+/23bT/b9hLbx0m6VSXA5c+rJe1Vfmtpf0kbIuLftt9v+17b99n+Stt9rbN9vu2lqk4C82Hbf7H9O1W/YNFtTT5r+3ZX54k9r+3rWWH7e2Wtbiy/+SXbB5dtl9j+WplnZ0nnSzrZ1TlwTy53f6DtW2yvtH1Gt1nwwkSAMea0iHiTpPmSzpB0maT2l/cnS7rE9nxJ71F1VrATy/aTMa/c1+tVRWnz7+HbniPpGklnR8Q1kpZLel2J2GGSlkh6UNJryue32t5H0lckHVnu++C2wxYvUnXO40NVnSv6PFXhfYekA7c2pO2jJB2g6jSn8yS9yfbh5eYDJH07Il4r6X9lPSTpR5I+FhELJW2SpHJq1LMlXRIR8yLikrLtXElHl/s/x9X5JrCDIcAYc4btuyX9UdXJSfaXtNL2gvLy/9WqnpG+RdKVEbGhnB/3V5Pcz28iYm1EPCXpz5JeWa6fKek3ks6MiJskKSKelnS/qpPHL1B1GsglquJ7mKrDDwdLuiUi1kTERkk/U3XCeamK4KXl8qFt2z0jaSyEEzmqfNyp6ixec1WFV5L+HhF3lcvLJQ2V8yDsHhFjh0Qu6nL/10TE0xHxqKrTN87psj1egAgwZPsIVSdPWVhOFXinpF1URep9qp7hXV5+b3+80/dNxtNtlzfpufORbFQVs6M7tr9NVVB3j4jHVf0DMRbgW7vM81REbGr7fDK/d29JXy7PWudFxHBE/GArX8Nk12WidcAOhABDkl4i6fGIWG97rqpnm1J1GOIESe/Xc88Y/yDpXa7eA2w3Ve82MB1C0mmS5nrL9/O7VdJHVb3FlCTdU+bbT9Wz46WS3lqOHQ+UWX83zv0vlXSE7T3Ly/33dpnnBkmnla9Rtgdtv2zC4at/HJ4sZ9SSqrNvjXlS1dsZAVsgwJCk6yXNsH2PpC+pepY5FpU/qzqJ9p/KdberOoXf3aoCvUzS2rb7+q7tVeVjyWSGKM9WT5H0NtufKFffpup9/paUbTaqesm+LCKeLacOPEvSzWWmOyLiynHu+2FJ55b7+bWqwwrtvtA296qIuFHVYYQltu+V9Et1j+hHJC0uX7f13LrcrOqHbu0/hAM4Gxomz/ZuEbHO9q6Sfi/p9Cjvm7YjG1uXcvlzkvaOiOe9izIwhuNOmIrFtg9UdZz4QuK72XG2z1L1ffWQpFNzx0G/4xkwACThGDAAJCHAAJCEAANAEgIMAEkIMAAk+T88xJIRz5if5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"avgLinkWordLength\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_1 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"avgLinkWordLength\"] = numeric_col.apply(outlier_remover_1.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"avgLinkWordLength\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9a4571c5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"redundancyMeasure\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_2 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"redundancyMeasure\"] = numeric_col.apply(outlier_remover_2.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"redundancyMeasure\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d066f788",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"embedRatio\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_3 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"embedRatio\"] = numeric_col.apply(outlier_remover_3.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"embedRatio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "55a0a700",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df.drop(axis=\"columns\", labels=\"embedRatio\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5f8b3a57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"frameTagRatio\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_4 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"frameTagRatio\"] = numeric_col.apply(outlier_remover_4.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"frameTagRatio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "04ecf615",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"tagRatio\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_5 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"tagRatio\"] = numeric_col.apply(outlier_remover_5.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"tagRatio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cc377924",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"imageTagRatio\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_6 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"imageTagRatio\"] = numeric_col.apply(outlier_remover_6.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"imageTagRatio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "108bdcc6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"lengthyDomain\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_7 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"lengthyDomain\"] = numeric_col.apply(outlier_remover_7.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"lengthyDomain\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ab27ed13",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAQb0lEQVR4nO3df5BdZX3H8fc32VZiUlEaZHDRrphU/AHGGluJjrOgtBACTpkyhKkD0g6WGQ1BqI5AHMm0WhlrLUS0Q2mhVQpWUSGRURyB2goFNxWQSqhbiJrUHxD8FQlqkm//OOfi3SW/Nu7ud/fu+zVzZ8997jn3eb7743PPffbe50ZmIkmafLOqByBJM5UBLElFDGBJKmIAS1IRA1iSivSNZef58+fnwMDABA1FknrT+vXrH83Mg0e3jymABwYGGBoaGr9RSdIMEBHf3FW7UxCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUZ02fCzQRr1qxheHi4dAybN28GoL+/v3QcE2XBggWsWLGiehhSOQN4lOHhYe65/wF2PP2gsjHMfvxHAHz3Z73345n9+GPVQ5CmjN77Cx8HO55+ENuOWFrW/5wNNwOUjmGidGqT5BywJJUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVmZQAXrNmDWvWrJmMriRNE+YC9E1GJ8PDw5PRjaRpxFxwCkKSyhjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSeVWr17N4OAg73nPe7j88ssZHBzkiiuu4Nprr2VwcJDrr79+RPuNN97I4OAga9euHbE9NDTEsccey/r16xkeHubEE09keHiYW2+9lcHBQW677Ta2bNnCueeey5YtW0a0dx/bvX3ppZcyODjIBz7wgXGvOzJzn3devHhxDg0NjbmTlStXAnDZZZeN+djJtnLlStY/9D22HbG0bAxzNtwMUDqGiTJnw8284vBDpsXvgiZWdy4MDg6O6diIIDOJCIAnt+fOncvWrVuZN28e8+fPZ+PGjQwMDLBp0ya2b99OX18fS5cuZe3atZx88sl89rOffbL9gAMOePJY4MntrVu3Ptnv7bffvl+1RsT6zFw8ut0zYEmlVq9ePeZjOieOmTliuxOWW7duZePGjQBs3LiR7du3A7B9+3bWrVtHZrJu3boR7d3Hdm93G++z4L5xvbfd2Lx5M9u2bXvyEW8qGx4eZtbP9/1ZgcZm1hM/Znj4J9Pid0ETa3h4mDlz5nDvvfdOar87d+4EYMeOHWM+du3atVxwwQXjNpa9ngFHxJsjYigihh555JFx61iSZrq9ngFn5pXAldDMAe9PJ/39/cD0mgPWxNh5wDNY4Byw+OUc8KOPPlo8kjrOAUsqdcwxx0xqf7NmNbE3e/bsMR970kknje9YxvXeJGmM3v3ud4/5mM6rHyJixHbnFQzz5s1jYGAAgIGBAfr6mif7fX19LFu2jIhg2bJlI9q7j+3e7jae879gAEuaAjpnwccddxynnHIKAKeeeipnn302AOecc86I9vPOOw+A888/f8T2JZdcwqxZs1i9ejWrVq1i7ty5rFq1iosuugiAiy++mDPPPJMjjzySM844Y0R797Hd2yeccAIw/me/4OuAn8LXAU8sXwesjumUC78qXwcsSVOMASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklSkbzI6WbBgwWR0I2kaMRcmKYBXrFgxGd1ImkbMBacgJKmMASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKGMCSVMQAlqQiBrAkFTGAJamIASxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBXpqx7AVDT78ceYs+Hmwv63AJSOYaLMfvwx4JDqYUhTggE8yoIFC6qHwObN2wHo7+/FoDpkSnyPpanAAB5lxYoV1UOQNEM4ByxJRQxgSSpiAEtSEQNYkooYwJJUxACWpCIGsCQVMYAlqYgBLElFDGBJKmIAS1IRA1iSihjAklTEAJakIgawJBUxgCWpiAEsSUUMYEkqYgBLUhEDWJKKRGbu+84RjwDf3M++5gOP7uex05U1zwwzreaZVi/86jX/VmYePLpxTAH8q4iIocxcPCmdTRHWPDPMtJpnWr0wcTU7BSFJRQxgSSoymQF85ST2NVVY88ww02qeafXCBNU8aXPAkqSRnIKQpCIGsCQVmfAAjojjI+LBiBiOiHdOdH8VIuK5EXFbRDwQEf8dESvb9oMi4gsR8Y3267OqxzreImJ2RHw1Ita113u65oh4ZkR8MiI2tD/vo2dAzW9rf6/vj4jrIuKAXqs5Iv4xIr4fEfd3te22xoi4sM20ByPiD/a33wkN4IiYDVwBnAC8GDg9Il48kX0W2Q5ckJkvAl4FvKWt853AFzNzIfDF9nqvWQk80HW912u+DPhcZh4BvIym9p6tOSL6gXOBxZn5UmA2sJzeq/ka4PhRbbussf3bXg68pD3mw23WjV1mTtgFOBr4fNf1C4ELJ7LPqXABbgSOAx4EDm3bDgUerB7bONd5WPuLeSywrm3r2ZqBZwAP0/7zuqu9l2vuB74NHAT0AeuA3+/FmoEB4P69/VxH5xjweeDo/elzoqcgOj+8jk1tW8+KiAHg5cBdwCGZ+R2A9uuzC4c2Ef4WeAews6utl2s+HHgEuLqddrkqIubSwzVn5mbgr4FvAd8BfpSZt9DDNXfZXY3jlmsTHcCxi7aefd1bRMwDbgDOy8wfV49nIkXEMuD7mbm+eiyTqA/4HeAjmfly4KdM/6fee9TOe74BeD7wHGBuRLyxdlTlxi3XJjqANwHP7bp+GPB/E9xniYj4NZrwvTYzP9U2fy8iDm1vPxT4ftX4JsCrgZMjYiNwPXBsRHyM3q55E7ApM+9qr3+SJpB7uebXAw9n5iOZ+QvgU8AServmjt3VOG65NtEB/BVgYUQ8PyJ+nWbi+qYJ7nPSRUQA/wA8kJl/03XTTcCZ7faZNHPDPSEzL8zMwzJzgObnemtmvpHervm7wLcj4oVt0+uAr9PDNdNMPbwqIp7e/p6/juYfj71cc8fuarwJWB4RT4uI5wMLgbv3q4dJmNheCvwP8L/AxdUT7RNU42tonoLcB9zTXpYCv0nzT6pvtF8Pqh7rBNU/yC//CdfTNQOLgKH2Z/0Z4FkzoObVwAbgfuCjwNN6rWbgOpo57l/QnOH+6Z5qBC5uM+1B4IT97de3IktSEd8JJ0lFDGBJKmIAS1IRA1iSihjAklTEAO4xETHQvaLTJPQ32LUS2sl7W/Gue/+utrMi4p728vOI+Fq7/b493M/bIuKJiDhwN2N5U0R8qF29bEv7Glba1csyIg5rrx8YEY9FxH79LUTE7RGx2w9rjIhrIuLhtp57I+J1+3CfF426fsf+jE1TnwGs/RYRfd3XM/OmzNxtaO5OZl6dmYsycxHNO4qOaa/vKcxPp3mjzx/u5b5/CHwXeFHbtAT4avsVmtXr7srMnU89eqTR9Y7B29vazgP+bh/2HxHAmblkdztqejOAe9PsiPj7dg3XWyLiJRHxX50bI2JhRKxvtzdGxKURcXd7WdC2HxwRN0TEV9rLq9v2SyLiyoi4Bfjn7k47Z53t9jURcXlE3BERD0XEH40eZES8sl3U5vBd3BYR8f5o1qD9WkSc1nXbC4B5wCqaIN6bL/PLwF0CfHDU9TuiWeP26ravr0bEMV01fSIi1gK3RMSciLg+Iu6LiI8Dc9r9Zrc1d8b7tl2M4066Fm2JiM9ExPr25/Tmtu19wJz2jPnatm3r3r4nmp729xFdU9tC4PTMPDsi/pVmdbYfRcSizLwHOItm/dOOH2fm70bEGTQrnC2jWff2g5n5HxHxPJol9zpnka8AXpOZ2yJicA/jOJTmXYJH0Lx985OdGyJiCbAGeENmfmsXx55C866zlwHzga9ExJeyWZXqdJp3Lv078MKIeHZm7mktgjuA1wJX0axo9gngz9rblgB/BbwFIDOPjIgjaML2t9t9jgaOyszHIuJ84PHMPCoijgI6D2yLgP5s1swlIp65i3EcT/PuuY4/ae9zTlvfDZn5zoh4a3vGPJbviaYhz4B708Nt0AKsp1nn9CrgrGgWjj4N+Jeu/a/r+np0u/164EMRcQ9NeD4jIn6jve2mzNy2D+P4TGbuzMyvA4d0tb+I5lNmT9pN+EIT3Ndl5o7M/B7wb8Ar29uWA9e30wafAk7dyzi+DCyJ5n37GzPzCZoTynk0DyZ3t/19FCAzNwDfBDoB/IXMfKzdfi3wsXa/+2jekgzwEHB4RKyJiOOB7tXw3h8RD7XHvber/dyIuBf4T5rFXRbupY49fU80DRnAvelnXds7aJ7p3EDzySTLgPWZuaVrn9zF9iyaRaYXtZf+zPxJe9tP92Mc3Uv4fQd4gubMfHd2teQf7VnnQuAL0azEtpy9TENk5jdo1mw4iWYaAJoHprNoHqy27q6/1uh6n/L+/cz8Ac2Z6e00Z9NXdd38dmABzZTJP7V1DNI8yB2dmS+jmZc+YE917GWMmoYM4BmiPev7PPAR4OpRN5/W9bUTULcAb+3sEBGLxnE4PwROBN67hymMLwGntXOrB9Oced5NE7aXZOZAe3kO0B8Rv7WXPu+k+fikO7uun0czPdHp748B2qmH59EstLKrcXX2eylwVLs9H5iVmTcA76JZpvJJ7dn6ZcCsaD5D7EDgB5n5eDvl8aqu3X8RzfKm+/o90TRlAM8s19Kcvd0yqv1pEXEXTUB1/nl0LrC4/WfT14FzxnMg7VPok4ArIuL3drHLp2me3t8L3Aq8I5vlIJe3t43ed/leuvwyzdP8ofb6nTTzwZ0A/jDNPy+/BnwceFNm/uwp99I8gM2LiPtoPg2kE4D9wO3tlM01NB9bM0I2K1/9ZXvc54C+9n7+gmYaouNK4L7OP+FG1bmr74mmKVdDm0Ei4s+BAzPzXV1tG2k+cPHRsoFJM5SvgpghIuLTwAtoPkBT0hTgGbAkFXEOWJKKGMCSVMQAlqQiBrAkFTGAJanI/wM1/LhDEkeUfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOBUlEQVR4nO3df5DcdX3H8dcrd1UOKb8azMAhHnARsIixjZZExkFxRlQQdcokjI5Cxx+d0SPQogNOnbFTtXbs1MbT2okooCCoBCjtMBpGi1qgQE5CoIDjTvghV8BA5IdNAIV3//h8VpbLXi53Zvd9m30+Zm6y+929/X72k7vnfe97t59zRAgA0H0LsgcAAP2KAANAEgIMAEkIMAAkIcAAkGRwNndeuHBhjIyMdGgoALB7mpiYeCQiDpi6fVYBHhkZ0fr163fdqACgD9i+r912TkEAQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkmdXfhOtn4+PjajQa2cNoa3JyUpI0PDycPJIco6OjGhsbyx4GMGsEeCc1Gg1tuOMuPbvn/tlD2c7A1sclSQ893X//nQNbt2QPAZiz/vuM/T08u+f+2nbk27KHsZ2hu6+RpHk5tk5rPnegF3EOGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJJ0JcDj4+MaHx/vxq4AYJfqZL8GO/KoUzQajW7sBgB2uU72i1MQAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkGSwGzuZnJzUtm3btGrVqm7sriMajYYWPBPZw8AUC556Qo3Gkz39sYX5rdFoaGhoqCOPPeMRsO0P2V5ve/3mzZs7MggA6EczHgFHxBpJayRp6dKlczoEHB4eliStXr16Lu8+L6xatUoTmx7OHgameG6PvTV62KKe/tjC/NbJ7644BwwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJBnsxk5GR0e7sRsA2OU62a+uBHhsbKwbuwGAXa6T/eIUBAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQazB9BLBrZu0dDd12QPYzsDWx+VpHk5tk4b2LpF0qLsYQBzQoB30ujoaPYQpjU5+VtJ0vBwP4Zo0bz+vwF2hADvpLGxsewhANjNcA4YAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCSOiJ2/s71Z0n1z3NdCSY/M8X13Z8zL9Jib9piX9ubzvLw8Ig6YunFWAf592F4fEUu7srMewrxMj7lpj3lprxfnhVMQAJCEAANAkm4GeE0X99VLmJfpMTftMS/t9dy8dO0cMADghTgFAQBJCDAAJOl4gG2faPtnthu2z+30/uYz2y+z/Z+277L9P7ZX1e37277W9s/rv/tljzWD7QHbt9r+j3q97+fF9r62L7d9d/24Wca8FLbPrp9Hd9i+1PYevTY3HQ2w7QFJX5b0VkmvlHSa7Vd2cp/z3G8l/XVEHCXpWEkfqfNxrqQfRMRiST+o1/vRKkl3tVxnXqTVkr4XEUdKerXK/PT9vNgelnSmpKURcbSkAUkr1WNz0+kj4NdJakTEpoh4RtJlkk7p8D7nrYh4MCJ+Wi8/qfLJNKwyJxfVu10k6Z0pA0xk+2BJb5d0fsvmvp4X23tLeoOkr0lSRDwTEY+pz+elxaCkIduDkvaU9L/qsbnpdICHJf2i5foDdVvfsz0i6TWSbpK0KCIelEqkJb00cWhZ/lnSxyU917Kt3+flMEmbJV1QT82cb/slYl4UEZOS/lHS/ZIelPR4RKxTj81NpwPsNtv6/vfebO8laa2ksyLiiezxZLN9kqRfRsRE9ljmmUFJfyLpKxHxGkn/p3n+LXW31HO7p0g6VNJBkl5i+725o5q9Tgf4AUkva7l+sMq3CX3L9h+oxPeSiLiibn7Y9oH19gMl/TJrfEleL+kdtu9VOU31JtsXi3l5QNIDEXFTvX65SpD7fV4k6c2S7omIzRHxG0lXSFquHpubTgf4FkmLbR9q+0UqJ8mv7vA+5y3bVjmfd1dE/FPLTVdLen+9/H5J/9btsWWKiPMi4uCIGFH5GPlhRLxXzMtDkn5h+4i66QRJd6rP56W6X9Kxtvesn1cnqPxMpafmpuOvhLP9NpXzewOSvh4Rn+noDucx28dJ+omk2/X8uc5PqJwH/o6kQ1Q+sE6NiC0pg0xm+3hJ50TESbb/SH0+L7aXqPxg8kWSNkk6Q+XAqa/nRZJs/62kFSq/XXSrpA9I2ks9NDe8FBkAkvBKOABIQoABIAkBBoAkBBgAkhBgAEhCgHcztkds39HF/R3fsnrZO2Za8a71/i3bzrC9ob49Y/v2evlzO3ics20/ZXufacZyuu0v1dXEHq2/K6q6mljUtSdkex/bW2zP6XPB9nW2p/1DkLYvtH1PfT632T5hJx7zE1Ou3zCXsWH+I8CYs7oIyu9ExNURMW00pxMRF0TEkohYovJKyTfW6zuK+WkqL/R51wyP/ZikhyQdVTctV/md0eX1+rGSboqI57Z/7xea+nxn4WP1uZ0l6V934v4vCHBELJ/ujuhtBHj3NGD7q3Wt1HW2/9j2T5s32l5se6Jevtf2P9i+ub6N1u0H2F5r+5b69vq6/VO219heJ+kbrTttHnXWyxfa/qLtG2xvsv3nUwdp+7V1kZnD2txm2593Wev1dtsrWm47XOUX7v9GJcQzuV7PB3e5pC9MuX6Dy1qyF9R93Wr7jS3P6bu2/13SOttDti+zvdH2tyUN1fsN1OfcHO/ZbcZxo1oWo7J9le2J+v/0obrtcyorfG2wfUnd9uuZ5gS9aa5f0TG/LZZ0WkR80PZ3VFZde9z2kojYoPJqqgtb7v9ERLzO9vtUXrV4kso6tF+IiP+yfYik7+v5o8g/lXRcRGyrr1ybzoGSjpN0pMpLRC9v3mB7uaRxSadExP1t3vfdkpaorIG7UNIttn9cV7g6TdKlKq8qPML2SyNiR6/5v0FlWcfzVVYY+66kD9fblkv6e0kfkaSIeJXtI1Vi+4p6n2WSjomILbb/StLWiDjG9jGSml/YlkgarmvTyva+bcZxoqSrWq7/RX3Mofr81kbEubY/Wo+YZzMn6EEcAe+e7qmhlaQJSSMq8TnDZZH8FZK+1XL/S1v+XVYvv1nSl2xvUInn3rb/sN52dURs24lxXBURz0XEnZIWtWw/SuUv2J48TXylEu5LI+LZiHhY0o8kvbbetlLSZfW0wRWSTp1hHNdLWm77UEn3RsRTKgeUe6l8Mbm57u+bkhQRd0u6T1IzwNe2vJz1DZIurvfbKGlj3b5J0mG2x22fKKl1lbvP295U3++zLdvPtH2bpP9WWbRq8QzPY0dzgh5EgHdPT7dcflblO521Kn+Z5CRJExHxaMt9os3lBZKWNc/NRsRwXUReKssiznYcrUuTPijpKZUj8+m0W8pU9ahzsaRrXVZPW6kZTkNExM8l7SfpZJXTAFL5wnSGyherX0+3v2rq893u9fsR8SuVI9PrVI6mWxeW/5ikUZVTJhfV53G8yhe5ZRHxapXz0nvs6HnMMEb0IALcJ+pR3/clfUXSBVNuXtHybzNQ6yR9tHkHl0VhdpXHVP76xWd3cArjx5JW1HOrB6gced6sEttPRcRIfTtI0rDtl8+wzxtV/uTRjS3Xz1I5PdHc33skqZ56OETSz6YZV/N+R0s6pl5eKGlBRKyV9EmVZSN/px6tr5a0wPZbJO0j6VcRsbWe8ji25e6/cVm2dGfnBD2KAPeXS1SO3tZN2f5i2zepBKr5w6MzJS2tP2y6U9Jf7sqB1G+hT5b0Zdt/1uYuV6p8e3+bpB9K+nhdnnFlvW3qfVfOsMvrVb7NX1+v36hyPrgZ4H9R+eHl7ZK+Len0iHh6u0cpX8D2sr1R5S94NAM4LOm6esrmQknnTX3HKCtffbq+3/ckDdbH+TuV0xBNayRtbP4QbsrzbDcn6FGshtZHbJ8jaZ+I+GTLtntV/rDhI2kDA/oUvwXRJ2xfKelwSW/KHguAgiNgAEjCOWAASEKAASAJAQaAJAQYAJIQYABI8v+B241OD4nHrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"hyperlinkToAllWordsRatio\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_8 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"hyperlinkToAllWordsRatio\"] = numeric_col.apply(outlier_remover_8.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"hyperlinkToAllWordsRatio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "091e9254",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAASZklEQVR4nO3df5BV5X3H8c+X3eXnGgYWay2oV7MQIdKoUBMnHWNaMLvrFJqpM0kndiHR5o82y0p1OlSgy1JaDY10WNLa+qMDRptkTGwrCtvgj07apIlZElApWK9mmUgsykpRIiq7PP3jPHdz97L37l3ce7972fdrZmfPPfc8P85zDh/Ofe69Zy2EIABA+Y3z7gAAjFUEMAA4IYABwAkBDABOCGAAcFI9nI1nzJgRUqlUiboCAGen3bt3HwkhnJu7flgBnEql1NXVNXK9AoAxwMwODraeKQgAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwM62/CvV9btmxROp2WJB06dEiSNHPmzP7n6+vr1dLSUs4uAYCbsgZwOp3Wnuf3q2/ydFW9fUyS9L/vJl2oevuNcnYFANyVfQqib/J0nbi0SX2T69Q3uU4nLm2Kj6eXuysA4Io5YABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcFJdjka2bNlSkvpaWlpGtF4AKKeyBHA6nR7V9QGAB6YgAMAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCk2rsDZ2Lv3r2SpGuvvbYs7U2bNk1Hjx4tuE1NTY1aWlq0adMmSdLUqVN17NixvNtXVVWpr6+v//Fdd92lBQsW6KmnntL69es1a9YsTZw4UWYmM1NVVZVuvfVWdXR0qLm5WW1tbVq/fr3uv/9+hRB00003ae3atbrgggt0xx13SJLa29vV1tamo0ePqrW1VbfddpvuvPNOvffeexo/frxWrVqljRs39pepq6uTJPX09Ki9vV0rVqxQR0eH2traVFdXp3Q6rdbWVm3evFn19fUFxyNTR3Nzs9auXatzzz1XR44cUUdHx5BlhyvTVltbmyRp7dq1CiFow4YN/ftUqEy+bVDYcM6HfCrhOJSyjxZCKHrjhQsXhq6urmE30tra2r+8++XDOnFpkyYd2CFJOnFpkyRp0oEdWnDJedq8efOQ9ZUreIfLzDSc8cxWW1urxx57TIsWLVJvb++g26RSKR08eFBTpkzR8ePHVVtbq+PHj/eXzywvXbpUIQRt375dS5Ys0d69e9Xd3a3q6uoBdWc/Xrp0qVauXClJ2rRpk7Zv366LLrpIBw8e1JIlS7Ry5UotX75c3d3dSqVS2rp1a8H9ydSR6Wv2PgxVdrgybS1ZskQhBD366KOn7VOhMvm2QWHDOR/yqYTjMBJ9NLPdIYSFuesrbgqioaHBuwt5nWn4StLx48d133335Q1fSeru7lYIoT/QsoMte/nxxx9XZ2enQgjasWOHuru7Jem0urMf79ixQz09Perp6ekvm2mvs7NTXV1d/fV0d3crnU7n7Wd2Hdn9KqbscGW3tXPnTu3cubP/uZ07d6qnp6dgmc7OzkG3QWHpdLro8yGfSjgOpe5jWaYgDh06pBMnTkiSxr03eEiNe+dNpdNvDbhaHsw777wz4v0bLR588MERqae3t1dmJkk6efJkUWVOnjypBx54QCEEnTp1asBzfX19Wrdu3YB1GzZsyHvVs23bttPqKLbscGW3dfLkyQH/CWb2KfeqJbtMX1/foNugsA0bNpz2eLjHtBKOQ6n7OOQVsJl90cy6zKzr9ddfH7GGUVpncjW+a9cuPfHEE4NeKQ92JZvPYHUUW3a4stvK3ecQgnbt2lWwTG9v76DboLDcY3gmx7QSjkOp+zjkFXAI4R5J90jJHPCZNDJz5sz+5d0vHx50m1MTP6D6IuaAR+v872hzJvPRixcv7p+2yJ0rnjhx4mlzufksWrTotDqyFSo7XNlt5e6zmWnx4sUFy1RXVw+6DQpLpVIDQvdMjmklHIdS97Hi5oAnTpzo3YWSufHGG0eknurqatXU1EhS/++h1NTUqLm5WcuWLdO4cQNPi6qqqtOmINasWZO3rsHqKLbscGW3VVNTM2B/M/tUqExVVdWg26Cw3GN4Jse0Eo5DqftYcQHc2dnp3YW8MvOuZ6K2tlY333yzqqvzvyhJpVIyM9XW1vaXyS6fcf3116uhoUFmpqampv6rk9y6sx83NTWprq5OdXV1/WUz7TU0NGjhwoX99aRSqYIfO8quI7tfxZQdruy2Ghsb1djY2P9cY2PjoB8byi7T0NAwaj/+NJrV19cXfT7kUwnHodR9rLgA9jBt2rQht6mpqRkwOT916tSC21dVVQ143N7eLkm6/fbbJUmzZs1SfX29Zs+erTlz5mju3Llas2aN5s+fr3Xr1mnKlClqb2/XvHnzNHfuXK1bt06TJk3SnDlz+q9k58+fr+bmZq1Zs0ZTpkzR6tWrNWHCBJmZJkyYoNWrVw8ok5Epm2kv81ymnmKudjJ1ZPp14YUXavLkySN69ZvbVma/M2NS6GoluwzOzHDOh3wq4TiUso8V+TngTH3FbAsA3s6azwEDwNmCAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBSXY5G6uvrJUnpdHpE6wOASlaWAG5paZEktba2jmh9AFDJmIIAACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJwQwADghAAGACcEMAA4IYABwAkBDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4KS63A1Wvf2GJh3Yoaq3eyRJkw7s6F8vnVfu7gCAm7IGcH19ff/yoUO9kqSZMzOhe96A5wHgbFfWAG5paSlncwAwqjEHDABOCGAAcEIAA4ATAhgAnBDAAOCEAAYAJwQwADghgAHACQEMAE4IYABwQgADgBMCGACcEMAA4IQABgAnBDAAOCGAAcAJAQwATghgAHBCAAOAEwIYAJxYCKH4jc1el3TwDNuaIenIGZYdKxijoTFGQ2OMilPOcboohHBu7sphBfD7YWZdIYSFZWmsQjFGQ2OMhsYYFWc0jBNTEADghAAGACflDOB7ythWpWKMhsYYDY0xKo77OJVtDhgAMBBTEADghAAGACclD2AzazCzF8wsbWarSt3eaGBm3Wb2nJntMbOuuG66me0ysxfj72lZ2/9ZHJ8XzOxTWesXxHrSZtZhZhbXTzCzb8b1PzSzVNl3cpjM7B/N7DUzez5rXVnGxMyWxTZeNLNlZdrlYcszRuvM7FA8l/aYWVPWc2NxjC4ws6fNbL+Z7TOz1ri+Ms+lEELJfiRVSXpJ0iWSxkvaK2leKdscDT+SuiXNyFm3UdKquLxK0pfj8rw4LhMkXRzHqyo+94ykqyWZpJ2SGuP6P5L093H5s5K+6b3PRYzJNZKulPR8OcdE0nRJL8ff0+LyNO/xGMYYrZN02yDbjtUxOl/SlXH5HEn/E8eiIs+lUl8BXyUpHUJ4OYTwnqRvSFpa4jZHq6WStsXlbZJ+N2v9N0II74YQfiopLekqMztf0gdCCP8VkqP/QE6ZTF3fkvTbmf+9R6sQwnclvZGzuhxj8ilJu0IIb4QQjkraJalhpPdvJOQZo3zG6hi9GkL4cVx+S9J+STNVoedSqQN4pqSfZT1+Ja472wVJ3zGz3Wb2xbjuvBDCq1JyEkn6lbg+3xjNjMu56weUCSH0Sjomqa4E+1Fq5RiTs+Ec/JKZPRunKDIvrcf8GMWpgSsk/VAVei6VOoAHuyobC597+3gI4UpJjZL+2MyuKbBtvjEqNHZn+7iO5JhU+ljdLemDki6X9Kqku+L6MT1GZlYr6duSbgkhvFlo00HWjZpxKnUAvyLpgqzHsyT9vMRtugsh/Dz+fk3SPyuZijkcX/Yo/n4tbp5vjF6Jy7nrB5Qxs2pJU1X8S9fRpBxjUtHnYAjhcAihL4RwStK9Ss4laQyPkZnVKAnfh0IIj8TVFXkulTqAfyRptpldbGbjlUxoP1riNl2Z2RQzOyezLOk6Sc8r2e/Mu6bLJP1rXH5U0mfjO68XS5ot6Zn4MuotM/tYnH9qzimTqesGSU/FeaxKU44x+TdJ15nZtPjy/bq4riJkQiX6tJJzSRqjYxT36X5J+0MIm7KeqsxzqQzvWjYpeafyJUmrS92e94+ST3zsjT/7MvusZA7pSUkvxt/Ts8qsjuPzguI7sXH9QiX/4F6S9FX98puLEyU9rOQNhWckXeK930WMy9eVvIQ+qeRK4qZyjYmkL8T1aUmf9x6LYY7R1yQ9J+lZJcFw/hgfo99U8rL/WUl74k9TpZ5LfBUZAJzwTTgAcEIAA4ATAhgAnBDAAOCEAAYAJwQwTmPJ3dxmvN9tysXMas3sH8zspXiHrO+a2UfNLGVZdxZ7H/U3mllXvAPXATP7ykj0O6eNW8xs8kjXi9GNAMbZ4D4l31SaHUL4sKTlSv7k+PtiZtVmdpmSz4jeGEKYK+kyJXfBGmm3SCKAxxgCeIwzs3+JNw3al3XjoMxzqXjFty3eDOZbOVdpLWb243hP1UtjmavM7Ptm9pP4+0Nx/XIze8TMOuO9VDdmtXM8a/kGM9sal7ea2d2W3P/1ZTP7RLwhzf6sbT4o6aOS1oTk67oKyd33Ho9VVpnZvXH/vmNmk2K5PzSzH5nZXjP7dma/YpubzOxpSV+W9KeS/jKEcCDW3RtC+Lu47UVm9mQcmyfN7MKsOm7I3T8zu9bM/j2O4wEze8gSKyT9mqSnY7sYIwhgfCGEsEDJt4JWmFnuXdU+JOmeEMKvS3pTyb1SM46E5KZDd0u6La47IOmaEMIVkv5c0l9lbX+5pM9Imi/pM2aW/b36fKZJ+i1JKyVtl/Q3kj4sab6ZXR6X94QQ+vKUny3pb+OV8f9J+r24/pEQwm+EED6i5JaGN2WVmSNpUQjhViVXvLvz1P1VSQ/EsXlIUkcR+3OFkqvdeUq+NfnxEEKHknsKfDKE8Mki6sBZggDGCjPbK+kHSm40Mjvn+Z+FEL4Xlx9U8lXQjMyNUHZLSsXlqZIejnOvmbDMeDKEcCyE8I6k/5Z0URH92x6Sr2s+J+lwCOG5eKW7L6vNQn4aQtgzSD8vM7P/MLPnJH0up58PFwj0bFdL+qe4/DUNHJt8ngkhvBL3YY+K2wecpQjgMczMrpW0SNLV8UrwJ0q+B58t97vq2Y/fjb/7JFXH5b+Q9HQI4TJJv5NT37tZy9llsuvMbT9T5lRO+VOx/D5JHzGzfOdyvja3SvpSCGG+pPacdn+RtbxP0oI8defK7Eev4r+teKOX8UX0B2MQATy2TZV0NITwdpzD/dgg21xoZlfH5d+X9J9F1HkoLi8vsh+HzWxuDNFPF1lGkhRCeElSl6T2GHYys9lmNtRfXjlH0quW3NrwcwW2+2tJt5vZnFj3ODP7k/jc95Xc4U+xjszYdOuXob1UUk0Ru/JW7BPGEAJ4bOuUVG1mzyq5cv3BINvsl7QsbjNdyXxvIRsl3WFm31PyNwGLsUrSY5KeUnI3sOG6WdKvSkrHKYV7NfR9Wtcq+UsKu5TMWw8qhPCskjnbr5vZfiV3z8rcInKFpM/HsfkDSa1x/b2SPmFmzyh5g/AXGto9knbyJtzYwt3QkJclf/LlsTidAGCEcQUMAE64AgYAJ1wBA4ATAhgAnBDAAOCEAAYAJwQwADj5fx2DkZQ5y9Z9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"alphanumCharCount\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_9 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"alphanumCharCount\"] = numeric_col.apply(outlier_remover_9.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"alphanumCharCount\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9bc3918",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"linksCount\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_10 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"linksCount\"] = numeric_col.apply(outlier_remover_10.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"linksCount\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a6edcaf0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"wordCount\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_11 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"wordCount\"] = numeric_col.apply(outlier_remover_11.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"wordCount\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1fefa98b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"parametrizedLinkRatio\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_12 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"parametrizedLinkRatio\"] = numeric_col.apply(outlier_remover_12.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"parametrizedLinkRatio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "69995fca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_col = main_df[\"spellingErrorsRatio\"]\n",
    "sns.boxplot(x=numeric_col)\n",
    "plt.show()\n",
    "\n",
    "outlier_remover_13 = OutlierRemoval(numeric_col.quantile(0.25), numeric_col.quantile(0.75))\n",
    "main_df[\"spellingErrorsRatio\"] = numeric_col.apply(outlier_remover_13.removeOutlier)\n",
    "\n",
    "sns.boxplot(x=main_df[\"spellingErrorsRatio\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c498b407",
   "metadata": {},
   "source": [
    "## 1.4 One-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "09dd6ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown               2324\n",
       "recreation            1198\n",
       "arts_entertainment     962\n",
       "business               873\n",
       "health                 527\n",
       "sports                 387\n",
       "culture_politics       349\n",
       "computer_internet      293\n",
       "science_technology     293\n",
       "gaming                  79\n",
       "religion                72\n",
       "law_crime               33\n",
       "weather                  5\n",
       "Name: alchemy_category, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"alchemy_category\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae14c2f",
   "metadata": {},
   "source": [
    "Alchemy category is one-hot encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "44d1e14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names_to_one_hot = [\"alchemy_category\"]\n",
    "main_df = pd.get_dummies(main_df, columns=column_names_to_one_hot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6b6b93cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>webpageDescription</th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
       "      <th>AvglinkWithOneCommonWord</th>\n",
       "      <th>AvglinkWithTwoCommonWord</th>\n",
       "      <th>AvglinkWithThreeCommonWord</th>\n",
       "      <th>AvglinkWithFourCommonWord</th>\n",
       "      <th>redundancyMeasure</th>\n",
       "      <th>frameTagRatio</th>\n",
       "      <th>...</th>\n",
       "      <th>alchemy_category_culture_politics</th>\n",
       "      <th>alchemy_category_gaming</th>\n",
       "      <th>alchemy_category_health</th>\n",
       "      <th>alchemy_category_law_crime</th>\n",
       "      <th>alchemy_category_recreation</th>\n",
       "      <th>alchemy_category_religion</th>\n",
       "      <th>alchemy_category_science_technology</th>\n",
       "      <th>alchemy_category_sports</th>\n",
       "      <th>alchemy_category_unknown</th>\n",
       "      <th>alchemy_category_weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.washingtonpost.com/blogs/early-lead...</td>\n",
       "      <td>{\"title\":\"Nebraska California State Bakersfiel...</td>\n",
       "      <td>0.589160</td>\n",
       "      <td>2.055838</td>\n",
       "      <td>0.570962</td>\n",
       "      <td>0.285481</td>\n",
       "      <td>0.094617</td>\n",
       "      <td>0.048940</td>\n",
       "      <td>0.468646</td>\n",
       "      <td>0.028002</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.holiday-rentals.co.uk/info/holiday-...</td>\n",
       "      <td>{\"title\":\"Travel Cookbook Discover holiday hom...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.097222</td>\n",
       "      <td>0.697368</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494215</td>\n",
       "      <td>0.072948</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://www.huffingtonpost.com/2011/10/27/lefto...</td>\n",
       "      <td>{\"title\":\"5 Ways With Leftover Rice 5 ways wit...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>0.054522</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://www.sun-sentinel.com/</td>\n",
       "      <td>{\"title\":\"South Florida Broward Palm Beach and...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>0.037284</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://socoting.com/protecting-and-serving/</td>\n",
       "      <td>{\"url\":\"socoting protecting and serving\",\"titl...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625323</td>\n",
       "      <td>0.142339</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  http://www.washingtonpost.com/blogs/early-lead...   \n",
       "1  http://www.holiday-rentals.co.uk/info/holiday-...   \n",
       "2  http://www.huffingtonpost.com/2011/10/27/lefto...   \n",
       "3                       http://www.sun-sentinel.com/   \n",
       "4        http://socoting.com/protecting-and-serving/   \n",
       "\n",
       "                                  webpageDescription  alchemy_category_score  \\\n",
       "0  {\"title\":\"Nebraska California State Bakersfiel...                0.589160   \n",
       "1  {\"title\":\"Travel Cookbook Discover holiday hom...                0.630952   \n",
       "2  {\"title\":\"5 Ways With Leftover Rice 5 ways wit...                0.283897   \n",
       "3  {\"title\":\"South Florida Broward Palm Beach and...                0.630952   \n",
       "4  {\"url\":\"socoting protecting and serving\",\"titl...                0.283897   \n",
       "\n",
       "   avgLinkWordLength  AvglinkWithOneCommonWord  AvglinkWithTwoCommonWord  \\\n",
       "0           2.055838                  0.570962                  0.285481   \n",
       "1           2.097222                  0.697368                  0.236842   \n",
       "2           2.483680                  0.781333                  0.341333   \n",
       "3           2.919255                  0.629328                  0.323829   \n",
       "4           1.571429                  0.380952                  0.285714   \n",
       "\n",
       "   AvglinkWithThreeCommonWord  AvglinkWithFourCommonWord  redundancyMeasure  \\\n",
       "0                    0.094617                   0.048940           0.468646   \n",
       "1                    0.078947                   0.000000           0.494215   \n",
       "2                    0.138667                   0.066667           0.390071   \n",
       "3                    0.142566                   0.095723           0.505976   \n",
       "4                    0.000000                   0.000000           0.625323   \n",
       "\n",
       "   frameTagRatio  ...  alchemy_category_culture_politics  \\\n",
       "0       0.028002  ...                                  0   \n",
       "1       0.072948  ...                                  0   \n",
       "2       0.054522  ...                                  0   \n",
       "3       0.037284  ...                                  0   \n",
       "4       0.142339  ...                                  0   \n",
       "\n",
       "   alchemy_category_gaming alchemy_category_health  \\\n",
       "0                        0                       0   \n",
       "1                        0                       0   \n",
       "2                        0                       0   \n",
       "3                        0                       0   \n",
       "4                        0                       0   \n",
       "\n",
       "   alchemy_category_law_crime  alchemy_category_recreation  \\\n",
       "0                           0                            0   \n",
       "1                           0                            0   \n",
       "2                           0                            0   \n",
       "3                           0                            0   \n",
       "4                           1                            0   \n",
       "\n",
       "   alchemy_category_religion  alchemy_category_science_technology  \\\n",
       "0                          0                                    1   \n",
       "1                          0                                    0   \n",
       "2                          0                                    0   \n",
       "3                          0                                    0   \n",
       "4                          0                                    0   \n",
       "\n",
       "   alchemy_category_sports  alchemy_category_unknown  alchemy_category_weather  \n",
       "0                        0                         0                         0  \n",
       "1                        0                         1                         0  \n",
       "2                        0                         0                         0  \n",
       "3                        0                         1                         0  \n",
       "4                        0                         0                         0  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8629242",
   "metadata": {},
   "source": [
    "## 1.5 Replacing webpageDescription with 2-columns \"body\", \"title\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9970e2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "col = main_df[\"webpageDescription\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8b533d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name = \"body\"\n",
    "col = col.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8737f0af",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_list = []\n",
    "for i in range(len(col)):\n",
    "    a = json.loads(col[i])\n",
    "    if \"body\" in a.keys() and a[\"body\"]!=' ':\n",
    "        col_list.append(a[\"body\"])\n",
    "    else:\n",
    "        col_list.append(\"Unkonown\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8e73ba99",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df.insert(1, col_name, col_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6f8b24e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_name = \"title\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "523d47d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7395"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_list_title = []\n",
    "for i in range(len(col)):\n",
    "    a = json.loads(col[i])\n",
    "    if \"title\" in a.keys() and a[\"title\"]!=' ':\n",
    "        col_list_title.append(a[\"title\"])\n",
    "    else:\n",
    "        col_list_title.append(\"Unkonown\")\n",
    "        \n",
    "len(col_list_title)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "591ece4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df.insert(1, col_name, col_list_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5a1a2a98",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df.drop(axis=\"columns\", labels=\"webpageDescription\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b4fb3851",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>body</th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
       "      <th>AvglinkWithOneCommonWord</th>\n",
       "      <th>AvglinkWithTwoCommonWord</th>\n",
       "      <th>AvglinkWithThreeCommonWord</th>\n",
       "      <th>AvglinkWithFourCommonWord</th>\n",
       "      <th>redundancyMeasure</th>\n",
       "      <th>...</th>\n",
       "      <th>alchemy_category_culture_politics</th>\n",
       "      <th>alchemy_category_gaming</th>\n",
       "      <th>alchemy_category_health</th>\n",
       "      <th>alchemy_category_law_crime</th>\n",
       "      <th>alchemy_category_recreation</th>\n",
       "      <th>alchemy_category_religion</th>\n",
       "      <th>alchemy_category_science_technology</th>\n",
       "      <th>alchemy_category_sports</th>\n",
       "      <th>alchemy_category_unknown</th>\n",
       "      <th>alchemy_category_weather</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.washingtonpost.com/blogs/early-lead...</td>\n",
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       "      <td>http://www.holiday-rentals.co.uk/info/holiday-...</td>\n",
       "      <td>Travel Cookbook Discover holiday home owners f...</td>\n",
       "      <td>If you re looking for a truly authentic gastro...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.097222</td>\n",
       "      <td>0.697368</td>\n",
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       "      <th>2</th>\n",
       "      <td>http://www.huffingtonpost.com/2011/10/27/lefto...</td>\n",
       "      <td>5 Ways With Leftover Rice 5 ways with leftover...</td>\n",
       "      <td>Do you have leftover rice from last night s di...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://www.sun-sentinel.com/</td>\n",
       "      <td>South Florida Broward Palm Beach and Miami Dad...</td>\n",
       "      <td>Digital Memberships HOME DELIVERY Place an ad ...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://socoting.com/protecting-and-serving/</td>\n",
       "      <td>SoCoTing Protecting and serving</td>\n",
       "      <td>We believe that the short story is one of the ...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
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       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  http://www.washingtonpost.com/blogs/early-lead...   \n",
       "1  http://www.holiday-rentals.co.uk/info/holiday-...   \n",
       "2  http://www.huffingtonpost.com/2011/10/27/lefto...   \n",
       "3                       http://www.sun-sentinel.com/   \n",
       "4        http://socoting.com/protecting-and-serving/   \n",
       "\n",
       "                                               title  \\\n",
       "0  Nebraska California State Bakersfield in the b...   \n",
       "1  Travel Cookbook Discover holiday home owners f...   \n",
       "2  5 Ways With Leftover Rice 5 ways with leftover...   \n",
       "3  South Florida Broward Palm Beach and Miami Dad...   \n",
       "4                   SoCoTing Protecting and serving    \n",
       "\n",
       "                                                body  alchemy_category_score  \\\n",
       "0  Print Subscription Today s Paper Discussions G...                0.589160   \n",
       "1  If you re looking for a truly authentic gastro...                0.630952   \n",
       "2  Do you have leftover rice from last night s di...                0.283897   \n",
       "3  Digital Memberships HOME DELIVERY Place an ad ...                0.630952   \n",
       "4  We believe that the short story is one of the ...                0.283897   \n",
       "\n",
       "   avgLinkWordLength  AvglinkWithOneCommonWord  AvglinkWithTwoCommonWord  \\\n",
       "0           2.055838                  0.570962                  0.285481   \n",
       "1           2.097222                  0.697368                  0.236842   \n",
       "2           2.483680                  0.781333                  0.341333   \n",
       "3           2.919255                  0.629328                  0.323829   \n",
       "4           1.571429                  0.380952                  0.285714   \n",
       "\n",
       "   AvglinkWithThreeCommonWord  AvglinkWithFourCommonWord  redundancyMeasure  \\\n",
       "0                    0.094617                   0.048940           0.468646   \n",
       "1                    0.078947                   0.000000           0.494215   \n",
       "2                    0.138667                   0.066667           0.390071   \n",
       "3                    0.142566                   0.095723           0.505976   \n",
       "4                    0.000000                   0.000000           0.625323   \n",
       "\n",
       "   ...  alchemy_category_culture_politics  alchemy_category_gaming  \\\n",
       "0  ...                                  0                        0   \n",
       "1  ...                                  0                        0   \n",
       "2  ...                                  0                        0   \n",
       "3  ...                                  0                        0   \n",
       "4  ...                                  0                        0   \n",
       "\n",
       "   alchemy_category_health alchemy_category_law_crime  \\\n",
       "0                        0                          0   \n",
       "1                        0                          0   \n",
       "2                        0                          0   \n",
       "3                        0                          0   \n",
       "4                        0                          1   \n",
       "\n",
       "   alchemy_category_recreation  alchemy_category_religion  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "\n",
       "   alchemy_category_science_technology  alchemy_category_sports  \\\n",
       "0                                    1                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    0                        0   \n",
       "4                                    0                        0   \n",
       "\n",
       "   alchemy_category_unknown  alchemy_category_weather  \n",
       "0                         0                         0  \n",
       "1                         1                         0  \n",
       "2                         0                         0  \n",
       "3                         1                         0  \n",
       "4                         0                         0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5055b5d",
   "metadata": {},
   "source": [
    "## 1.6 Modifying url column by deleting symbols and strings which are not useful in predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "da1b312a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['http://www.washingtonpost.com/blogs/early-lead/post/nebraska-california-state-bakersfield-in-the-best-rain-delay-ever-video/2012/04/30/gIQAUZdxrT_blog.html',\n",
       "       'http://www.holiday-rentals.co.uk/info/holiday-ideas/travel-cookbook',\n",
       "       'http://www.huffingtonpost.com/2011/10/27/leftover-rice-recipes_n_1042537.html',\n",
       "       ...,\n",
       "       'http://www.laweekly.com/bestof/2011/award/best-belly-dance-workout-1509632/',\n",
       "       'http://www.kraftrecipes.com/recipes/buffalo-chicken-burgers-118650.aspx?cm_mmc=eml-_-rbe-_-20110526-_-1020',\n",
       "       'http://canarygirl.com/?p=80'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = main_df[\"url\"]\n",
    "col = col.to_numpy()\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "549b7118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['washingtonpost blogs early lead post nebraska california state bakersfield in the best rain delay ever video 2012 04 30 gIQAUZdxrT blog html',\n",
       "       'holiday rentals co uk info holiday ideas travel cookbook',\n",
       "       'huffingtonpost 2011 10 27 leftover rice recipes n 1042537 html',\n",
       "       ..., 'laweekly bestof 2011 award best belly dance workout 1509632',\n",
       "       'kraftrecipes recipes buffalo chicken burgers 118650 aspx cm mmc eml rbe 20110526 1020',\n",
       "       'canarygirl p 80'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charlist = ['/','?','=','-','_','&','(',')','@','$','%','#',':',';',\"<\",\">\",\".\"]\n",
    "for i in range(len(col)):\n",
    "    col[i] = col[i].replace('http://', '')\n",
    "    col[i] = col[i].replace('https://', '')\n",
    "    col[i] = col[i].replace('www.','').replace('.com','').replace('.org','')\n",
    "    for j in range(len(charlist)):\n",
    "        col[i] = col[i].replace(charlist[j],' ')\n",
    "    col[i] = \" \".join(col[i].split()) \n",
    "    \n",
    "    \n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4ee7928d",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df[\"url\"] = col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "89816e05",
   "metadata": {},
   "outputs": [
    {
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       "      <td>huffingtonpost 2011 10 27 leftover rice recipe...</td>\n",
       "      <td>5 Ways With Leftover Rice 5 ways with leftover...</td>\n",
       "      <td>Do you have leftover rice from last night s di...</td>\n",
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       "      <td>0.390071</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sun sentinel</td>\n",
       "      <td>South Florida Broward Palm Beach and Miami Dad...</td>\n",
       "      <td>Digital Memberships HOME DELIVERY Place an ad ...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>socoting protecting and serving</td>\n",
       "      <td>SoCoTing Protecting and serving</td>\n",
       "      <td>We believe that the short story is one of the ...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625323</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>lanascooking 2011 07 08 roasted potato salad</td>\n",
       "      <td>Unkonown</td>\n",
       "      <td>Unkonown</td>\n",
       "      <td>0.681418</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>0.367521</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.034188</td>\n",
       "      <td>0.017094</td>\n",
       "      <td>0.782845</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1475</th>\n",
       "      <td>marthastewart 255456 billys chocolate buttercream</td>\n",
       "      <td>Billy s Chocolate Buttercream Martha Stewart R...</td>\n",
       "      <td>Save to your Collections Sorry for the inconve...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.236559</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.413333</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>laweekly bestof 2011 award best belly dance wo...</td>\n",
       "      <td>Los Angeles Best Belly Dance Workout Swerve St...</td>\n",
       "      <td>Best Belly Dance Workout 2011 Swerve Studio s ...</td>\n",
       "      <td>0.943693</td>\n",
       "      <td>1.829787</td>\n",
       "      <td>0.512690</td>\n",
       "      <td>0.279188</td>\n",
       "      <td>0.055838</td>\n",
       "      <td>0.020305</td>\n",
       "      <td>0.547304</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>kraftrecipes recipes buffalo chicken burgers 1...</td>\n",
       "      <td>Buffalo Chicken Burgers recipe buffalo chicken...</td>\n",
       "      <td>outerbanxchic posted 10 14 2011 Delicious and ...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>1.743316</td>\n",
       "      <td>0.559091</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.081818</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.573416</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>canarygirl p 80</td>\n",
       "      <td>canarygirl com Sirloin with Peppercorn Cream S...</td>\n",
       "      <td>Summer always signifies the beginning of steak...</td>\n",
       "      <td>0.715035</td>\n",
       "      <td>2.020243</td>\n",
       "      <td>0.491935</td>\n",
       "      <td>0.217742</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.028226</td>\n",
       "      <td>0.506269</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7395 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    url  \\\n",
       "0     washingtonpost blogs early lead post nebraska ...   \n",
       "1     holiday rentals co uk info holiday ideas trave...   \n",
       "2     huffingtonpost 2011 10 27 leftover rice recipe...   \n",
       "3                                          sun sentinel   \n",
       "4                       socoting protecting and serving   \n",
       "...                                                 ...   \n",
       "1474       lanascooking 2011 07 08 roasted potato salad   \n",
       "1475  marthastewart 255456 billys chocolate buttercream   \n",
       "1476  laweekly bestof 2011 award best belly dance wo...   \n",
       "1477  kraftrecipes recipes buffalo chicken burgers 1...   \n",
       "1478                                    canarygirl p 80   \n",
       "\n",
       "                                                  title  \\\n",
       "0     Nebraska California State Bakersfield in the b...   \n",
       "1     Travel Cookbook Discover holiday home owners f...   \n",
       "2     5 Ways With Leftover Rice 5 ways with leftover...   \n",
       "3     South Florida Broward Palm Beach and Miami Dad...   \n",
       "4                      SoCoTing Protecting and serving    \n",
       "...                                                 ...   \n",
       "1474                                           Unkonown   \n",
       "1475  Billy s Chocolate Buttercream Martha Stewart R...   \n",
       "1476  Los Angeles Best Belly Dance Workout Swerve St...   \n",
       "1477  Buffalo Chicken Burgers recipe buffalo chicken...   \n",
       "1478  canarygirl com Sirloin with Peppercorn Cream S...   \n",
       "\n",
       "                                                   body  \\\n",
       "0     Print Subscription Today s Paper Discussions G...   \n",
       "1     If you re looking for a truly authentic gastro...   \n",
       "2     Do you have leftover rice from last night s di...   \n",
       "3     Digital Memberships HOME DELIVERY Place an ad ...   \n",
       "4     We believe that the short story is one of the ...   \n",
       "...                                                 ...   \n",
       "1474                                           Unkonown   \n",
       "1475  Save to your Collections Sorry for the inconve...   \n",
       "1476  Best Belly Dance Workout 2011 Swerve Studio s ...   \n",
       "1477  outerbanxchic posted 10 14 2011 Delicious and ...   \n",
       "1478  Summer always signifies the beginning of steak...   \n",
       "\n",
       "      alchemy_category_score  avgLinkWordLength  AvglinkWithOneCommonWord  \\\n",
       "0                   0.589160           2.055838                  0.570962   \n",
       "1                   0.630952           2.097222                  0.697368   \n",
       "2                   0.283897           2.483680                  0.781333   \n",
       "3                   0.630952           2.919255                  0.629328   \n",
       "4                   0.283897           1.571429                  0.380952   \n",
       "...                      ...                ...                       ...   \n",
       "1474                0.681418           1.640000                  0.367521   \n",
       "1475                0.630952           2.236559                  0.676768   \n",
       "1476                0.943693           1.829787                  0.512690   \n",
       "1477                0.630952           1.743316                  0.559091   \n",
       "1478                0.715035           2.020243                  0.491935   \n",
       "\n",
       "      AvglinkWithTwoCommonWord  AvglinkWithThreeCommonWord  \\\n",
       "0                     0.285481                    0.094617   \n",
       "1                     0.236842                    0.078947   \n",
       "2                     0.341333                    0.138667   \n",
       "3                     0.323829                    0.142566   \n",
       "4                     0.285714                    0.000000   \n",
       "...                        ...                         ...   \n",
       "1474                  0.102564                    0.034188   \n",
       "1475                  0.232323                    0.090909   \n",
       "1476                  0.279188                    0.055838   \n",
       "1477                  0.227273                    0.081818   \n",
       "1478                  0.217742                    0.032258   \n",
       "\n",
       "      AvglinkWithFourCommonWord  redundancyMeasure  ...  \\\n",
       "0                      0.048940           0.468646  ...   \n",
       "1                      0.000000           0.494215  ...   \n",
       "2                      0.066667           0.390071  ...   \n",
       "3                      0.095723           0.505976  ...   \n",
       "4                      0.000000           0.625323  ...   \n",
       "...                         ...                ...  ...   \n",
       "1474                   0.017094           0.782845  ...   \n",
       "1475                   0.020202           0.413333  ...   \n",
       "1476                   0.020305           0.547304  ...   \n",
       "1477                   0.018182           0.573416  ...   \n",
       "1478                   0.028226           0.506269  ...   \n",
       "\n",
       "      alchemy_category_culture_politics  alchemy_category_gaming  \\\n",
       "0                                     0                        0   \n",
       "1                                     0                        0   \n",
       "2                                     0                        0   \n",
       "3                                     0                        0   \n",
       "4                                     0                        0   \n",
       "...                                 ...                      ...   \n",
       "1474                                  0                        0   \n",
       "1475                                  0                        0   \n",
       "1476                                  0                        0   \n",
       "1477                                  0                        0   \n",
       "1478                                  0                        0   \n",
       "\n",
       "      alchemy_category_health alchemy_category_law_crime  \\\n",
       "0                           0                          0   \n",
       "1                           0                          0   \n",
       "2                           0                          0   \n",
       "3                           0                          0   \n",
       "4                           0                          1   \n",
       "...                       ...                        ...   \n",
       "1474                        0                          0   \n",
       "1475                        0                          0   \n",
       "1476                        0                          0   \n",
       "1477                        0                          0   \n",
       "1478                        0                          0   \n",
       "\n",
       "      alchemy_category_recreation  alchemy_category_religion  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "...                           ...                        ...   \n",
       "1474                            0                          0   \n",
       "1475                            0                          0   \n",
       "1476                            0                          0   \n",
       "1477                            0                          0   \n",
       "1478                            0                          0   \n",
       "\n",
       "      alchemy_category_science_technology  alchemy_category_sports  \\\n",
       "0                                       1                        0   \n",
       "1                                       0                        0   \n",
       "2                                       0                        0   \n",
       "3                                       0                        0   \n",
       "4                                       0                        0   \n",
       "...                                   ...                      ...   \n",
       "1474                                    0                        0   \n",
       "1475                                    0                        0   \n",
       "1476                                    0                        0   \n",
       "1477                                    0                        0   \n",
       "1478                                    0                        0   \n",
       "\n",
       "      alchemy_category_unknown  alchemy_category_weather  \n",
       "0                            0                         0  \n",
       "1                            1                         0  \n",
       "2                            0                         0  \n",
       "3                            1                         0  \n",
       "4                            0                         0  \n",
       "...                        ...                       ...  \n",
       "1474                         0                         0  \n",
       "1475                         1                         0  \n",
       "1476                         0                         0  \n",
       "1477                         1                         0  \n",
       "1478                         0                         0  \n",
       "\n",
       "[7395 rows x 37 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22311081",
   "metadata": {},
   "source": [
    "## 1.7 Removing null values in title and body columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4d15a731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                                       0\n",
       "title                                    10\n",
       "body                                     51\n",
       "alchemy_category_score                    0\n",
       "avgLinkWordLength                         0\n",
       "AvglinkWithOneCommonWord                  0\n",
       "AvglinkWithTwoCommonWord                  0\n",
       "AvglinkWithThreeCommonWord                0\n",
       "AvglinkWithFourCommonWord                 0\n",
       "redundancyMeasure                         0\n",
       "frameTagRatio                             0\n",
       "tagRatio                                  0\n",
       "imageTagRatio                             0\n",
       "isNews                                    0\n",
       "lengthyDomain                             0\n",
       "hyperlinkToAllWordsRatio                  0\n",
       "isFrontPageNews                           0\n",
       "alphanumCharCount                         0\n",
       "linksCount                                0\n",
       "wordCount                                 0\n",
       "parametrizedLinkRatio                     0\n",
       "spellingErrorsRatio                       0\n",
       "label                                  1479\n",
       "id                                        0\n",
       "alchemy_category_arts_entertainment       0\n",
       "alchemy_category_business                 0\n",
       "alchemy_category_computer_internet        0\n",
       "alchemy_category_culture_politics         0\n",
       "alchemy_category_gaming                   0\n",
       "alchemy_category_health                   0\n",
       "alchemy_category_law_crime                0\n",
       "alchemy_category_recreation               0\n",
       "alchemy_category_religion                 0\n",
       "alchemy_category_science_technology       0\n",
       "alchemy_category_sports                   0\n",
       "alchemy_category_unknown                  0\n",
       "alchemy_category_weather                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c9518c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df[\"title\"].fillna(\"Unknown\", inplace=True)\n",
    "main_df[\"body\"].fillna(\"Unknown\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e40bdc05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                                       0\n",
       "title                                     0\n",
       "body                                      0\n",
       "alchemy_category_score                    0\n",
       "avgLinkWordLength                         0\n",
       "AvglinkWithOneCommonWord                  0\n",
       "AvglinkWithTwoCommonWord                  0\n",
       "AvglinkWithThreeCommonWord                0\n",
       "AvglinkWithFourCommonWord                 0\n",
       "redundancyMeasure                         0\n",
       "frameTagRatio                             0\n",
       "tagRatio                                  0\n",
       "imageTagRatio                             0\n",
       "isNews                                    0\n",
       "lengthyDomain                             0\n",
       "hyperlinkToAllWordsRatio                  0\n",
       "isFrontPageNews                           0\n",
       "alphanumCharCount                         0\n",
       "linksCount                                0\n",
       "wordCount                                 0\n",
       "parametrizedLinkRatio                     0\n",
       "spellingErrorsRatio                       0\n",
       "label                                  1479\n",
       "id                                        0\n",
       "alchemy_category_arts_entertainment       0\n",
       "alchemy_category_business                 0\n",
       "alchemy_category_computer_internet        0\n",
       "alchemy_category_culture_politics         0\n",
       "alchemy_category_gaming                   0\n",
       "alchemy_category_health                   0\n",
       "alchemy_category_law_crime                0\n",
       "alchemy_category_recreation               0\n",
       "alchemy_category_religion                 0\n",
       "alchemy_category_science_technology       0\n",
       "alchemy_category_sports                   0\n",
       "alchemy_category_unknown                  0\n",
       "alchemy_category_weather                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37921cce",
   "metadata": {},
   "source": [
    "## 1.8 NLP "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c967fcb",
   "metadata": {},
   "source": [
    "### 1.8.1. Removing Punctuations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ccfaedbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punct(text):\n",
    "    text_nopunct = \"\".join([c for c in text if c not in string.punctuation])\n",
    "    return text_nopunct\n",
    "\n",
    "body = main_df[\"body\"].to_numpy()\n",
    "body_chan = []\n",
    "for i in range(len(body)):\n",
    "    body[i] = body[i].strip()\n",
    "    body_chan.append( remove_punct(body[i]))\n",
    "    \n",
    "main_df[\"body\"] = body_chan\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "26b1baf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "body = main_df[\"title\"].to_numpy()\n",
    "body_chan = []\n",
    "for i in range(len(body)):\n",
    "    body[i] = body[i].strip()\n",
    "    body_chan.append( remove_punct(body[i]))\n",
    "    \n",
    "main_df[\"title\"] = body_chan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "16e5fee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "body = main_df[\"url\"].to_numpy()\n",
    "body_chan = []\n",
    "for i in range(len(body)):\n",
    "    body[i] = body[i].strip()\n",
    "    body_chan.append( remove_punct(body[i]))\n",
    "    \n",
    "main_df[\"url\"] = body_chan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "c2e8ac2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "      <th>alchemy_category_gaming</th>\n",
       "      <th>alchemy_category_health</th>\n",
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       "      <th>alchemy_category_religion</th>\n",
       "      <th>alchemy_category_science_technology</th>\n",
       "      <th>alchemy_category_sports</th>\n",
       "      <th>alchemy_category_unknown</th>\n",
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       "      <td>holiday rentals co uk info holiday ideas trave...</td>\n",
       "      <td>Travel Cookbook Discover holiday home owners f...</td>\n",
       "      <td>If you re looking for a truly authentic gastro...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.097222</td>\n",
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       "      <td>Do you have leftover rice from last night s di...</td>\n",
       "      <td>0.283897</td>\n",
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       "      <td>Digital Memberships HOME DELIVERY Place an ad ...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
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       "      <td>0.095723</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>socoting protecting and serving</td>\n",
       "      <td>SoCoTing Protecting and serving</td>\n",
       "      <td>We believe that the short story is one of the ...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
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      "text/plain": [
       "                                                 url  \\\n",
       "0  washingtonpost blogs early lead post nebraska ...   \n",
       "1  holiday rentals co uk info holiday ideas trave...   \n",
       "2  huffingtonpost 2011 10 27 leftover rice recipe...   \n",
       "3                                       sun sentinel   \n",
       "4                    socoting protecting and serving   \n",
       "\n",
       "                                               title  \\\n",
       "0  Nebraska California State Bakersfield in the b...   \n",
       "1  Travel Cookbook Discover holiday home owners f...   \n",
       "2  5 Ways With Leftover Rice 5 ways with leftover...   \n",
       "3  South Florida Broward Palm Beach and Miami Dad...   \n",
       "4                    SoCoTing Protecting and serving   \n",
       "\n",
       "                                                body  alchemy_category_score  \\\n",
       "0  Print Subscription Today s Paper Discussions G...                0.589160   \n",
       "1  If you re looking for a truly authentic gastro...                0.630952   \n",
       "2  Do you have leftover rice from last night s di...                0.283897   \n",
       "3  Digital Memberships HOME DELIVERY Place an ad ...                0.630952   \n",
       "4  We believe that the short story is one of the ...                0.283897   \n",
       "\n",
       "   avgLinkWordLength  AvglinkWithOneCommonWord  AvglinkWithTwoCommonWord  \\\n",
       "0           2.055838                  0.570962                  0.285481   \n",
       "1           2.097222                  0.697368                  0.236842   \n",
       "2           2.483680                  0.781333                  0.341333   \n",
       "3           2.919255                  0.629328                  0.323829   \n",
       "4           1.571429                  0.380952                  0.285714   \n",
       "\n",
       "   AvglinkWithThreeCommonWord  AvglinkWithFourCommonWord  redundancyMeasure  \\\n",
       "0                    0.094617                   0.048940           0.468646   \n",
       "1                    0.078947                   0.000000           0.494215   \n",
       "2                    0.138667                   0.066667           0.390071   \n",
       "3                    0.142566                   0.095723           0.505976   \n",
       "4                    0.000000                   0.000000           0.625323   \n",
       "\n",
       "   ...  alchemy_category_culture_politics  alchemy_category_gaming  \\\n",
       "0  ...                                  0                        0   \n",
       "1  ...                                  0                        0   \n",
       "2  ...                                  0                        0   \n",
       "3  ...                                  0                        0   \n",
       "4  ...                                  0                        0   \n",
       "\n",
       "   alchemy_category_health alchemy_category_law_crime  \\\n",
       "0                        0                          0   \n",
       "1                        0                          0   \n",
       "2                        0                          0   \n",
       "3                        0                          0   \n",
       "4                        0                          1   \n",
       "\n",
       "   alchemy_category_recreation  alchemy_category_religion  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "\n",
       "   alchemy_category_science_technology  alchemy_category_sports  \\\n",
       "0                                    1                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    0                        0   \n",
       "4                                    0                        0   \n",
       "\n",
       "   alchemy_category_unknown  alchemy_category_weather  \n",
       "0                         0                         0  \n",
       "1                         1                         0  \n",
       "2                         0                         0  \n",
       "3                         1                         0  \n",
       "4                         0                         0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "994d9cde",
   "metadata": {},
   "source": [
    "### 1.8.2. Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f226cff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(text):\n",
    "    tokens = re.split('\\W+',text)\n",
    "    return tokens\n",
    "\n",
    "token_change = []\n",
    "tok = main_df[\"body\"].to_numpy()\n",
    "for i in range(len(tok)):\n",
    "    token_change.append(tokenize(tok[i]))\n",
    "    \n",
    "main_df[\"body\"] = token_change  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "74762887",
   "metadata": {},
   "outputs": [],
   "source": [
    "token_change = []\n",
    "tok = main_df[\"title\"].to_numpy()\n",
    "for i in range(len(tok)):\n",
    "    token_change.append(tokenize(tok[i]))\n",
    "    \n",
    "main_df[\"title\"] = token_change  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "007b58f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "token_change = []\n",
    "tok = main_df[\"url\"].to_numpy()\n",
    "for i in range(len(tok)):\n",
    "    token_change.append(tokenize(tok[i]))\n",
    "    \n",
    "main_df[\"url\"] = token_change  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "112f62a5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.283897</td>\n",
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       "      <td>[Digital, Memberships, HOME, DELIVERY, Place, ...</td>\n",
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      "text/plain": [
       "                                                 url  \\\n",
       "0  [washingtonpost, blogs, early, lead, post, neb...   \n",
       "1  [holiday, rentals, co, uk, info, holiday, idea...   \n",
       "2  [huffingtonpost, 2011, 10, 27, leftover, rice,...   \n",
       "3                                    [sun, sentinel]   \n",
       "4               [socoting, protecting, and, serving]   \n",
       "\n",
       "                                               title  \\\n",
       "0  [Nebraska, California, State, Bakersfield, in,...   \n",
       "1  [Travel, Cookbook, Discover, holiday, home, ow...   \n",
       "2  [5, Ways, With, Leftover, Rice, 5, ways, with,...   \n",
       "3  [South, Florida, Broward, Palm, Beach, and, Mi...   \n",
       "4               [SoCoTing, Protecting, and, serving]   \n",
       "\n",
       "                                                body  alchemy_category_score  \\\n",
       "0  [Print, Subscription, Today, s, Paper, Discuss...                0.589160   \n",
       "1  [If, you, re, looking, for, a, truly, authenti...                0.630952   \n",
       "2  [Do, you, have, leftover, rice, from, last, ni...                0.283897   \n",
       "3  [Digital, Memberships, HOME, DELIVERY, Place, ...                0.630952   \n",
       "4  [We, believe, that, the, short, story, is, one...                0.283897   \n",
       "\n",
       "   avgLinkWordLength  AvglinkWithOneCommonWord  AvglinkWithTwoCommonWord  \\\n",
       "0           2.055838                  0.570962                  0.285481   \n",
       "1           2.097222                  0.697368                  0.236842   \n",
       "2           2.483680                  0.781333                  0.341333   \n",
       "3           2.919255                  0.629328                  0.323829   \n",
       "4           1.571429                  0.380952                  0.285714   \n",
       "\n",
       "   AvglinkWithThreeCommonWord  AvglinkWithFourCommonWord  redundancyMeasure  \\\n",
       "0                    0.094617                   0.048940           0.468646   \n",
       "1                    0.078947                   0.000000           0.494215   \n",
       "2                    0.138667                   0.066667           0.390071   \n",
       "3                    0.142566                   0.095723           0.505976   \n",
       "4                    0.000000                   0.000000           0.625323   \n",
       "\n",
       "   ...  alchemy_category_culture_politics  alchemy_category_gaming  \\\n",
       "0  ...                                  0                        0   \n",
       "1  ...                                  0                        0   \n",
       "2  ...                                  0                        0   \n",
       "3  ...                                  0                        0   \n",
       "4  ...                                  0                        0   \n",
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       "   alchemy_category_health alchemy_category_law_crime  \\\n",
       "0                        0                          0   \n",
       "1                        0                          0   \n",
       "2                        0                          0   \n",
       "3                        0                          0   \n",
       "4                        0                          1   \n",
       "\n",
       "   alchemy_category_recreation  alchemy_category_religion  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "\n",
       "   alchemy_category_science_technology  alchemy_category_sports  \\\n",
       "0                                    1                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    0                        0   \n",
       "4                                    0                        0   \n",
       "\n",
       "   alchemy_category_unknown  alchemy_category_weather  \n",
       "0                         0                         0  \n",
       "1                         1                         0  \n",
       "2                         0                         0  \n",
       "3                         1                         0  \n",
       "4                         0                         0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15bf69a",
   "metadata": {},
   "source": [
    "### 1.8.3. Remove Stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f64f808b",
   "metadata": {},
   "outputs": [],
   "source": [
    "stopwords = nltk.corpus.stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e8ddc2d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(tokenized_list):\n",
    "    text = [word for word in tokenized_list if word not in stopwords]\n",
    "    return text\n",
    "\n",
    "stop_list = main_df[\"body\"].to_numpy()\n",
    "stop_list_change = []\n",
    "for i in range(len(stop_list)):\n",
    "    stop_list_change.append(remove_stopwords(stop_list[i]))\n",
    "    \n",
    "main_df[\"body\"] = stop_list_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7db7b96e",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_list = main_df[\"title\"].to_numpy()\n",
    "stop_list_change = []\n",
    "for i in range(len(stop_list)):\n",
    "    stop_list_change.append(remove_stopwords(stop_list[i]))\n",
    "    \n",
    "main_df[\"title\"] = stop_list_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "dcdd9ac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_list = main_df[\"url\"].to_numpy()\n",
    "stop_list_change = []\n",
    "for i in range(len(stop_list)):\n",
    "    stop_list_change.append(remove_stopwords(stop_list[i]))\n",
    "    \n",
    "main_df[\"url\"] = stop_list_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "260ea178",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>url</th>\n",
       "      <th>title</th>\n",
       "      <th>body</th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
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       "      <th>AvglinkWithThreeCommonWord</th>\n",
       "      <th>AvglinkWithFourCommonWord</th>\n",
       "      <th>redundancyMeasure</th>\n",
       "      <th>...</th>\n",
       "      <th>alchemy_category_culture_politics</th>\n",
       "      <th>alchemy_category_gaming</th>\n",
       "      <th>alchemy_category_health</th>\n",
       "      <th>alchemy_category_law_crime</th>\n",
       "      <th>alchemy_category_recreation</th>\n",
       "      <th>alchemy_category_religion</th>\n",
       "      <th>alchemy_category_science_technology</th>\n",
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       "      <th>0</th>\n",
       "      <td>[washingtonpost, blogs, early, lead, post, neb...</td>\n",
       "      <td>[Nebraska, California, State, Bakersfield, bes...</td>\n",
       "      <td>[Print, Subscription, Today, Paper, Discussion...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[holiday, rentals, co, uk, info, holiday, idea...</td>\n",
       "      <td>[Travel, Cookbook, Discover, holiday, home, ow...</td>\n",
       "      <td>[If, looking, truly, authentic, gastro, break,...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.097222</td>\n",
       "      <td>0.697368</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494215</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[huffingtonpost, 2011, 10, 27, leftover, rice,...</td>\n",
       "      <td>[5, Ways, With, Leftover, Rice, 5, ways, lefto...</td>\n",
       "      <td>[Do, leftover, rice, last, night, dinner, Chin...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[sun, sentinel]</td>\n",
       "      <td>[South, Florida, Broward, Palm, Beach, Miami, ...</td>\n",
       "      <td>[Digital, Memberships, HOME, DELIVERY, Place, ...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[socoting, protecting, serving]</td>\n",
       "      <td>[SoCoTing, Protecting, serving]</td>\n",
       "      <td>[We, believe, short, story, one, exciting, imp...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  [washingtonpost, blogs, early, lead, post, neb...   \n",
       "1  [holiday, rentals, co, uk, info, holiday, idea...   \n",
       "2  [huffingtonpost, 2011, 10, 27, leftover, rice,...   \n",
       "3                                    [sun, sentinel]   \n",
       "4                    [socoting, protecting, serving]   \n",
       "\n",
       "                                               title  \\\n",
       "0  [Nebraska, California, State, Bakersfield, bes...   \n",
       "1  [Travel, Cookbook, Discover, holiday, home, ow...   \n",
       "2  [5, Ways, With, Leftover, Rice, 5, ways, lefto...   \n",
       "3  [South, Florida, Broward, Palm, Beach, Miami, ...   \n",
       "4                    [SoCoTing, Protecting, serving]   \n",
       "\n",
       "                                                body  alchemy_category_score  \\\n",
       "0  [Print, Subscription, Today, Paper, Discussion...                0.589160   \n",
       "1  [If, looking, truly, authentic, gastro, break,...                0.630952   \n",
       "2  [Do, leftover, rice, last, night, dinner, Chin...                0.283897   \n",
       "3  [Digital, Memberships, HOME, DELIVERY, Place, ...                0.630952   \n",
       "4  [We, believe, short, story, one, exciting, imp...                0.283897   \n",
       "\n",
       "   avgLinkWordLength  AvglinkWithOneCommonWord  AvglinkWithTwoCommonWord  \\\n",
       "0           2.055838                  0.570962                  0.285481   \n",
       "1           2.097222                  0.697368                  0.236842   \n",
       "2           2.483680                  0.781333                  0.341333   \n",
       "3           2.919255                  0.629328                  0.323829   \n",
       "4           1.571429                  0.380952                  0.285714   \n",
       "\n",
       "   AvglinkWithThreeCommonWord  AvglinkWithFourCommonWord  redundancyMeasure  \\\n",
       "0                    0.094617                   0.048940           0.468646   \n",
       "1                    0.078947                   0.000000           0.494215   \n",
       "2                    0.138667                   0.066667           0.390071   \n",
       "3                    0.142566                   0.095723           0.505976   \n",
       "4                    0.000000                   0.000000           0.625323   \n",
       "\n",
       "   ...  alchemy_category_culture_politics  alchemy_category_gaming  \\\n",
       "0  ...                                  0                        0   \n",
       "1  ...                                  0                        0   \n",
       "2  ...                                  0                        0   \n",
       "3  ...                                  0                        0   \n",
       "4  ...                                  0                        0   \n",
       "\n",
       "   alchemy_category_health alchemy_category_law_crime  \\\n",
       "0                        0                          0   \n",
       "1                        0                          0   \n",
       "2                        0                          0   \n",
       "3                        0                          0   \n",
       "4                        0                          1   \n",
       "\n",
       "   alchemy_category_recreation  alchemy_category_religion  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "\n",
       "   alchemy_category_science_technology  alchemy_category_sports  \\\n",
       "0                                    1                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    0                        0   \n",
       "4                                    0                        0   \n",
       "\n",
       "   alchemy_category_unknown  alchemy_category_weather  \n",
       "0                         0                         0  \n",
       "1                         1                         0  \n",
       "2                         0                         0  \n",
       "3                         1                         0  \n",
       "4                         0                         0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52f75d5",
   "metadata": {},
   "source": [
    "### 1.8.4. Lemmatizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0a097752",
   "metadata": {},
   "outputs": [],
   "source": [
    "wnl = nltk.WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "0211f988",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatizing(tokenized_text):\n",
    "    text = [wnl.lemmatize(word) for word in tokenized_text]\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d68bdc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatize_list = main_df[\"url\"].to_numpy()\n",
    "lemmatized_list = []\n",
    "for i in range(len(lemmatize_list)):\n",
    "    lemmatized_list.append(lemmatizing(lemmatize_list[i]))\n",
    "    \n",
    "main_df[\"url\"] = lemmatized_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "a7b82ec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatize_list = main_df[\"title\"].to_numpy()\n",
    "lemmatized_list = []\n",
    "for i in range(len(lemmatize_list)):\n",
    "    lemmatized_list.append(lemmatizing(lemmatize_list[i]))\n",
    "    \n",
    "main_df[\"title\"] = lemmatized_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "32e5b2f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatize_list = main_df[\"body\"].to_numpy()\n",
    "lemmatized_list = []\n",
    "for i in range(len(lemmatize_list)):\n",
    "    lemmatized_list.append(lemmatizing(lemmatize_list[i]))\n",
    "    \n",
    "main_df[\"body\"] = lemmatized_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e1d60c7b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>[washingtonpost, blog, early, lead, post, nebr...</td>\n",
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       "      <td>[holiday, rental, co, uk, info, holiday, idea,...</td>\n",
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       "      <td>[huffingtonpost, 2011, 10, 27, leftover, rice,...</td>\n",
       "      <td>[5, Ways, With, Leftover, Rice, 5, way, leftov...</td>\n",
       "      <td>[Do, leftover, rice, last, night, dinner, Chin...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[sun, sentinel]</td>\n",
       "      <td>[South, Florida, Broward, Palm, Beach, Miami, ...</td>\n",
       "      <td>[Digital, Memberships, HOME, DELIVERY, Place, ...</td>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[socoting, protecting, serving]</td>\n",
       "      <td>[SoCoTing, Protecting, serving]</td>\n",
       "      <td>[We, believe, short, story, one, exciting, imp...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625323</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 url  \\\n",
       "0  [washingtonpost, blog, early, lead, post, nebr...   \n",
       "1  [holiday, rental, co, uk, info, holiday, idea,...   \n",
       "2  [huffingtonpost, 2011, 10, 27, leftover, rice,...   \n",
       "3                                    [sun, sentinel]   \n",
       "4                    [socoting, protecting, serving]   \n",
       "\n",
       "                                               title  \\\n",
       "0  [Nebraska, California, State, Bakersfield, bes...   \n",
       "1  [Travel, Cookbook, Discover, holiday, home, ow...   \n",
       "2  [5, Ways, With, Leftover, Rice, 5, way, leftov...   \n",
       "3  [South, Florida, Broward, Palm, Beach, Miami, ...   \n",
       "4                    [SoCoTing, Protecting, serving]   \n",
       "\n",
       "                                                body  alchemy_category_score  \\\n",
       "0  [Print, Subscription, Today, Paper, Discussion...                0.589160   \n",
       "1  [If, looking, truly, authentic, gastro, break,...                0.630952   \n",
       "2  [Do, leftover, rice, last, night, dinner, Chin...                0.283897   \n",
       "3  [Digital, Memberships, HOME, DELIVERY, Place, ...                0.630952   \n",
       "4  [We, believe, short, story, one, exciting, imp...                0.283897   \n",
       "\n",
       "   avgLinkWordLength  AvglinkWithOneCommonWord  AvglinkWithTwoCommonWord  \\\n",
       "0           2.055838                  0.570962                  0.285481   \n",
       "1           2.097222                  0.697368                  0.236842   \n",
       "2           2.483680                  0.781333                  0.341333   \n",
       "3           2.919255                  0.629328                  0.323829   \n",
       "4           1.571429                  0.380952                  0.285714   \n",
       "\n",
       "   AvglinkWithThreeCommonWord  AvglinkWithFourCommonWord  redundancyMeasure  \\\n",
       "0                    0.094617                   0.048940           0.468646   \n",
       "1                    0.078947                   0.000000           0.494215   \n",
       "2                    0.138667                   0.066667           0.390071   \n",
       "3                    0.142566                   0.095723           0.505976   \n",
       "4                    0.000000                   0.000000           0.625323   \n",
       "\n",
       "   ...  alchemy_category_culture_politics  alchemy_category_gaming  \\\n",
       "0  ...                                  0                        0   \n",
       "1  ...                                  0                        0   \n",
       "2  ...                                  0                        0   \n",
       "3  ...                                  0                        0   \n",
       "4  ...                                  0                        0   \n",
       "\n",
       "   alchemy_category_health alchemy_category_law_crime  \\\n",
       "0                        0                          0   \n",
       "1                        0                          0   \n",
       "2                        0                          0   \n",
       "3                        0                          0   \n",
       "4                        0                          1   \n",
       "\n",
       "   alchemy_category_recreation  alchemy_category_religion  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
       "2                            0                          0   \n",
       "3                            0                          0   \n",
       "4                            0                          0   \n",
       "\n",
       "   alchemy_category_science_technology  alchemy_category_sports  \\\n",
       "0                                    1                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    0                        0   \n",
       "4                                    0                        0   \n",
       "\n",
       "   alchemy_category_unknown  alchemy_category_weather  \n",
       "0                         0                         0  \n",
       "1                         1                         0  \n",
       "2                         0                         0  \n",
       "3                         1                         0  \n",
       "4                         0                         0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d76bff41",
   "metadata": {},
   "source": [
    "### 1.8.5. Vectorizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "82546026",
   "metadata": {},
   "outputs": [],
   "source": [
    "body_data = main_df[\"body\"].to_numpy()\n",
    "body_data_combined = [' '.join(i) for i in body_data]\n",
    "main_df[\"body\"] = body_data_combined\n",
    "\n",
    "title_data = main_df[\"title\"].to_numpy()\n",
    "title_data_combined = [' '.join(i) for i in title_data]\n",
    "main_df[\"title\"] = title_data_combined\n",
    "\n",
    "url_data = main_df[\"url\"].to_numpy()\n",
    "url_data_combined = [' '.join(i) for i in url_data]\n",
    "main_df[\"url\"] = url_data_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "662b151a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Do leftover rice last night dinner Chinese tak...</td>\n",
       "      <td>0.283897</td>\n",
       "      <td>2.483680</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
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       "      <td>Digital Memberships HOME DELIVERY Place ad Job...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>socoting protecting serving</td>\n",
       "      <td>SoCoTing Protecting serving</td>\n",
       "      <td>We believe short story one exciting important ...</td>\n",
       "      <td>0.283897</td>\n",
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       "<p>5 rows × 37 columns</p>\n",
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       "                                                 url  \\\n",
       "0  washingtonpost blog early lead post nebraska c...   \n",
       "1  holiday rental co uk info holiday idea travel ...   \n",
       "2  huffingtonpost 2011 10 27 leftover rice recipe...   \n",
       "3                                       sun sentinel   \n",
       "4                        socoting protecting serving   \n",
       "\n",
       "                                               title  \\\n",
       "0  Nebraska California State Bakersfield best rai...   \n",
       "1  Travel Cookbook Discover holiday home owner fa...   \n",
       "2      5 Ways With Leftover Rice 5 way leftover rice   \n",
       "3  South Florida Broward Palm Beach Miami Dade br...   \n",
       "4                        SoCoTing Protecting serving   \n",
       "\n",
       "                                                body  alchemy_category_score  \\\n",
       "0  Print Subscription Today Paper Discussions Goi...                0.589160   \n",
       "1  If looking truly authentic gastro break better...                0.630952   \n",
       "2  Do leftover rice last night dinner Chinese tak...                0.283897   \n",
       "3  Digital Memberships HOME DELIVERY Place ad Job...                0.630952   \n",
       "4  We believe short story one exciting important ...                0.283897   \n",
       "\n",
       "   avgLinkWordLength  AvglinkWithOneCommonWord  AvglinkWithTwoCommonWord  \\\n",
       "0           2.055838                  0.570962                  0.285481   \n",
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       "2           2.483680                  0.781333                  0.341333   \n",
       "3           2.919255                  0.629328                  0.323829   \n",
       "4           1.571429                  0.380952                  0.285714   \n",
       "\n",
       "   AvglinkWithThreeCommonWord  AvglinkWithFourCommonWord  redundancyMeasure  \\\n",
       "0                    0.094617                   0.048940           0.468646   \n",
       "1                    0.078947                   0.000000           0.494215   \n",
       "2                    0.138667                   0.066667           0.390071   \n",
       "3                    0.142566                   0.095723           0.505976   \n",
       "4                    0.000000                   0.000000           0.625323   \n",
       "\n",
       "   ...  alchemy_category_culture_politics  alchemy_category_gaming  \\\n",
       "0  ...                                  0                        0   \n",
       "1  ...                                  0                        0   \n",
       "2  ...                                  0                        0   \n",
       "3  ...                                  0                        0   \n",
       "4  ...                                  0                        0   \n",
       "\n",
       "   alchemy_category_health alchemy_category_law_crime  \\\n",
       "0                        0                          0   \n",
       "1                        0                          0   \n",
       "2                        0                          0   \n",
       "3                        0                          0   \n",
       "4                        0                          1   \n",
       "\n",
       "   alchemy_category_recreation  alchemy_category_religion  \\\n",
       "0                            0                          0   \n",
       "1                            0                          0   \n",
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       "3                            0                          0   \n",
       "4                            0                          0   \n",
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       "   alchemy_category_science_technology  alchemy_category_sports  \\\n",
       "0                                    1                        0   \n",
       "1                                    0                        0   \n",
       "2                                    0                        0   \n",
       "3                                    0                        0   \n",
       "4                                    0                        0   \n",
       "\n",
       "   alchemy_category_unknown  alchemy_category_weather  \n",
       "0                         0                         0  \n",
       "1                         1                         0  \n",
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       "3                         1                         0  \n",
       "4                         0                         0  \n",
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       "[5 rows x 37 columns]"
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     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d501ff55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>count</th>\n",
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       "      <td>7395.000000</td>\n",
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       "      <td>0.618294</td>\n",
       "      <td>2.178713</td>\n",
       "      <td>0.466765</td>\n",
       "      <td>0.213517</td>\n",
       "      <td>0.091983</td>\n",
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       "      <td>0.525715</td>\n",
       "      <td>0.054848</td>\n",
       "      <td>0.232736</td>\n",
       "      <td>0.136379</td>\n",
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       "      <td>0.162001</td>\n",
       "      <td>0.009736</td>\n",
       "      <td>0.039621</td>\n",
       "      <td>0.052333</td>\n",
       "      <td>0.314266</td>\n",
       "      <td>0.000676</td>\n",
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       "      <td>0.869851</td>\n",
       "      <td>0.202891</td>\n",
       "      <td>0.147180</td>\n",
       "      <td>0.096638</td>\n",
       "      <td>0.072303</td>\n",
       "      <td>0.130310</td>\n",
       "      <td>0.034762</td>\n",
       "      <td>0.046934</td>\n",
       "      <td>0.219970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212068</td>\n",
       "      <td>0.102811</td>\n",
       "      <td>0.257283</td>\n",
       "      <td>0.066657</td>\n",
       "      <td>0.368477</td>\n",
       "      <td>0.098198</td>\n",
       "      <td>0.195081</td>\n",
       "      <td>0.222712</td>\n",
       "      <td>0.464254</td>\n",
       "      <td>0.025996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.283897</td>\n",
       "      <td>0.080984</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.239256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111886</td>\n",
       "      <td>-0.286541</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.540952</td>\n",
       "      <td>1.607571</td>\n",
       "      <td>0.337018</td>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.443102</td>\n",
       "      <td>0.028502</td>\n",
       "      <td>0.201450</td>\n",
       "      <td>0.025394</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.087838</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.201493</td>\n",
       "      <td>0.067416</td>\n",
       "      <td>0.021505</td>\n",
       "      <td>0.484536</td>\n",
       "      <td>0.045918</td>\n",
       "      <td>0.230824</td>\n",
       "      <td>0.081633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.712322</td>\n",
       "      <td>2.625296</td>\n",
       "      <td>0.615731</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.064321</td>\n",
       "      <td>0.578999</td>\n",
       "      <td>0.074037</td>\n",
       "      <td>0.261160</td>\n",
       "      <td>0.233350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.969377</td>\n",
       "      <td>4.151883</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.980392</td>\n",
       "      <td>0.782845</td>\n",
       "      <td>0.142339</td>\n",
       "      <td>0.350725</td>\n",
       "      <td>0.545285</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 × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       alchemy_category_score  avgLinkWordLength  AvglinkWithOneCommonWord  \\\n",
       "count             7395.000000        7395.000000               7395.000000   \n",
       "mean                 0.618294           2.178713                  0.466765   \n",
       "std                  0.164705           0.869851                  0.202891   \n",
       "min                  0.283897           0.080984                  0.000000   \n",
       "25%                  0.540952           1.607571                  0.337018   \n",
       "50%                  0.630952           2.087838                  0.479167   \n",
       "75%                  0.712322           2.625296                  0.615731   \n",
       "max                  0.969377           4.151883                  1.000000   \n",
       "\n",
       "       AvglinkWithTwoCommonWord  AvglinkWithThreeCommonWord  \\\n",
       "count               7395.000000                 7395.000000   \n",
       "mean                   0.213517                    0.091983   \n",
       "std                    0.147180                    0.096638   \n",
       "min                    0.000000                    0.000000   \n",
       "25%                    0.102041                    0.021739   \n",
       "50%                    0.201493                    0.067416   \n",
       "75%                    0.300000                    0.133333   \n",
       "max                    1.000000                    0.980392   \n",
       "\n",
       "       AvglinkWithFourCommonWord  redundancyMeasure  frameTagRatio  \\\n",
       "count                7395.000000        7395.000000    7395.000000   \n",
       "mean                    0.048793           0.525715       0.054848   \n",
       "std                     0.072303           0.130310       0.034762   \n",
       "min                     0.000000           0.239256       0.000000   \n",
       "25%                     0.000000           0.443102       0.028502   \n",
       "50%                     0.021505           0.484536       0.045918   \n",
       "75%                     0.064321           0.578999       0.074037   \n",
       "max                     0.980392           0.782845       0.142339   \n",
       "\n",
       "          tagRatio  imageTagRatio  ...  alchemy_category_culture_politics  \\\n",
       "count  7395.000000    7395.000000  ...                        7395.000000   \n",
       "mean      0.232736       0.136379  ...                           0.047194   \n",
       "std       0.046934       0.219970  ...                           0.212068   \n",
       "min       0.111886      -0.286541  ...                           0.000000   \n",
       "25%       0.201450       0.025394  ...                           0.000000   \n",
       "50%       0.230824       0.081633  ...                           0.000000   \n",
       "75%       0.261160       0.233350  ...                           0.000000   \n",
       "max       0.350725       0.545285  ...                           1.000000   \n",
       "\n",
       "       alchemy_category_gaming  alchemy_category_health  \\\n",
       "count              7395.000000              7395.000000   \n",
       "mean                  0.010683                 0.071264   \n",
       "std                   0.102811                 0.257283   \n",
       "min                   0.000000                 0.000000   \n",
       "25%                   0.000000                 0.000000   \n",
       "50%                   0.000000                 0.000000   \n",
       "75%                   0.000000                 0.000000   \n",
       "max                   1.000000                 1.000000   \n",
       "\n",
       "       alchemy_category_law_crime  alchemy_category_recreation  \\\n",
       "count                 7395.000000                  7395.000000   \n",
       "mean                     0.004462                     0.162001   \n",
       "std                      0.066657                     0.368477   \n",
       "min                      0.000000                     0.000000   \n",
       "25%                      0.000000                     0.000000   \n",
       "50%                      0.000000                     0.000000   \n",
       "75%                      0.000000                     0.000000   \n",
       "max                      1.000000                     1.000000   \n",
       "\n",
       "       alchemy_category_religion  alchemy_category_science_technology  \\\n",
       "count                7395.000000                          7395.000000   \n",
       "mean                    0.009736                             0.039621   \n",
       "std                     0.098198                             0.195081   \n",
       "min                     0.000000                             0.000000   \n",
       "25%                     0.000000                             0.000000   \n",
       "50%                     0.000000                             0.000000   \n",
       "75%                     0.000000                             0.000000   \n",
       "max                     1.000000                             1.000000   \n",
       "\n",
       "       alchemy_category_sports  alchemy_category_unknown  \\\n",
       "count              7395.000000               7395.000000   \n",
       "mean                  0.052333                  0.314266   \n",
       "std                   0.222712                  0.464254   \n",
       "min                   0.000000                  0.000000   \n",
       "25%                   0.000000                  0.000000   \n",
       "50%                   0.000000                  0.000000   \n",
       "75%                   0.000000                  1.000000   \n",
       "max                   1.000000                  1.000000   \n",
       "\n",
       "       alchemy_category_weather  \n",
       "count               7395.000000  \n",
       "mean                   0.000676  \n",
       "std                    0.025996  \n",
       "min                    0.000000  \n",
       "25%                    0.000000  \n",
       "50%                    0.000000  \n",
       "75%                    0.000000  \n",
       "max                    1.000000  \n",
       "\n",
       "[8 rows x 33 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "50e10cf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1773\n",
       "1       1558\n",
       "2       7148\n",
       "3        565\n",
       "4       2606\n",
       "        ... \n",
       "1474    3336\n",
       "1475    2867\n",
       "1476     738\n",
       "1477    4124\n",
       "1478    6056\n",
       "Name: id, Length: 7395, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"id\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4f09ae75",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df['label_out'] = main_df['label']\n",
    "main_df['id_out'] = main_df['id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0080680c",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df[\"total_description\"] = main_df[\"title\"] + main_df[\"body\"] + main_df[\"url\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f29b4c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_be_deleted = [\"title\", \"body\",\"url\", \"label\",\"id\"] \n",
    "main_df.drop(axis=\"columns\", labels=columns_to_be_deleted, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019828d6",
   "metadata": {},
   "source": [
    "### Using Tfidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "389b41ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7395, 3000)\n"
     ]
    }
   ],
   "source": [
    "tfidf_vect = TfidfVectorizer(stop_words = {'english'}, max_features = 3000)\n",
    "X_tfidf = tfidf_vect.fit_transform(main_df['total_description'])\n",
    "print(X_tfidf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "de4bdc33",
   "metadata": {},
   "outputs": [],
   "source": [
    "names_list = tfidf_vect.get_feature_names()\n",
    "len(names_list)\n",
    "tfidf = X_tfidf.todense() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "467f5548",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(tfidf, columns = names_list) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "15fb792c",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df_list = main_df.to_numpy() \n",
    "column_names = main_df.columns\n",
    "main_df = pd.DataFrame(main_df_list, columns = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "e27188cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
       "      <th>AvglinkWithOneCommonWord</th>\n",
       "      <th>AvglinkWithTwoCommonWord</th>\n",
       "      <th>AvglinkWithThreeCommonWord</th>\n",
       "      <th>AvglinkWithFourCommonWord</th>\n",
       "      <th>redundancyMeasure</th>\n",
       "      <th>frameTagRatio</th>\n",
       "      <th>tagRatio</th>\n",
       "      <th>imageTagRatio</th>\n",
       "      <th>...</th>\n",
       "      <th>alchemy_category_law_crime</th>\n",
       "      <th>alchemy_category_recreation</th>\n",
       "      <th>alchemy_category_religion</th>\n",
       "      <th>alchemy_category_science_technology</th>\n",
       "      <th>alchemy_category_sports</th>\n",
       "      <th>alchemy_category_unknown</th>\n",
       "      <th>alchemy_category_weather</th>\n",
       "      <th>label_out</th>\n",
       "      <th>id_out</th>\n",
       "      <th>total_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.58916</td>\n",
       "      <td>2.055838</td>\n",
       "      <td>0.570962</td>\n",
       "      <td>0.285481</td>\n",
       "      <td>0.094617</td>\n",
       "      <td>0.04894</td>\n",
       "      <td>0.468646</td>\n",
       "      <td>0.028002</td>\n",
       "      <td>0.170434</td>\n",
       "      <td>0.084942</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1773</td>\n",
       "      <td>Nebraska California State Bakersfield best rai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.097222</td>\n",
       "      <td>0.697368</td>\n",
       "      <td>0.236842</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.494215</td>\n",
       "      <td>0.072948</td>\n",
       "      <td>0.265497</td>\n",
       "      <td>0.014563</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1558</td>\n",
       "      <td>Travel Cookbook Discover holiday home owner fa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.283897</td>\n",
       "      <td>2.48368</td>\n",
       "      <td>0.781333</td>\n",
       "      <td>0.341333</td>\n",
       "      <td>0.138667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>0.054522</td>\n",
       "      <td>0.219335</td>\n",
       "      <td>0.253165</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7148</td>\n",
       "      <td>5 Ways With Leftover Rice 5 way leftover riceD...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.919255</td>\n",
       "      <td>0.629328</td>\n",
       "      <td>0.323829</td>\n",
       "      <td>0.142566</td>\n",
       "      <td>0.095723</td>\n",
       "      <td>0.505976</td>\n",
       "      <td>0.037284</td>\n",
       "      <td>0.120069</td>\n",
       "      <td>0.185976</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>565</td>\n",
       "      <td>South Florida Broward Palm Beach Miami Dade br...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.283897</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625323</td>\n",
       "      <td>0.142339</td>\n",
       "      <td>0.234109</td>\n",
       "      <td>0.026667</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2606</td>\n",
       "      <td>SoCoTing Protecting servingWe believe short st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7390</th>\n",
       "      <td>0.681418</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.367521</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.034188</td>\n",
       "      <td>0.017094</td>\n",
       "      <td>0.782845</td>\n",
       "      <td>0.071719</td>\n",
       "      <td>0.237913</td>\n",
       "      <td>-0.286541</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3336</td>\n",
       "      <td>UnkonownUnkonownlanascooking 2011 07 08 roaste...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7391</th>\n",
       "      <td>0.630952</td>\n",
       "      <td>2.236559</td>\n",
       "      <td>0.676768</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.413333</td>\n",
       "      <td>0.056471</td>\n",
       "      <td>0.237229</td>\n",
       "      <td>0.024793</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2867</td>\n",
       "      <td>Billy Chocolate Buttercream Martha Stewart Rec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7392</th>\n",
       "      <td>0.943693</td>\n",
       "      <td>1.829787</td>\n",
       "      <td>0.51269</td>\n",
       "      <td>0.279188</td>\n",
       "      <td>0.055838</td>\n",
       "      <td>0.020305</td>\n",
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       "      <td>0.04129</td>\n",
       "      <td>0.21018</td>\n",
       "      <td>0.433526</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>738</td>\n",
       "      <td>Los Angeles Best Belly Dance Workout Swerve St...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7393</th>\n",
       "      <td>0.630952</td>\n",
       "      <td>1.743316</td>\n",
       "      <td>0.559091</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.081818</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.573416</td>\n",
       "      <td>0.070261</td>\n",
       "      <td>0.190801</td>\n",
       "      <td>0.545285</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4124</td>\n",
       "      <td>Buffalo Chicken Burgers recipe buffalo chicken...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7394</th>\n",
       "      <td>0.715035</td>\n",
       "      <td>2.020243</td>\n",
       "      <td>0.491935</td>\n",
       "      <td>0.217742</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.028226</td>\n",
       "      <td>0.506269</td>\n",
       "      <td>0.012228</td>\n",
       "      <td>0.234657</td>\n",
       "      <td>0.078231</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6056</td>\n",
       "      <td>canarygirl com Sirloin Peppercorn Cream SauceS...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7395 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alchemy_category_score avgLinkWordLength AvglinkWithOneCommonWord  \\\n",
       "0                   0.58916          2.055838                 0.570962   \n",
       "1                  0.630952          2.097222                 0.697368   \n",
       "2                  0.283897           2.48368                 0.781333   \n",
       "3                  0.630952          2.919255                 0.629328   \n",
       "4                  0.283897          1.571429                 0.380952   \n",
       "...                     ...               ...                      ...   \n",
       "7390               0.681418              1.64                 0.367521   \n",
       "7391               0.630952          2.236559                 0.676768   \n",
       "7392               0.943693          1.829787                  0.51269   \n",
       "7393               0.630952          1.743316                 0.559091   \n",
       "7394               0.715035          2.020243                 0.491935   \n",
       "\n",
       "     AvglinkWithTwoCommonWord AvglinkWithThreeCommonWord  \\\n",
       "0                    0.285481                   0.094617   \n",
       "1                    0.236842                   0.078947   \n",
       "2                    0.341333                   0.138667   \n",
       "3                    0.323829                   0.142566   \n",
       "4                    0.285714                        0.0   \n",
       "...                       ...                        ...   \n",
       "7390                 0.102564                   0.034188   \n",
       "7391                 0.232323                   0.090909   \n",
       "7392                 0.279188                   0.055838   \n",
       "7393                 0.227273                   0.081818   \n",
       "7394                 0.217742                   0.032258   \n",
       "\n",
       "     AvglinkWithFourCommonWord redundancyMeasure frameTagRatio  tagRatio  \\\n",
       "0                      0.04894          0.468646      0.028002  0.170434   \n",
       "1                          0.0          0.494215      0.072948  0.265497   \n",
       "2                     0.066667          0.390071      0.054522  0.219335   \n",
       "3                     0.095723          0.505976      0.037284  0.120069   \n",
       "4                          0.0          0.625323      0.142339  0.234109   \n",
       "...                        ...               ...           ...       ...   \n",
       "7390                  0.017094          0.782845      0.071719  0.237913   \n",
       "7391                  0.020202          0.413333      0.056471  0.237229   \n",
       "7392                  0.020305          0.547304       0.04129   0.21018   \n",
       "7393                  0.018182          0.573416      0.070261  0.190801   \n",
       "7394                  0.028226          0.506269      0.012228  0.234657   \n",
       "\n",
       "     imageTagRatio  ... alchemy_category_law_crime  \\\n",
       "0         0.084942  ...                          0   \n",
       "1         0.014563  ...                          0   \n",
       "2         0.253165  ...                          0   \n",
       "3         0.185976  ...                          0   \n",
       "4         0.026667  ...                          1   \n",
       "...            ...  ...                        ...   \n",
       "7390     -0.286541  ...                          0   \n",
       "7391      0.024793  ...                          0   \n",
       "7392      0.433526  ...                          0   \n",
       "7393      0.545285  ...                          0   \n",
       "7394      0.078231  ...                          0   \n",
       "\n",
       "     alchemy_category_recreation alchemy_category_religion  \\\n",
       "0                              0                         0   \n",
       "1                              0                         0   \n",
       "2                              0                         0   \n",
       "3                              0                         0   \n",
       "4                              0                         0   \n",
       "...                          ...                       ...   \n",
       "7390                           0                         0   \n",
       "7391                           0                         0   \n",
       "7392                           0                         0   \n",
       "7393                           0                         0   \n",
       "7394                           0                         0   \n",
       "\n",
       "     alchemy_category_science_technology alchemy_category_sports  \\\n",
       "0                                      1                       0   \n",
       "1                                      0                       0   \n",
       "2                                      0                       0   \n",
       "3                                      0                       0   \n",
       "4                                      0                       0   \n",
       "...                                  ...                     ...   \n",
       "7390                                   0                       0   \n",
       "7391                                   0                       0   \n",
       "7392                                   0                       0   \n",
       "7393                                   0                       0   \n",
       "7394                                   0                       0   \n",
       "\n",
       "     alchemy_category_unknown alchemy_category_weather label_out id_out  \\\n",
       "0                           0                        0       0.0   1773   \n",
       "1                           1                        0       1.0   1558   \n",
       "2                           0                        0       1.0   7148   \n",
       "3                           1                        0       0.0    565   \n",
       "4                           0                        0       1.0   2606   \n",
       "...                       ...                      ...       ...    ...   \n",
       "7390                        0                        0       NaN   3336   \n",
       "7391                        1                        0       NaN   2867   \n",
       "7392                        0                        0       NaN    738   \n",
       "7393                        1                        0       NaN   4124   \n",
       "7394                        0                        0       NaN   6056   \n",
       "\n",
       "                                      total_description  \n",
       "0     Nebraska California State Bakersfield best rai...  \n",
       "1     Travel Cookbook Discover holiday home owner fa...  \n",
       "2     5 Ways With Leftover Rice 5 way leftover riceD...  \n",
       "3     South Florida Broward Palm Beach Miami Dade br...  \n",
       "4     SoCoTing Protecting servingWe believe short st...  \n",
       "...                                                 ...  \n",
       "7390  UnkonownUnkonownlanascooking 2011 07 08 roaste...  \n",
       "7391  Billy Chocolate Buttercream Martha Stewart Rec...  \n",
       "7392  Los Angeles Best Belly Dance Workout Swerve St...  \n",
       "7393  Buffalo Chicken Burgers recipe buffalo chicken...  \n",
       "7394  canarygirl com Sirloin Peppercorn Cream SauceS...  \n",
       "\n",
       "[7395 rows x 35 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2d8262b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df = pd.concat([main_df,df], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "12110fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_be_deleted = [\"total_description\"]\n",
    "main_df.drop(axis=\"columns\", labels=columns_to_be_deleted, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9ede146a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1479"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df[\"label_out\"].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3a01ed27",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alchemy_category_score</th>\n",
       "      <th>avgLinkWordLength</th>\n",
       "      <th>AvglinkWithOneCommonWord</th>\n",
       "      <th>AvglinkWithTwoCommonWord</th>\n",
       "      <th>AvglinkWithThreeCommonWord</th>\n",
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       "      <th>you</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7393</th>\n",
       "      <td>0.630952</td>\n",
       "      <td>1.743316</td>\n",
       "      <td>0.559091</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.081818</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.573416</td>\n",
       "      <td>0.070261</td>\n",
       "      <td>0.190801</td>\n",
       "      <td>0.545285</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7394</th>\n",
       "      <td>0.715035</td>\n",
       "      <td>2.020243</td>\n",
       "      <td>0.491935</td>\n",
       "      <td>0.217742</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.028226</td>\n",
       "      <td>0.506269</td>\n",
       "      <td>0.012228</td>\n",
       "      <td>0.234657</td>\n",
       "      <td>0.078231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7395 rows × 3034 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alchemy_category_score avgLinkWordLength AvglinkWithOneCommonWord  \\\n",
       "0                   0.58916          2.055838                 0.570962   \n",
       "1                  0.630952          2.097222                 0.697368   \n",
       "2                  0.283897           2.48368                 0.781333   \n",
       "3                  0.630952          2.919255                 0.629328   \n",
       "4                  0.283897          1.571429                 0.380952   \n",
       "...                     ...               ...                      ...   \n",
       "7390               0.681418              1.64                 0.367521   \n",
       "7391               0.630952          2.236559                 0.676768   \n",
       "7392               0.943693          1.829787                  0.51269   \n",
       "7393               0.630952          1.743316                 0.559091   \n",
       "7394               0.715035          2.020243                 0.491935   \n",
       "\n",
       "     AvglinkWithTwoCommonWord AvglinkWithThreeCommonWord  \\\n",
       "0                    0.285481                   0.094617   \n",
       "1                    0.236842                   0.078947   \n",
       "2                    0.341333                   0.138667   \n",
       "3                    0.323829                   0.142566   \n",
       "4                    0.285714                        0.0   \n",
       "...                       ...                        ...   \n",
       "7390                 0.102564                   0.034188   \n",
       "7391                 0.232323                   0.090909   \n",
       "7392                 0.279188                   0.055838   \n",
       "7393                 0.227273                   0.081818   \n",
       "7394                 0.217742                   0.032258   \n",
       "\n",
       "     AvglinkWithFourCommonWord redundancyMeasure frameTagRatio  tagRatio  \\\n",
       "0                      0.04894          0.468646      0.028002  0.170434   \n",
       "1                          0.0          0.494215      0.072948  0.265497   \n",
       "2                     0.066667          0.390071      0.054522  0.219335   \n",
       "3                     0.095723          0.505976      0.037284  0.120069   \n",
       "4                          0.0          0.625323      0.142339  0.234109   \n",
       "...                        ...               ...           ...       ...   \n",
       "7390                  0.017094          0.782845      0.071719  0.237913   \n",
       "7391                  0.020202          0.413333      0.056471  0.237229   \n",
       "7392                  0.020305          0.547304       0.04129   0.21018   \n",
       "7393                  0.018182          0.573416      0.070261  0.190801   \n",
       "7394                  0.028226          0.506269      0.012228  0.234657   \n",
       "\n",
       "     imageTagRatio  ...       you young      your youre youtube  yum yummy  \\\n",
       "0         0.084942  ...  0.000000   0.0  0.051423   0.0     0.0  0.0   0.0   \n",
       "1         0.014563  ...  0.000000   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "2         0.253165  ...  0.038678   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "3         0.185976  ...  0.000000   0.0  0.081983   0.0     0.0  0.0   0.0   \n",
       "4         0.026667  ...  0.000000   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "...            ...  ...       ...   ...       ...   ...     ...  ...   ...   \n",
       "7390     -0.286541  ...  0.000000   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "7391      0.024793  ...  0.040347   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "7392      0.433526  ...  0.023822   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "7393      0.545285  ...  0.000000   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "7394      0.078231  ...  0.000000   0.0  0.000000   0.0     0.0  0.0   0.0   \n",
       "\n",
       "     zest zone zucchini  \n",
       "0     0.0  0.0      0.0  \n",
       "1     0.0  0.0      0.0  \n",
       "2     0.0  0.0      0.0  \n",
       "3     0.0  0.0      0.0  \n",
       "4     0.0  0.0      0.0  \n",
       "...   ...  ...      ...  \n",
       "7390  0.0  0.0      0.0  \n",
       "7391  0.0  0.0      0.0  \n",
       "7392  0.0  0.0      0.0  \n",
       "7393  0.0  0.0      0.0  \n",
       "7394  0.0  0.0      0.0  \n",
       "\n",
       "[7395 rows x 3034 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "bd405e50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>00</th>\n",
       "      <th>000</th>\n",
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       "      <th>yummy</th>\n",
       "      <th>zest</th>\n",
       "      <th>zone</th>\n",
       "      <th>zucchini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>7395.000000</td>\n",
       "      <td>7395.000000</td>\n",
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       "      <td>7395.000000</td>\n",
       "      <td>7395.000000</td>\n",
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       "      <td>7395.000000</td>\n",
       "      <td>7395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.001881</td>\n",
       "      <td>0.003778</td>\n",
       "      <td>0.002771</td>\n",
       "      <td>0.003198</td>\n",
       "      <td>0.002878</td>\n",
       "      <td>0.003427</td>\n",
       "      <td>0.002957</td>\n",
       "      <td>0.002320</td>\n",
       "      <td>0.002896</td>\n",
       "      <td>0.003463</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011353</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0.005030</td>\n",
       "      <td>0.001154</td>\n",
       "      <td>0.001611</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>0.001682</td>\n",
       "      <td>0.000618</td>\n",
       "      <td>0.002220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.025143</td>\n",
       "      <td>0.028220</td>\n",
       "      <td>0.023132</td>\n",
       "      <td>0.024861</td>\n",
       "      <td>0.025313</td>\n",
       "      <td>0.029335</td>\n",
       "      <td>0.022855</td>\n",
       "      <td>0.018103</td>\n",
       "      <td>0.024086</td>\n",
       "      <td>0.026874</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026665</td>\n",
       "      <td>0.013323</td>\n",
       "      <td>0.023225</td>\n",
       "      <td>0.013844</td>\n",
       "      <td>0.018502</td>\n",
       "      <td>0.009298</td>\n",
       "      <td>0.010324</td>\n",
       "      <td>0.014677</td>\n",
       "      <td>0.011008</td>\n",
       "      <td>0.032092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012498</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.793628</td>\n",
       "      <td>0.973553</td>\n",
       "      <td>0.762891</td>\n",
       "      <td>0.624134</td>\n",
       "      <td>0.841834</td>\n",
       "      <td>0.749769</td>\n",
       "      <td>0.533200</td>\n",
       "      <td>0.596888</td>\n",
       "      <td>0.560138</td>\n",
       "      <td>0.550956</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532259</td>\n",
       "      <td>0.411719</td>\n",
       "      <td>0.544086</td>\n",
       "      <td>0.395880</td>\n",
       "      <td>0.682678</td>\n",
       "      <td>0.484749</td>\n",
       "      <td>0.264217</td>\n",
       "      <td>0.309393</td>\n",
       "      <td>0.755525</td>\n",
       "      <td>0.778779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 3000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                00          000           01           02           03  \\\n",
       "count  7395.000000  7395.000000  7395.000000  7395.000000  7395.000000   \n",
       "mean      0.001881     0.003778     0.002771     0.003198     0.002878   \n",
       "std       0.025143     0.028220     0.023132     0.024861     0.025313   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       0.793628     0.973553     0.762891     0.624134     0.841834   \n",
       "\n",
       "                04           05           06           07           08  ...  \\\n",
       "count  7395.000000  7395.000000  7395.000000  7395.000000  7395.000000  ...   \n",
       "mean      0.003427     0.002957     0.002320     0.002896     0.003463  ...   \n",
       "std       0.029335     0.022855     0.018103     0.024086     0.026874  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       0.749769     0.533200     0.596888     0.560138     0.550956  ...   \n",
       "\n",
       "               you        young         your        youre      youtube  \\\n",
       "count  7395.000000  7395.000000  7395.000000  7395.000000  7395.000000   \n",
       "mean      0.011353     0.001971     0.005030     0.001154     0.001611   \n",
       "std       0.026665     0.013323     0.023225     0.013844     0.018502   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.012498     0.000000     0.000000     0.000000     0.000000   \n",
       "max       0.532259     0.411719     0.544086     0.395880     0.682678   \n",
       "\n",
       "               yum        yummy         zest         zone     zucchini  \n",
       "count  7395.000000  7395.000000  7395.000000  7395.000000  7395.000000  \n",
       "mean      0.000710     0.001176     0.001682     0.000618     0.002220  \n",
       "std       0.009298     0.010324     0.014677     0.011008     0.032092  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       0.484749     0.264217     0.309393     0.755525     0.778779  \n",
       "\n",
       "[8 rows x 3000 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc28fef9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "28620e4d",
   "metadata": {},
   "source": [
    "### Test - Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "00567aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = main_df.loc[main_df[\"label_out\"].isna() == False,:]\n",
    "test_df = main_df.loc[main_df[\"label_out\"].isna(),:].drop(columns=\"label_out\")\n",
    "\n",
    "train_df = train_df.drop(columns=\"id_out\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "208ea98d",
   "metadata": {},
   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>00</th>\n",
       "      <th>000</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>03</th>\n",
       "      <th>04</th>\n",
       "      <th>05</th>\n",
       "      <th>06</th>\n",
       "      <th>07</th>\n",
       "      <th>08</th>\n",
       "      <th>...</th>\n",
       "      <th>you</th>\n",
       "      <th>young</th>\n",
       "      <th>your</th>\n",
       "      <th>youre</th>\n",
       "      <th>youtube</th>\n",
       "      <th>yum</th>\n",
       "      <th>yummy</th>\n",
       "      <th>zest</th>\n",
       "      <th>zone</th>\n",
       "      <th>zucchini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "      <td>5916.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.001895</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.002804</td>\n",
       "      <td>0.003092</td>\n",
       "      <td>0.002905</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.003111</td>\n",
       "      <td>0.002318</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>0.003386</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011450</td>\n",
       "      <td>0.002002</td>\n",
       "      <td>0.005013</td>\n",
       "      <td>0.001306</td>\n",
       "      <td>0.001683</td>\n",
       "      <td>0.000719</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.001769</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.002183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.024716</td>\n",
       "      <td>0.024894</td>\n",
       "      <td>0.022731</td>\n",
       "      <td>0.024635</td>\n",
       "      <td>0.025186</td>\n",
       "      <td>0.030339</td>\n",
       "      <td>0.023070</td>\n",
       "      <td>0.018149</td>\n",
       "      <td>0.024200</td>\n",
       "      <td>0.026630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026947</td>\n",
       "      <td>0.013711</td>\n",
       "      <td>0.023114</td>\n",
       "      <td>0.015161</td>\n",
       "      <td>0.018968</td>\n",
       "      <td>0.009761</td>\n",
       "      <td>0.010565</td>\n",
       "      <td>0.015038</td>\n",
       "      <td>0.012076</td>\n",
       "      <td>0.031888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012761</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.793628</td>\n",
       "      <td>0.973553</td>\n",
       "      <td>0.762891</td>\n",
       "      <td>0.624134</td>\n",
       "      <td>0.841834</td>\n",
       "      <td>0.749769</td>\n",
       "      <td>0.533200</td>\n",
       "      <td>0.596888</td>\n",
       "      <td>0.560138</td>\n",
       "      <td>0.550956</td>\n",
       "      <td>...</td>\n",
       "      <td>0.532259</td>\n",
       "      <td>0.411719</td>\n",
       "      <td>0.544086</td>\n",
       "      <td>0.395880</td>\n",
       "      <td>0.682678</td>\n",
       "      <td>0.484749</td>\n",
       "      <td>0.264217</td>\n",
       "      <td>0.309393</td>\n",
       "      <td>0.755525</td>\n",
       "      <td>0.778779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 3000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                00          000           01           02           03  \\\n",
       "count  5916.000000  5916.000000  5916.000000  5916.000000  5916.000000   \n",
       "mean      0.001895     0.003550     0.002804     0.003092     0.002905   \n",
       "std       0.024716     0.024894     0.022731     0.024635     0.025186   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       0.793628     0.973553     0.762891     0.624134     0.841834   \n",
       "\n",
       "                04           05           06           07           08  ...  \\\n",
       "count  5916.000000  5916.000000  5916.000000  5916.000000  5916.000000  ...   \n",
       "mean      0.003613     0.003111     0.002318     0.002892     0.003386  ...   \n",
       "std       0.030339     0.023070     0.018149     0.024200     0.026630  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "max       0.749769     0.533200     0.596888     0.560138     0.550956  ...   \n",
       "\n",
       "               you        young         your        youre      youtube  \\\n",
       "count  5916.000000  5916.000000  5916.000000  5916.000000  5916.000000   \n",
       "mean      0.011450     0.002002     0.005013     0.001306     0.001683   \n",
       "std       0.026947     0.013711     0.023114     0.015161     0.018968   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.012761     0.000000     0.000000     0.000000     0.000000   \n",
       "max       0.532259     0.411719     0.544086     0.395880     0.682678   \n",
       "\n",
       "               yum        yummy         zest         zone     zucchini  \n",
       "count  5916.000000  5916.000000  5916.000000  5916.000000  5916.000000  \n",
       "mean      0.000719     0.001197     0.001769     0.000665     0.002183  \n",
       "std       0.009761     0.010565     0.015038     0.012076     0.031888  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "max       0.484749     0.264217     0.309393     0.755525     0.778779  \n",
       "\n",
       "[8 rows x 3000 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "23fd57c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5916    7172\n",
       "5917    2687\n",
       "5918    6990\n",
       "5919    5639\n",
       "5920    2701\n",
       "        ... \n",
       "7390    3336\n",
       "7391    2867\n",
       "7392     738\n",
       "7393    4124\n",
       "7394    6056\n",
       "Name: id_out, Length: 1479, dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df[\"id_out\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0bca2789",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sai/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label_out', ylabel='count'>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train_df[\"label_out\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "a3a00293",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_x = train_df.drop(columns=\"label_out\").to_numpy().astype(\"float\")\n",
    "main_y = train_df[\"label_out\"].to_numpy().astype(\"float\")\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(main_x, main_y, train_size=0.6, stratify=main_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e8288f",
   "metadata": {},
   "source": [
    "# 2. Model Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "733798b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "train_x_scaled = scaler.fit_transform(train_x)\n",
    "\n",
    "test_x_scaled = scaler.transform(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "96a81756",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=300)\n",
    "train_x_pca = pca.fit_transform(train_x_scaled)\n",
    "test_x_pca = pca.transform(test_x_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efc5b50",
   "metadata": {},
   "source": [
    "### SVM with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "4f84e82e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.5, class_weight='balanced', probability=True)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pca = SVC(kernel = 'rbf',class_weight = \"balanced\",C = 0.5, probability = True,gamma = 'scale')\n",
    "clf_pca.fit(train_x_pca, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "3aeea3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_yhat = clf_pca.predict_proba(test_x_pca)[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "7bd74059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8734288547715945"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.roc_auc_score(test_y, test_yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30aa5c03",
   "metadata": {},
   "source": [
    "## 3 Kaggle submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "3811533a",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_x_scaled = scaler.fit_transform(main_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "3c351cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1479, 3032)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaggle_test_X = test_df.drop(columns=\"id_out\", inplace=False).to_numpy().astype(\"float\")\n",
    "\n",
    "kaggle_x_scaled = scaler.fit_transform(kaggle_test_X)\n",
    "\n",
    "kaggle_test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "f2863c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_x_pca = pca.fit_transform(main_x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "153f68ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_x_pca = pca.transform(kaggle_x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "14572f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdf_new = test_df[\"id_out\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "cdfb9c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdf_new = testdf_new.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "368a7ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "testdf_new[\"id\"] = test_df[\"id_out\"]\n",
    "testdf_new.drop(columns=\"id_out\",inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc74c6fc",
   "metadata": {},
   "source": [
    "### SVM with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "3d088bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=0.5, class_weight='balanced', probability=True)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_pca.fit(main_x_pca,main_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "c79c0439",
   "metadata": {},
   "outputs": [],
   "source": [
    "kaggle_test_yhat = clf_pca.predict_proba(kaggle_x_pca)[:,-1]\n",
    "testdf_new[\"label\"] = kaggle_test_yhat \n",
    "to_submit = testdf_new[[\"id\", \"label\"]]\n",
    "to_submit.to_csv(\"phantom_svm_pca_sub.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}