Reason_for_closure_using_Logistic-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>AboutMe</th>\n",
       "      <th>Body</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Downvotes</th>\n",
       "      <th>Location</th>\n",
       "      <th>PostId</th>\n",
       "      <th>ProfileImageUrl</th>\n",
       "      <th>Reputation</th>\n",
       "      <th>SOUrlCount</th>\n",
       "      <th>...</th>\n",
       "      <th>sentencesStartWithICount</th>\n",
       "      <th>sentencesStartWithYouCount</th>\n",
       "      <th>shortWordCount</th>\n",
       "      <th>smogIndex</th>\n",
       "      <th>tagCount</th>\n",
       "      <th>tagWeightSum</th>\n",
       "      <th>titleLengthInChars</th>\n",
       "      <th>urlCount</th>\n",
       "      <th>userProfileFilled</th>\n",
       "      <th>websiteUrlFilled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;p&gt;Please someone explain complete step by ste...</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32203002</td>\n",
       "      <td>https://lh4.googleusercontent.com/-_GtduZ_HroM...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>-110.462467</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;p&gt;I've used SFST language for my project on N...</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31961877</td>\n",
       "      <td>https://lh4.googleusercontent.com/-_GtduZ_HroM...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-157.135019</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>&lt;p&gt;Currently a software developer at HiQ Finla...</td>\n",
       "      <td>&lt;p&gt;The following JSON is returned from SharePo...</td>\n",
       "      <td>100</td>\n",
       "      <td>2413</td>\n",
       "      <td>Helsinki, Finland</td>\n",
       "      <td>22080350</td>\n",
       "      <td>https://i.stack.imgur.com/w2whQ.png</td>\n",
       "      <td>33857</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-279.862500</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>&lt;p&gt;Love the computer science &amp;amp; technology ...</td>\n",
       "      <td>&lt;p&gt;What's the benifit of allocating a chunk of...</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>中国Shanghai Shi</td>\n",
       "      <td>28019349</td>\n",
       "      <td>https://www.gravatar.com/avatar/a33ac21cad99e5...</td>\n",
       "      <td>1470</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>-234.970000</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;p&gt;I'm using an S3 bucket as the origin or a C...</td>\n",
       "      <td>100</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32211159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10992</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>10.7</td>\n",
       "      <td>4</td>\n",
       "      <td>-527.002535</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                            AboutMe  \\\n",
       "0           0                                                NaN   \n",
       "1           1                                                NaN   \n",
       "2           2  <p>Currently a software developer at HiQ Finla...   \n",
       "3           3  <p>Love the computer science &amp; technology ...   \n",
       "4           4                                                NaN   \n",
       "\n",
       "                                                Body  Comment  Downvotes  \\\n",
       "0  <p>Please someone explain complete step by ste...      100          0   \n",
       "1  <p>I've used SFST language for my project on N...      100          0   \n",
       "2  <p>The following JSON is returned from SharePo...      100       2413   \n",
       "3  <p>What's the benifit of allocating a chunk of...      100          3   \n",
       "4  <p>I'm using an S3 bucket as the origin or a C...      100         77   \n",
       "\n",
       "            Location    PostId  \\\n",
       "0                NaN  32203002   \n",
       "1                NaN  31961877   \n",
       "2  Helsinki, Finland  22080350   \n",
       "3     中国Shanghai Shi  28019349   \n",
       "4                NaN  32211159   \n",
       "\n",
       "                                     ProfileImageUrl  Reputation  SOUrlCount  \\\n",
       "0  https://lh4.googleusercontent.com/-_GtduZ_HroM...           6           0   \n",
       "1  https://lh4.googleusercontent.com/-_GtduZ_HroM...           6           0   \n",
       "2                https://i.stack.imgur.com/w2whQ.png       33857           0   \n",
       "3  https://www.gravatar.com/avatar/a33ac21cad99e5...        1470           0   \n",
       "4                                                NaN       10992           0   \n",
       "\n",
       "         ...        sentencesStartWithICount sentencesStartWithYouCount  \\\n",
       "0        ...                               0                          0   \n",
       "1        ...                               3                          0   \n",
       "2        ...                               0                          0   \n",
       "3        ...                               0                          0   \n",
       "4        ...                               2                          0   \n",
       "\n",
       "   shortWordCount  smogIndex  tagCount  tagWeightSum titleLengthInChars  \\\n",
       "0               6        0.0         2   -110.462467                 33   \n",
       "1              27        0.0         4   -157.135019                 25   \n",
       "2              25        0.0         3   -279.862500                134   \n",
       "3              10        0.0         5   -234.970000                 78   \n",
       "4              35       10.7         4   -527.002535                 59   \n",
       "\n",
       "   urlCount  userProfileFilled  websiteUrlFilled  \n",
       "0         0                  1                 0  \n",
       "1         2                  1                 0  \n",
       "2         0                  4                 1  \n",
       "3         0                  3                 0  \n",
       "4         0                  0                 0  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3 rows deleted:\n",
    "#20484\t,RegEx used to extract tokens from an ASCIIMath expression\"\t11\t\t1\t0\t0\thttps://i.stack.imgur.com/6VcaB.png\t\t\t\t\t\t\t\t\t0\t1\t\t0\t0\t0\t0\t0\t1\t0\t0\t0\t1\t1\t0\t0\t0\t0\t0\n",
    "#71350\t,Bootstrap modal wont trigger on mobile\"\t443\tLake Mary, FL, United States\t85\t165\t11\thttps://i.stack.imgur.com/TLzJW.png?s=128&g=1\t\t\t\t\t\t\t\t\t0\t1\t\t0\t0\t0\t0\t0\t3\t0\t0\t0\t1\t1\t0\t0\t0\t0\t0\n",
    "#60036\t<p>from group \"\"Value\"\" i can get the value of hour and it gives output as 1\t3\t2\t4\t5\t6 and so on for each generator name but since no of hour is common to all i need that i get it only once that is i should get 1\t2\t5\t6 only once</p>\t\t\t\t\t\t\t0\t1\t\t0\t0\t0\t0\t0\t1\t0\t0\t0\t1\t1\t0\t1\t0\t0\t0\n",
    "\n",
    "openCloseData=pd.read_csv(\"openCloseData9.csv\")\n",
    "openCloseData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996, 47)\n",
      "(159996, 46)\n"
     ]
    }
   ],
   "source": [
    "print(openCloseData.shape)\n",
    "openCloseData.drop(['Unnamed: 0'],inplace=True,axis=1)\n",
    "print(openCloseData.shape)\n",
    "#Actually 4 rows are removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AboutMe', 'Body', 'Comment', 'Downvotes', 'Location', 'PostId',\n",
       "       'ProfileImageUrl', 'Reputation', 'SOUrlCount', 'Tags', 'Title',\n",
       "       'Unnamed: 0.1', 'Upvotes', 'UserId', 'Views', 'WebsiteUrl',\n",
       "       'aboutMeFilled', 'bodyWordCount', 'closed', 'codeLength',\n",
       "       'colemanLiauIndex', 'countOfInterrogativeSent',\n",
       "       'daleChallReadabilityScore', 'experienceInTime', 'firstLineLength',\n",
       "       'fleschReadingEase', 'gunningFog', 'locationFilled', 'lowerUpperRatio',\n",
       "       'numAns', 'numPR', 'numPeopleReached', 'numQues',\n",
       "       'profileImageUrlFilled', 'punctuationCount', 'sentenceCount',\n",
       "       'sentencesStartWithICount', 'sentencesStartWithYouCount',\n",
       "       'shortWordCount', 'smogIndex', 'tagCount', 'tagWeightSum',\n",
       "       'titleLengthInChars', 'urlCount', 'userProfileFilled',\n",
       "       'websiteUrlFilled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openCloseData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996, 45)\n"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/33961028/remove-non-numeric-rows-in-one-column-with-pandas\n",
    "openCloseData.drop(['Unnamed: 0.1'],inplace=True,axis=1)\n",
    "print(openCloseData.shape)\n",
    "#openCloseData[openCloseData.Reputation.apply(lambda x: x.isnumeric())]\n",
    "#print(openCloseData.dtypes)\n",
    "#print(openCloseData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                             AboutMe  \\\n",
      "0                                                NaN   \n",
      "1                                                NaN   \n",
      "2  <p>Currently a software developer at HiQ Finla...   \n",
      "3  <p>Love the computer science &amp; technology ...   \n",
      "4                                                NaN   \n",
      "\n",
      "                                                Body  Comment  Downvotes  \\\n",
      "0  <p>Please someone explain complete step by ste...      100          0   \n",
      "1  <p>I've used SFST language for my project on N...      100          0   \n",
      "2  <p>The following JSON is returned from SharePo...      100       2413   \n",
      "3  <p>What's the benifit of allocating a chunk of...      100          3   \n",
      "4  <p>I'm using an S3 bucket as the origin or a C...      100         77   \n",
      "\n",
      "            Location    PostId  \\\n",
      "0                NaN  32203002   \n",
      "1                NaN  31961877   \n",
      "2  Helsinki, Finland  22080350   \n",
      "3     中国Shanghai Shi  28019349   \n",
      "4                NaN  32211159   \n",
      "\n",
      "                                     ProfileImageUrl  Reputation  SOUrlCount  \\\n",
      "0  https://lh4.googleusercontent.com/-_GtduZ_HroM...           6           0   \n",
      "1  https://lh4.googleusercontent.com/-_GtduZ_HroM...           6           0   \n",
      "2                https://i.stack.imgur.com/w2whQ.png       33857           0   \n",
      "3  https://www.gravatar.com/avatar/a33ac21cad99e5...        1470           0   \n",
      "4                                                NaN       10992           0   \n",
      "\n",
      "                                                Tags        ...         \\\n",
      "0                                       kernel|minix        ...          \n",
      "1                          python|nlp|transducer|fst        ...          \n",
      "2                               json|rest|powershell        ...          \n",
      "3  linux|memory-management|linux-kernel|memory-ad...        ...          \n",
      "4  amazon-web-services|amazon-s3|cdn|amazon-cloud...        ...          \n",
      "\n",
      "  sentencesStartWithICount  sentencesStartWithYouCount  shortWordCount  \\\n",
      "0                        0                           0               6   \n",
      "1                        3                           0              27   \n",
      "2                        0                           0              25   \n",
      "3                        0                           0              10   \n",
      "4                        2                           0              35   \n",
      "\n",
      "   smogIndex tagCount  tagWeightSum  titleLengthInChars  urlCount  \\\n",
      "0        0.0        2   -110.462467                  33         0   \n",
      "1        0.0        4   -157.135019                  25         2   \n",
      "2        0.0        3   -279.862500                 134         0   \n",
      "3        0.0        5   -234.970000                  78         0   \n",
      "4       10.7        4   -527.002535                  59         0   \n",
      "\n",
      "   userProfileFilled  websiteUrlFilled  \n",
      "0                  1                 0  \n",
      "1                  1                 0  \n",
      "2                  4                 1  \n",
      "3                  3                 0  \n",
      "4                  0                 0  \n",
      "\n",
      "[5 rows x 45 columns]\n"
     ]
    }
   ],
   "source": [
    "print(openCloseData.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "closeData = openCloseData[openCloseData['closed'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79996, 45)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closeData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AboutMe', 'Body', 'Comment', 'Downvotes', 'Location', 'PostId',\n",
       "       'ProfileImageUrl', 'Reputation', 'SOUrlCount', 'Tags', 'Title',\n",
       "       'Upvotes', 'UserId', 'Views', 'WebsiteUrl', 'aboutMeFilled',\n",
       "       'bodyWordCount', 'closed', 'codeLength', 'colemanLiauIndex',\n",
       "       'countOfInterrogativeSent', 'daleChallReadabilityScore',\n",
       "       'experienceInTime', 'firstLineLength', 'fleschReadingEase',\n",
       "       'gunningFog', 'locationFilled', 'lowerUpperRatio', 'numAns', 'numPR',\n",
       "       'numPeopleReached', 'numQues', 'profileImageUrlFilled',\n",
       "       'punctuationCount', 'sentenceCount', 'sentencesStartWithICount',\n",
       "       'sentencesStartWithYouCount', 'shortWordCount', 'smogIndex', 'tagCount',\n",
       "       'tagWeightSum', 'titleLengthInChars', 'urlCount', 'userProfileFilled',\n",
       "       'websiteUrlFilled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closeData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(79996, 28)\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "X = closeData.loc[:,['Comment', 'Reputation',\n",
    "       'experienceInTime', 'Views',\n",
    "       'Upvotes', 'Downvotes', 'tagCount',\n",
    "       'lowerUpperRatio', 'sentenceCount', 'firstLineLength', 'codeLength',\n",
    "       'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
    "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
    "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
    "       'punctuationCount', 'gunningFog',\n",
    "       'fleschReadingEase', 'daleChallReadabilityScore', 'smogIndex',\n",
    "       'colemanLiauIndex', 'userProfileFilled', 'numAns', 'numQues'] ]\n",
    "X.replace(np.nan,0,inplace=True)  #replacing NaN to 0\n",
    "X.lowerUpperRatio.replace(np.inf,0,inplace=True)  #replacing infinity to 0\n",
    "print(X.shape)\n",
    "print(type(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comment</th>\n",
       "      <th>Reputation</th>\n",
       "      <th>experienceInTime</th>\n",
       "      <th>Views</th>\n",
       "      <th>Upvotes</th>\n",
       "      <th>Downvotes</th>\n",
       "      <th>tagCount</th>\n",
       "      <th>lowerUpperRatio</th>\n",
       "      <th>sentenceCount</th>\n",
       "      <th>firstLineLength</th>\n",
       "      <th>...</th>\n",
       "      <th>bodyWordCount</th>\n",
       "      <th>punctuationCount</th>\n",
       "      <th>gunningFog</th>\n",
       "      <th>fleschReadingEase</th>\n",
       "      <th>daleChallReadabilityScore</th>\n",
       "      <th>smogIndex</th>\n",
       "      <th>colemanLiauIndex</th>\n",
       "      <th>userProfileFilled</th>\n",
       "      <th>numAns</th>\n",
       "      <th>numQues</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80000</th>\n",
       "      <td>102</td>\n",
       "      <td>17527</td>\n",
       "      <td>1953</td>\n",
       "      <td>2765</td>\n",
       "      <td>2005</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>26.153846</td>\n",
       "      <td>6</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>168</td>\n",
       "      <td>50</td>\n",
       "      <td>71.70</td>\n",
       "      <td>-80.10</td>\n",
       "      <td>13.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.83</td>\n",
       "      <td>2</td>\n",
       "      <td>583</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80001</th>\n",
       "      <td>102</td>\n",
       "      <td>151</td>\n",
       "      <td>1425</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>16.448276</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>19</td>\n",
       "      <td>52.99</td>\n",
       "      <td>-26.31</td>\n",
       "      <td>12.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80002</th>\n",
       "      <td>102</td>\n",
       "      <td>1639</td>\n",
       "      <td>900</td>\n",
       "      <td>208</td>\n",
       "      <td>75</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18.916667</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "      <td>17.06</td>\n",
       "      <td>64.54</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.97</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80003</th>\n",
       "      <td>102</td>\n",
       "      <td>2669</td>\n",
       "      <td>620</td>\n",
       "      <td>658</td>\n",
       "      <td>282</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>12.272727</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>94</td>\n",
       "      <td>45</td>\n",
       "      <td>26.89</td>\n",
       "      <td>32.23</td>\n",
       "      <td>9.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.02</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80004</th>\n",
       "      <td>105</td>\n",
       "      <td>2669</td>\n",
       "      <td>369</td>\n",
       "      <td>658</td>\n",
       "      <td>282</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>32.714286</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>128</td>\n",
       "      <td>20.32</td>\n",
       "      <td>53.58</td>\n",
       "      <td>7.94</td>\n",
       "      <td>11.9</td>\n",
       "      <td>15.97</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Comment  Reputation  experienceInTime  Views  Upvotes  Downvotes  \\\n",
       "80000      102       17527              1953   2765     2005        119   \n",
       "80001      102         151              1425     32       16          0   \n",
       "80002      102        1639               900    208       75          1   \n",
       "80003      102        2669               620    658      282         11   \n",
       "80004      105        2669               369    658      282         11   \n",
       "\n",
       "       tagCount  lowerUpperRatio  sentenceCount  firstLineLength   ...     \\\n",
       "80000         4        26.153846              6               69   ...      \n",
       "80001         5        16.448276             10               75   ...      \n",
       "80002         3        18.916667              4               79   ...      \n",
       "80003         2        12.272727              5              210   ...      \n",
       "80004         2        32.714286             10               35   ...      \n",
       "\n",
       "       bodyWordCount  punctuationCount  gunningFog  fleschReadingEase  \\\n",
       "80000            168                50       71.70             -80.10   \n",
       "80001            113                19       52.99             -26.31   \n",
       "80002             47                17       17.06              64.54   \n",
       "80003             94                45       26.89              32.23   \n",
       "80004            104               128       20.32              53.58   \n",
       "\n",
       "       daleChallReadabilityScore  smogIndex  colemanLiauIndex  \\\n",
       "80000                      13.96        0.0              9.83   \n",
       "80001                      12.32        0.0             11.98   \n",
       "80002                       7.83        0.0             14.97   \n",
       "80003                       9.16        0.0             17.02   \n",
       "80004                       7.94       11.9             15.97   \n",
       "\n",
       "       userProfileFilled  numAns  numQues  \n",
       "80000                  2     583      209  \n",
       "80001                  0       0        0  \n",
       "80002                  3      20       13  \n",
       "80003                  4      43       70  \n",
       "80004                  4      43       70  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(79996, 28)\n"
     ]
    }
   ],
   "source": [
    "X['Comment']=X['Comment']-102\n",
    "#print(offTopic.head())\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.reset_index(inplace = True)\n",
    "X.drop(['index'],inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(79996,)\n"
     ]
    }
   ],
   "source": [
    "# print(X[np.isinf(X).any(axis=1)])\n",
    "y = X.Comment\n",
    "X.drop(['Comment'],inplace=True,axis=1)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(79996, 27)\n",
      "Index(['Reputation', 'experienceInTime', 'Views', 'Upvotes', 'Downvotes',\n",
      "       'tagCount', 'lowerUpperRatio', 'sentenceCount', 'firstLineLength',\n",
      "       'codeLength', 'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
      "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
      "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
      "       'punctuationCount', 'gunningFog', 'fleschReadingEase',\n",
      "       'daleChallReadabilityScore', 'smogIndex', 'colemanLiauIndex',\n",
      "       'userProfileFilled', 'numAns', 'numQues'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(y[0])\n",
    "print(y[25000])\n",
    "print(y[45000])\n",
    "print(y[65000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# x=(x-u)/std\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "#print(scaler.fit(X))\n",
    "#print(scaler.mean_)\n",
    "X=scaler.fit_transform(X)\n",
    "print(type(X))\n",
    "X = pd.DataFrame(X, columns=['Reputation', 'experienceInTime', 'Views', 'Upvotes', 'Downvotes',\n",
    "       'tagCount', 'lowerUpperRatio', 'sentenceCount', 'firstLineLength',\n",
    "       'codeLength', 'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
    "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
    "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
    "       'punctuationCount', 'gunningFog', 'fleschReadingEase',\n",
    "       'daleChallReadabilityScore', 'smogIndex', 'colemanLiauIndex',\n",
    "       'userProfileFilled', 'numAns', 'numQues'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55997, 27)\n",
      "(55997,)\n",
      "(23999, 27)\n",
      "(23999,)\n"
     ]
    }
   ],
   "source": [
    "#Train/Test Split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression()\n",
    "logreg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRAIN ACCURACY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40393235351893853"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train=logreg.predict(X_train)\n",
    "#Train Accuracy\n",
    "np.mean(y_pred_train==y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TEST ACCURACY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23999,)\n"
     ]
    }
   ],
   "source": [
    "y_pred=logreg.predict(X_test)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4053085545231051"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Test Accuracy\n",
    "np.mean(y_pred==y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.35      0.20      0.26      5916\n",
      "          1       0.39      0.52      0.45      6116\n",
      "          2       0.34      0.26      0.30      5915\n",
      "          3       0.48      0.62      0.54      6052\n",
      "\n",
      "avg / total       0.39      0.41      0.39     23999\n",
      "\n",
      "Precision: 0.391009474252\n",
      "Recall: 0.403050797276\n",
      "F1 Score:0.386205400178\n",
      "(0.39100947425227545, 0.4030507972763665, 0.38620540017751392, None)\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred) )\n",
    "print(\"Precision: \"+str(precision_score(y_test, y_pred, average='macro')) )\n",
    "print(\"Recall: \"+str(recall_score(y_test, y_pred, average='macro')) )\n",
    "print(\"F1 Score:\"+str(f1_score(y_test, y_pred, average='macro')) )\n",
    "print(precision_recall_fscore_support(y_test, y_pred, average='macro') )\n",
    "# Precision, recall, fscore, support in order\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO REGULARIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01, 0.1, 1, 10, 100, 1000]\n",
      "0.01\n",
      "lambda=0.01 c=100.0 Accuracy:0.405433559732\n",
      "0.1\n",
      "lambda=0.1 c=10.0 Accuracy:0.405516896537\n",
      "1\n",
      "lambda=1 c=1.0 Accuracy:0.405141880912\n",
      "10\n",
      "lambda=10 c=0.1 Accuracy:0.404433518063\n",
      "100\n",
      "lambda=100 c=0.01 Accuracy:0.403641818409\n",
      "1000\n",
      "lambda=1000 c=0.001 Accuracy:0.389057877412\n"
     ]
    }
   ],
   "source": [
    "#lambdas=list(range(1,50,1))\n",
    "lambdas=[0.01,0.1,1,10,100,1000]\n",
    "print(lambdas)\n",
    "for l in lambdas:\n",
    "    print(l)\n",
    "    lassoLogReg=LogisticRegression(penalty=\"l1\",C=1/l)\n",
    "    lassoLogReg.fit(X_train,y_train)\n",
    "    y_pred=lassoLogReg.predict(X_test)\n",
    "    #print(y_pred.shape)\n",
    "    #Accuracy\n",
    "    print(\"lambda=\"+str(l)+\" c=\"+str(1/l)+\" Accuracy:\"+str(np.mean(y_pred==y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RIGDE REGULARIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
      "lambda=0.001 c=1000.0 Accuracy:0.405433559732\n",
      "lambda=0.01 c=100.0 Accuracy:0.405433559732\n",
      "lambda=0.1 c=10.0 Accuracy:0.405433559732\n",
      "lambda=1 c=1.0 Accuracy:0.405308554523\n",
      "lambda=10 c=0.1 Accuracy:0.404766865286\n",
      "lambda=100 c=0.01 Accuracy:0.404433518063\n",
      "lambda=1000 c=0.001 Accuracy:0.399724988541\n"
     ]
    }
   ],
   "source": [
    "#lambdas=list(range(1,50,2))\n",
    "lambdas=[0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "print(lambdas)\n",
    "for l in lambdas:\n",
    "    ridgeLogReg=LogisticRegression(penalty=\"l2\",C=1/l)\n",
    "    ridgeLogReg.fit(X_train,y_train)\n",
    "    y_pred=ridgeLogReg.predict(X_test)\n",
    "    #print(y_pred.shape)\n",
    "    #Accuracy\n",
    "    print(\"lambda=\"+str(l)+\" c=\"+str(1/l)+\" Accuracy:\"+str(np.mean(y_pred==y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import KFold,cross_val_score\n",
    "k_fold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Reputation  experienceInTime     Views   Upvotes  Downvotes  tagCount  \\\n",
      "0        1.174938          2.671481  1.212542  2.312384   0.254255  0.981698   \n",
      "1       -0.170701          1.742801 -0.154499 -0.273151  -0.055221  1.795214   \n",
      "2       -0.055467          0.819398 -0.066464 -0.196456  -0.052620  0.168182   \n",
      "3        0.024299          0.326916  0.158625  0.072627  -0.026614 -0.645333   \n",
      "4        0.024299         -0.114558  0.158625  0.072627  -0.026614 -0.645333   \n",
      "5       -0.163421          0.376164 -0.131489 -0.275750  -0.055221  0.981698   \n",
      "6       -0.181775         -0.763579 -0.168504 -0.291349  -0.055221  1.795214   \n",
      "7       -0.181930         -0.763579 -0.170505 -0.293949  -0.055221  0.168182   \n",
      "8        0.548816         -0.605281  0.379713  0.756383  -0.018812  0.168182   \n",
      "9        0.548816          1.436759  0.379713  0.756383  -0.018812  1.795214   \n",
      "10       0.548816          0.019115  0.379713  0.756383  -0.018812 -0.645333   \n",
      "11      -0.173024         -0.700260 -0.148496 -0.282250  -0.055221 -0.645333   \n",
      "12      -0.173024          1.044532 -0.148496 -0.282250  -0.055221 -0.645333   \n",
      "13      -0.182317         -0.745990 -0.168004 -0.293949  -0.055221  0.168182   \n",
      "14      -0.182317         -0.763579 -0.168004 -0.293949  -0.055221 -0.645333   \n",
      "15      -0.149637         -0.221849 -0.108981 -0.170457  -0.042218  0.168182   \n",
      "16      -0.149637          0.096505 -0.108981 -0.170457  -0.042218 -0.645333   \n",
      "17      -0.178832         -0.607040 -0.166003 -0.293949  -0.055221 -0.645333   \n",
      "18      -0.123926         -0.209537  0.035076 -0.014467  -0.055221  1.795214   \n",
      "19      -0.123926         -0.578898  0.035076 -0.014467  -0.055221  1.795214   \n",
      "20      -0.123926          0.244250  0.035076 -0.014467  -0.055221  0.981698   \n",
      "21      -0.178290         -0.482161 -0.165003 -0.292649  -0.055221  0.168182   \n",
      "22      -0.178290         -0.763579 -0.165003 -0.292649  -0.055221  0.168182   \n",
      "23      -0.177903          0.527427 -0.161001 -0.275750  -0.055221  0.168182   \n",
      "24      -0.173411          0.604817 -0.162002 -0.271851  -0.055221 -0.645333   \n",
      "25      -0.173411          0.409583 -0.162002 -0.271851  -0.055221  0.168182   \n",
      "26      -0.182317          0.601299 -0.169004 -0.293949  -0.055221 -0.645333   \n",
      "27      -0.177284          0.362093 -0.157500 -0.288750  -0.055221  0.981698   \n",
      "28       2.843124          1.385752  2.625101  3.143031   1.281506 -0.645333   \n",
      "29       2.843124          1.762148  2.625101  3.143031   1.281506 -1.458849   \n",
      "...           ...               ...       ...       ...        ...       ...   \n",
      "79966   -0.167913          1.088504 -0.147996 -0.277050  -0.055221 -0.645333   \n",
      "79967   -0.174496         -0.763579 -0.168004 -0.293949  -0.055221  1.795214   \n",
      "79968   -0.132135          0.043739 -0.087972 -0.140559  -0.050019  0.168182   \n",
      "79969   -0.127953         -0.517338 -0.109981 -0.136659   0.158031  1.795214   \n",
      "79970   -0.181001          0.172136 -0.165503 -0.247152  -0.055221  1.795214   \n",
      "79971   -0.158310          0.539739 -0.145995 -0.145759  -0.055221  0.981698   \n",
      "79972   -0.060423         -0.756543 -0.125487 -0.137959  -0.029214  0.168182   \n",
      "79973   -0.181543         -0.524374 -0.170505 -0.293949  -0.055221 -1.458849   \n",
      "79974   -0.118350          1.405099  0.117609  0.291013  -0.005809  0.168182   \n",
      "79975   -0.083656          0.235455 -0.059961  0.281914  -0.031815 -1.458849   \n",
      "79976   -0.182317         -0.483920 -0.168004 -0.293949  -0.055221 -0.645333   \n",
      "79977   -0.179375          0.242491 -0.166503 -0.287450  -0.055221 -1.458849   \n",
      "79978    1.683424         -0.663324  0.516767  0.652390   0.085214 -0.645333   \n",
      "79979   -0.173102         -0.202502 -0.166503 -0.286150  -0.055221 -0.645333   \n",
      "79980   -0.178600          0.905582 -0.164503 -0.293949  -0.055221  0.168182   \n",
      "79981   -0.151340         -0.642217 -0.109981 -0.205555  -0.042218  1.795214   \n",
      "79982   -0.100228          1.204589 -0.119985 -0.223754  -0.039617  0.981698   \n",
      "79983   -0.169694         -0.760061 -0.164503 -0.291349  -0.055221  0.981698   \n",
      "79984   -0.177051         -0.763579 -0.149497 -0.287450  -0.055221 -1.458849   \n",
      "79985   -0.029601         -0.543721 -0.032450 -0.089862  -0.026614  1.795214   \n",
      "79986   -0.106191         -0.049481 -0.108480  0.714786  -0.039617  0.168182   \n",
      "79987   -0.157691          0.866887 -0.147496 -0.167857  -0.052620 -0.645333   \n",
      "79988   -0.176819         -0.763579 -0.163502 -0.293949  -0.055221  0.168182   \n",
      "79989   -0.086908          0.071881 -0.126988 -0.223754  -0.050019  1.795214   \n",
      "79990   -0.153276          0.094746 -0.136992 -0.270551  -0.055221  0.168182   \n",
      "79991   -0.150489         -0.510303 -0.132990 -0.284850  -0.055221  1.795214   \n",
      "79992   -0.156606         -0.763579 -0.163502 -0.290050  -0.055221  1.795214   \n",
      "79993   -0.136007         -0.524374 -0.119985  1.821015  -0.047419  1.795214   \n",
      "79994   -0.153586          0.872164 -0.126487 -0.274451   0.082613  0.981698   \n",
      "79995    0.408103          0.479937  0.040579  3.609701  -0.031815 -0.645333   \n",
      "\n",
      "       lowerUpperRatio  sentenceCount  firstLineLength  codeLength    ...     \\\n",
      "0            -0.196705       0.085283        -0.278687   -0.261458    ...      \n",
      "1            -0.554385       0.614348        -0.225007   -0.294237    ...      \n",
      "2            -0.463417      -0.179250        -0.189221   -0.294237    ...      \n",
      "3            -0.708266      -0.046983         0.982785    0.247427    ...      \n",
      "4             0.045066       0.614348        -0.582872    0.022075    ...      \n",
      "5             2.451040      -0.443782        -0.072915   -0.294237    ...      \n",
      "6             0.657528      -0.179250         0.866479   -0.294237    ...      \n",
      "7            -0.174735      -0.311516        -0.511299    0.036006    ...      \n",
      "8             0.062296       0.349816        -0.743911   -0.294237    ...      \n",
      "9            -0.380970      -0.311516        -0.332367   -0.294237    ...      \n",
      "10           -0.451533       1.011147         1.108037   -0.294237    ...      \n",
      "11            2.082510      -0.179250        -0.412886    0.382637    ...      \n",
      "12           -0.423493      -0.179250        -0.448672   -0.294237    ...      \n",
      "13            0.387273      -0.576049        -0.421833   -0.157387    ...      \n",
      "14           -0.902582      -0.179250        -0.770750    0.022075    ...      \n",
      "15            0.674726      -0.179250        -0.090808    0.107299    ...      \n",
      "16            1.234891      -0.443782         0.213377   -0.294237    ...      \n",
      "17           -0.566298      -0.046983        -0.153434   -0.141817    ...      \n",
      "18           -0.478772      -0.179250        -0.180274   -0.294237    ...      \n",
      "19           -0.445605      -0.179250         0.660707   -0.294237    ...      \n",
      "20            0.392537      -0.311516         0.437042    1.619203    ...      \n",
      "21           -0.651114      -0.311516        -0.466566   -0.294237    ...      \n",
      "22            0.636030      -0.179250        -0.591818   -0.294237    ...      \n",
      "23           -0.747273       0.085283        -0.260794   -0.294237    ...      \n",
      "24           -0.113928       0.349816        -0.394993    0.855466    ...      \n",
      "25           -0.128669      -0.179250         0.490721   -0.019718    ...      \n",
      "26           -0.465260      -0.443782         0.544401   -0.094288    ...      \n",
      "27           -0.008897      -0.311516        -0.099755   -0.294237    ...      \n",
      "28           -0.602493      -0.179250        -0.377100   -0.282764    ...      \n",
      "29           -0.722923      -0.311516        -0.439726   -0.294237    ...      \n",
      "...                ...            ...              ...         ...    ...      \n",
      "79966         0.110875      -0.179250        -0.242900   -0.261458    ...      \n",
      "79967         0.163851       0.349816         0.043391   -0.209013    ...      \n",
      "79968        -0.828876       0.085283         0.276003   -0.124608    ...      \n",
      "79969         0.679929       1.011147        -0.412886   -0.294237    ...      \n",
      "79970         0.943577      -0.708315        -0.896003   -0.178693    ...      \n",
      "79971        -0.061983       3.391940         0.329682    1.059512    ...      \n",
      "79972         0.396486      -0.046983         0.114964   -0.294237    ...      \n",
      "79973        -0.073390      -0.576049         0.624920    0.079437    ...      \n",
      "79974        -0.954790       0.482082        -0.386046   -0.294237    ...      \n",
      "79975         0.940067      -0.443782         0.571241   -0.294237    ...      \n",
      "79976         0.002364      -0.311516        -0.225007   -0.186068    ...      \n",
      "79977        -1.018810      -0.576049        -0.063968   -0.278667    ...      \n",
      "79978         0.387273      -0.311516        -0.180274    0.007325    ...      \n",
      "79979        -0.283452      -0.443782        -0.162381   -0.045121    ...      \n",
      "79980         0.460979      -0.179250         0.007605   -0.226222    ...      \n",
      "79981        -0.507518       1.143413        -0.207114   -0.294237    ...      \n",
      "79982        -0.162451      -0.311516         0.392309   -0.218846    ...      \n",
      "79983         0.002620       0.878881        -0.636551   -0.294237    ...      \n",
      "79984         0.239861      -0.443782         0.204430   -0.294237    ...      \n",
      "79985        -0.626546       1.804745        -0.314473   -0.239333    ...      \n",
      "79986         0.090401       1.937011         2.718427   -0.274570    ...      \n",
      "79987         0.310732       0.217549         0.472828    0.042561    ...      \n",
      "79988        -0.504024      -0.046983         0.204430    0.221204    ...      \n",
      "79989        -0.553707       0.614348        -0.627605   -0.254903    ...      \n",
      "79990         0.107190      -0.443782         0.025498   -0.294237    ...      \n",
      "79991        -0.802772      -0.046983         0.607027   -0.294237    ...      \n",
      "79992        -0.757217      -0.046983        -0.314473   -0.294237    ...      \n",
      "79993        -0.301595       0.085283         1.850606   -0.294237    ...      \n",
      "79994        -0.062334       0.482082        -0.233954   -0.087733    ...      \n",
      "79995        -0.792023      -0.311516        -0.412886   -0.222124    ...      \n",
      "\n",
      "       bodyWordCount  punctuationCount  gunningFog  fleschReadingEase  \\\n",
      "0           1.245922         -0.106980    3.692609          -2.927187   \n",
      "1           0.472585         -0.266603    2.319556          -1.723746   \n",
      "2          -0.455419         -0.276901   -0.317204           0.308837   \n",
      "3           0.205433         -0.132726    0.404181          -0.414033   \n",
      "4           0.346039          0.294650   -0.077965           0.063629   \n",
      "5          -0.160145         -0.307796   -0.009716          -0.118711   \n",
      "6          -0.174205         -0.323243   -0.375179           0.271697   \n",
      "7          -0.019538          0.006300    0.178152           0.146185   \n",
      "8           0.107008         -0.287199    1.355998          -1.133100   \n",
      "9          -0.202327         -0.328392   -0.028063           0.104571   \n",
      "10          1.274043          0.129879    0.033581          -0.497261   \n",
      "11         -0.849117          0.732325   -0.238681           0.221806   \n",
      "12         -0.750692         -0.333541   -0.354631           0.441508   \n",
      "13         -1.003784         -0.168770   -0.967404          -0.096114   \n",
      "14         -0.933481          0.186519   -0.961533           1.115158   \n",
      "15         -0.300751          0.186519   -0.546902           0.404369   \n",
      "16         -0.736632         -0.348989   -0.303260           0.346647   \n",
      "17         -0.019538         -0.076086   -0.273906           0.524735   \n",
      "18         -0.497600         -0.338690   -0.337018           0.319352   \n",
      "19         -0.117963         -0.318094   -0.072094           0.225609   \n",
      "20         -0.202327          0.927991   -0.073562          -0.084704   \n",
      "21         -0.385115         -0.312945   -0.072094           0.252233   \n",
      "22         -0.328873         -0.297497    0.418858          -0.217152   \n",
      "23         -0.624146         -0.282050    0.276489          -0.088507   \n",
      "24         -0.385115          0.840456    0.973656          -0.716740   \n",
      "25         -0.638207          0.057791   -0.139610           0.449115   \n",
      "26         -0.075781         -0.055489   -0.447097           0.470817   \n",
      "27         -0.244509         -0.307796   -0.043474           0.138578   \n",
      "28         -0.385115         -0.338690    0.521598          -0.527464   \n",
      "29         -0.483540         -0.343839   -0.321607           0.331657   \n",
      "...              ...               ...         ...                ...   \n",
      "79966      -0.441358         -0.333541   -0.359034          -0.081795   \n",
      "79967       0.767859         -0.168770    0.137056          -0.172629   \n",
      "79968       0.078887         -0.045191   -0.517548           0.645773   \n",
      "79969       1.878652         -0.276901   -0.572587           0.591183   \n",
      "79970       2.666049         -0.271752    6.691896          -5.576592   \n",
      "79971       1.063133          0.613895    1.047042          -0.538875   \n",
      "79972      -0.075781         -0.328392   -0.248955           0.092266   \n",
      "79973      -0.286691          0.104133    0.710201          -0.497261   \n",
      "79974       0.599131         -0.246006    0.654427          -0.542678   \n",
      "79975      -0.469479         -0.338690    0.355746          -0.769763   \n",
      "79976      -0.174205         -0.137875   -0.523419           0.343067   \n",
      "79977      -0.863178         -0.354138   -0.897687           0.267223   \n",
      "79978      -0.413237         -0.086384    0.779183          -0.671322   \n",
      "79979      -0.666328          0.104133   -0.232810          -0.149138   \n",
      "79980      -0.146084         -0.312945   -0.514612           0.326959   \n",
      "79981       0.528828         -0.276901   -0.736238           0.547332   \n",
      "79982       0.036705         -0.209963    1.231975          -1.019446   \n",
      "79983       0.655374         -0.199664   -0.553507           0.464105   \n",
      "79984      -0.511661         -0.333541   -0.460307           0.354254   \n",
      "79985       1.358407         -0.158471   -0.484524           0.260511   \n",
      "79986       2.778534          0.155624    0.962648          -1.087012   \n",
      "79987       0.346039          0.042344    0.380697          -0.527464   \n",
      "79988       0.683495          0.171072    0.594251          -0.364813   \n",
      "79989       0.697556         -0.266603   -0.311333           0.256708   \n",
      "79990      -0.596025         -0.312945   -0.391324           0.043941   \n",
      "79991      -0.356994         -0.318094   -0.124199           0.229413   \n",
      "79992      -0.399176         -0.338690   -0.552039           0.456498   \n",
      "79993       0.950648         -0.235708    1.167396          -1.015866   \n",
      "79994       2.455139         -0.143024    1.321506          -0.891696   \n",
      "79995      -0.652268         -0.338690   -0.907227           0.846458   \n",
      "\n",
      "       daleChallReadabilityScore  smogIndex  colemanLiauIndex  \\\n",
      "0                       2.752358  -0.741272         -0.252183   \n",
      "1                       1.995954  -0.741272          0.108811   \n",
      "2                      -0.074932  -0.741272          0.610844   \n",
      "3                       0.538493  -0.741272          0.955047   \n",
      "4                      -0.024198   1.331136          0.778747   \n",
      "5                       0.160291  -0.741272         -0.322702   \n",
      "6                       0.114169   1.296306         -0.403296   \n",
      "7                       0.377065  -0.741272          0.202837   \n",
      "8                       0.902858  -0.741272         -0.174947   \n",
      "9                       0.192576  -0.741272         -0.205169   \n",
      "10                      0.266372   2.079990          1.158210   \n",
      "11                      0.340167  -0.741272          0.377457   \n",
      "12                     -0.291707  -0.741272         -0.673621   \n",
      "13                     -0.914356  -0.741272          1.499055   \n",
      "14                     -1.149580  -0.741272          0.140713   \n",
      "15                     -0.439298   1.087324         -0.206849   \n",
      "16                      0.455473  -0.741272          0.004710   \n",
      "17                     -0.743704  -0.741272         -0.838167   \n",
      "18                      0.312494   0.791265         -0.326060   \n",
      "19                     -0.070320  -0.741272         -0.097711   \n",
      "20                      0.077271  -0.741272          0.115527   \n",
      "21                      0.404738  -0.741272          0.377457   \n",
      "22                      0.174127  -0.741272         -0.779401   \n",
      "23                      1.769955  -0.741272          0.716623   \n",
      "24                      1.700772  -0.741272          3.174736   \n",
      "25                     -0.162565  -0.741272          0.476520   \n",
      "26                     -0.457747   0.948002         -0.148082   \n",
      "27                      0.224862  -0.741272         -0.468779   \n",
      "28                      0.580003  -0.741272         -0.195095   \n",
      "29                     -0.038035  -0.741272         -0.040624   \n",
      "...                          ...        ...               ...   \n",
      "79966                   0.211025   1.209230         -0.188379   \n",
      "79967                  -0.010361   1.714271         -0.097711   \n",
      "79968                  -0.250197   1.139569         -0.413370   \n",
      "79969                  -0.503869   0.843511         -0.734067   \n",
      "79970                   5.196835  -0.741272          0.293505   \n",
      "79971                   0.607676  -0.741272          0.506743   \n",
      "79972                   0.031149   1.209230          0.066835   \n",
      "79973                   0.699920  -0.741272         -0.097711   \n",
      "79974                   0.460085  -0.741272          0.409359   \n",
      "79975                   0.469309  -0.741272          0.427828   \n",
      "79976                  -0.513093   1.209230          0.318691   \n",
      "79977                  -1.191090  -0.741272         -0.559447   \n",
      "79978                   1.317958  -0.741272          1.383202   \n",
      "79979                  -0.042647  -0.741272          1.594760   \n",
      "79980                  -0.526930   1.087324         -0.196774   \n",
      "79981                  -0.591501   0.791265         -0.912045   \n",
      "79982                   0.842899  -0.741272          0.011427   \n",
      "79983                  -0.739092   1.139569         -0.245466   \n",
      "79984                  -0.333217  -0.741272         -0.255541   \n",
      "79985                  -0.181014   1.348552         -0.295838   \n",
      "79986                   0.861348   2.706937          0.478199   \n",
      "79987                   0.229474  -0.741272          1.042357   \n",
      "79988                   0.284821  -0.741272         -0.000327   \n",
      "79989                  -0.176401   1.139569         -0.468779   \n",
      "79990                  -0.010361  -0.741272         -0.012080   \n",
      "79991                   0.224862  -0.741272          0.416075   \n",
      "79992                  -0.333217   0.791265         -0.520829   \n",
      "79993                   1.110408  -0.741272         -0.388185   \n",
      "79994                   0.930531   1.958083         -0.134650   \n",
      "79995                  -1.191090  -0.741272         -0.812982   \n",
      "\n",
      "       userProfileFilled    numAns   numQues  \n",
      "0               0.423961  2.750534  1.287335  \n",
      "1              -1.383501 -0.161735 -0.358830  \n",
      "2               1.327692 -0.061829 -0.256437  \n",
      "3               2.231423  0.053063  0.192517  \n",
      "4               2.231423  0.053063  0.192517  \n",
      "5              -1.383501 -0.146749 -0.272190  \n",
      "6              -0.479770 -0.161735 -0.358830  \n",
      "7               0.423961 -0.161735 -0.350954  \n",
      "8               0.423961  1.481723 -0.177673  \n",
      "9               0.423961  1.481723 -0.177673  \n",
      "10              0.423961  1.481723 -0.177673  \n",
      "11             -0.479770 -0.151745  0.121630  \n",
      "12             -0.479770 -0.151745  0.121630  \n",
      "13             -0.479770 -0.161735 -0.335201  \n",
      "14             -0.479770 -0.161735 -0.335201  \n",
      "15              1.327692 -0.161735 -0.358830  \n",
      "16              1.327692 -0.161735 -0.358830  \n",
      "17              0.423961 -0.161735 -0.327324  \n",
      "18             -0.479770 -0.106787  1.547256  \n",
      "19             -0.479770 -0.106787  1.547256  \n",
      "20             -0.479770 -0.106787  1.547256  \n",
      "21              0.423961 -0.156740 -0.280066  \n",
      "22              0.423961 -0.156740 -0.280066  \n",
      "23              1.327692 -0.126768 -0.240684  \n",
      "24             -0.479770 -0.156740 -0.217055  \n",
      "25             -0.479770 -0.156740 -0.217055  \n",
      "26              1.327692 -0.151745 -0.350954  \n",
      "27              0.423961 -0.161735 -0.248561  \n",
      "28              2.231423 -0.161735 -0.358830  \n",
      "29              2.231423 -0.161735 -0.358830  \n",
      "...                  ...       ...       ...  \n",
      "79966          -1.383501 -0.161735 -0.311572  \n",
      "79967          -0.479770 -0.161735 -0.350954  \n",
      "79968           1.327692  0.113007 -0.106786  \n",
      "79969           1.327692  0.043073 -0.185549  \n",
      "79970           1.327692 -0.151745 -0.327324  \n",
      "79971          -0.479770 -0.151745 -0.035898  \n",
      "79972           1.327692  0.103016 -0.185549  \n",
      "79973          -0.479770 -0.161735 -0.319448  \n",
      "79974           1.327692 -0.031857  0.948650  \n",
      "79975           1.327692 -0.056834  0.011360  \n",
      "79976          -0.479770 -0.161735 -0.350954  \n",
      "79977           1.327692 -0.161735 -0.319448  \n",
      "79978           1.327692 -0.161735 -0.358830  \n",
      "79979          -1.383501 -0.136759 -0.335201  \n",
      "79980          -0.479770 -0.161735 -0.358830  \n",
      "79981           1.327692 -0.061829  0.042866  \n",
      "79982          -0.479770 -0.126768 -0.185549  \n",
      "79983          -0.479770 -0.116778 -0.350954  \n",
      "79984          -0.479770 -0.161735 -0.272190  \n",
      "79985          -0.479770  0.257871  0.460314  \n",
      "79986           0.423961 -0.116778 -0.272190  \n",
      "79987          -0.479770 -0.161735 -0.138291  \n",
      "79988          -0.479770 -0.161735 -0.232808  \n",
      "79989           0.423961 -0.001885 -0.004393  \n",
      "79990           0.423961 -0.136759 -0.067404  \n",
      "79991          -1.383501 -0.161735 -0.154044  \n",
      "79992          -1.383501 -0.141754 -0.248561  \n",
      "79993           2.231423  0.098021 -0.169797  \n",
      "79994           0.423961 -0.126768  0.098000  \n",
      "79995           0.423961 -0.001885  0.302787  \n",
      "\n",
      "[79996 rows x 27 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Reputation  experienceInTime     Views   Upvotes  Downvotes  tagCount  \\\n",
      "19432   -0.175115         -0.471608 -0.160501 -0.273151  -0.055221 -0.645333   \n",
      "4303    -0.110683         -0.293963 -0.058461 -0.262751  -0.055221 -0.645333   \n",
      "53957   -0.064992         -0.190190 -0.122986 -0.145759  -0.052620 -1.458849   \n",
      "41130    0.387349          1.860645  0.435735  0.907174  -0.055221  0.168182   \n",
      "31209   -0.162879         -0.578898 -0.138492 -0.219854  -0.055221  1.795214   \n",
      "68086   -0.075369          0.221384 -0.100477 -0.085963  -0.050019  1.795214   \n",
      "590     -0.176045          0.138718 -0.160501 -0.282250  -0.055221 -1.458849   \n",
      "49525   -0.182162         -0.753026 -0.168504 -0.293949  -0.055221  0.168182   \n",
      "8348    -0.179994         -0.756543 -0.169004 -0.290050  -0.055221 -1.458849   \n",
      "47990   -0.180149          1.934517 -0.166503 -0.277050  -0.055221 -0.645333   \n",
      "68933   -0.179452          0.764873 -0.162002 -0.288750  -0.055221  0.981698   \n",
      "9760    -0.181930         -0.760061 -0.169505 -0.293949  -0.055221  0.981698   \n",
      "17158   -0.182317         -0.763579 -0.164002 -0.293949  -0.055221 -0.645333   \n",
      "68613   -0.130896          1.208107 -0.045956 -0.126260  -0.003208  0.981698   \n",
      "44059   -0.182162         -0.763579 -0.170005 -0.293949  -0.055221  0.168182   \n",
      "63907   -0.137556          2.683793 -0.127988  0.612093  -0.026614 -0.645333   \n",
      "27710   -0.181078         -0.761820 -0.169004 -0.292649  -0.055221 -1.458849   \n",
      "59386   -0.151650          0.933724 -0.130989  0.072627  -0.011010  1.795214   \n",
      "42151   -0.181775         -0.763579 -0.168504 -0.293949  -0.055221  1.795214   \n",
      "9116    -0.177206         -0.763579 -0.166003 -0.284850  -0.055221 -0.645333   \n",
      "41829   -0.182162         -0.756543 -0.170505 -0.293949  -0.055221 -0.645333   \n",
      "2476     0.323459         -0.147977  0.893916  2.500872   0.043603 -0.645333   \n",
      "42873   -0.181775         -0.763579 -0.169505 -0.293949  -0.055221  0.981698   \n",
      "14250   -0.182162          0.309328 -0.166003 -0.293949  -0.055221 -1.458849   \n",
      "21547   -0.098292          1.383993 -0.120985 -0.260151  -0.037016  0.981698   \n",
      "77111   -0.181775          0.019115 -0.167504 -0.293949  -0.055221 -0.645333   \n",
      "6232    -0.182317         -0.763579 -0.170505 -0.293949  -0.055221  0.981698   \n",
      "1298    -0.174031          1.610886 -0.131990 -0.245852  -0.055221  1.795214   \n",
      "12655   -0.182085         -0.763579 -0.169004 -0.293949  -0.055221  0.168182   \n",
      "15970   -0.118660         -0.102246 -0.153998 -0.227654  -0.052620  0.168182   \n",
      "...           ...               ...       ...       ...        ...       ...   \n",
      "36911   -0.181775         -0.763579 -0.170505 -0.293949  -0.055221 -1.458849   \n",
      "59276   -0.181001         -0.753026 -0.163002 -0.291349  -0.055221 -1.458849   \n",
      "49258    0.152389         -0.300998  0.005065  0.644591   0.428498  1.795214   \n",
      "75424   -0.060733          2.075226 -0.020946  0.531498   0.030600  0.981698   \n",
      "33260   -0.179762         -0.635182 -0.165003 -0.291349  -0.055221 -1.458849   \n",
      "28485   -0.180846          0.154548 -0.160001 -0.287450  -0.055221  0.168182   \n",
      "1051     0.329190         -0.086417  0.062087  1.601329  -0.044818 -0.645333   \n",
      "26982   -0.181930         -0.483920 -0.168504 -0.293949  -0.055221  0.168182   \n",
      "74738    1.569507          0.376164  0.329193  0.141523   0.106019 -0.645333   \n",
      "56775   -0.180923         -0.024856 -0.164503 -0.290050  -0.055221  0.981698   \n",
      "72553    0.021434          0.284703  0.181634 -0.070364   0.186638  0.168182   \n",
      "53737   -0.181156         -0.665083 -0.169505 -0.293949  -0.055221  0.168182   \n",
      "65722   -0.091400          1.092022 -0.093474 -0.292649  -0.055221 -0.645333   \n",
      "35288   -0.173566          1.772702 -0.139493 -0.275750  -0.055221 -0.645333   \n",
      "19100    0.065498          0.085952 -0.010442 -0.078163   0.093016 -0.645333   \n",
      "32685   -0.181698          0.678689 -0.169505 -0.293949  -0.055221 -0.645333   \n",
      "76199    0.214575          3.167480  0.078594  0.454803  -0.050019 -0.645333   \n",
      "15697   -0.181930         -0.763579 -0.166003 -0.293949  -0.055221  0.168182   \n",
      "68521   -0.113781          0.835228 -0.091974 -0.212055  -0.031815 -1.458849   \n",
      "24565   -0.182085          2.908927 -0.169505 -0.293949  -0.055221  0.981698   \n",
      "37850   -0.182317         -0.763579 -0.170505 -0.293949  -0.055221 -1.458849   \n",
      "2229    -0.119512         -0.406530  0.060587 -0.113261  -0.055221  0.168182   \n",
      "3692    -0.141350          0.376164 -0.112982 -0.240653  -0.052620  1.795214   \n",
      "59290    4.700192         -0.156771  1.031471  0.985169   0.080012 -0.645333   \n",
      "76229    0.092448         -0.323863  0.061087  1.511635   0.046204  0.168182   \n",
      "8396    -0.182162         -0.763579 -0.169004 -0.293949  -0.055221  0.168182   \n",
      "19334   -0.180381         -0.257026 -0.165003 -0.270551  -0.055221 -1.458849   \n",
      "66846    3.024262         -0.332657  0.703841  4.159566  -0.029214  0.168182   \n",
      "62378    0.494839          0.119370  0.138117  0.001132  -0.005809  0.168182   \n",
      "11935   -0.182317         -0.760061 -0.169505 -0.293949  -0.055221 -1.458849   \n",
      "\n",
      "       lowerUpperRatio  sentenceCount  firstLineLength  codeLength  \\\n",
      "19432         0.014648      -0.046983        -0.529192   -0.294237   \n",
      "4303         -0.281346      -0.311516        -0.412886   -0.294237   \n",
      "53957        -0.551601       0.482082        -0.511299   -0.294237   \n",
      "41130        -0.499831      -0.046983        -0.233954   -0.294237   \n",
      "31209        -0.720774      -0.311516         1.179610    2.226423   \n",
      "68086        -0.177039      -0.046983         0.982785   -0.294237   \n",
      "590           0.620675      -0.576049        -0.072915   -0.221305   \n",
      "49525         0.141586       0.085283         0.624920   -0.284403   \n",
      "8348          0.311399       0.217549         0.088124    0.026172   \n",
      "47990         1.468293      -0.179250        -0.332367   -0.294237   \n",
      "68933        -0.859148      -0.046983         0.123910   -0.253264   \n",
      "9760         -0.589331      -0.179250        -0.072915   -0.294237   \n",
      "17158         0.508361      -0.046983         0.293896   -0.294237   \n",
      "68613        -0.554690       3.127408         0.007605   -0.294237   \n",
      "44059         0.178439       0.085283        -0.663391    0.096646   \n",
      "63907         1.529715      -0.311516        -0.046075   -0.294237   \n",
      "27710        -0.597228      -0.311516         1.600101   -0.002509   \n",
      "59386        -0.361425      -0.046983         0.445988   -0.294237   \n",
      "42151         0.221434       0.085283        -0.609711   -0.294237   \n",
      "9116         -0.801236      -0.311516         0.258110    2.528804   \n",
      "41829        -0.249757      -0.046983        -0.421833   -0.294237   \n",
      "2476          0.221434       1.011147        -0.135541   -0.294237   \n",
      "42873         0.065647       0.349816        -0.690231   -0.294237   \n",
      "14250         0.129302      -0.179250        -0.153434   -0.294237   \n",
      "21547        -0.522996      -0.179250        -0.135541   -0.294237   \n",
      "77111        -0.521768      -0.443782         0.499668   -0.294237   \n",
      "6232         -0.804568       1.143413        -0.609711    1.599536   \n",
      "1298         -0.039169      -0.311516         0.857532   -0.294237   \n",
      "12655        -0.312934      -0.708315        -0.896003    0.128605   \n",
      "15970        -0.588454      -0.179250         0.132857   -0.294237   \n",
      "...                ...            ...              ...         ...   \n",
      "36911        -0.755170      -0.443782        -0.564978   -0.283584   \n",
      "59276        -0.417351      -0.311516        -0.135541   -0.294237   \n",
      "49258        -0.228909       0.217549         0.454935   -0.239333   \n",
      "75424         0.483500       0.349816         0.276003   -0.255722   \n",
      "33260        -0.325218      -0.179250        -0.296580   -0.230319   \n",
      "28485         0.748432      -0.443782         0.580187    0.155647   \n",
      "1051         -0.073390      -0.443782        -0.323420   -0.162304   \n",
      "26982         0.027956      -0.576049         0.750173   -0.014801   \n",
      "74738        -0.423493      -0.443782         0.347576   -0.294237   \n",
      "56775        -0.220802       1.143413        -0.359206   -0.294237   \n",
      "72553        -0.501011       0.217549         0.052338   -0.289320   \n",
      "53737         0.221434       0.349816        -0.529192   -0.294237   \n",
      "65722        -0.131504       0.349816        -0.180274   -0.145095   \n",
      "35288         0.282856      -0.708315        -0.896003    0.916926   \n",
      "19100         0.589964       0.085283        -0.242900   -0.294237   \n",
      "32685         0.608391      -0.443782        -0.189221    0.019617   \n",
      "76199        -0.369442       0.878881        -0.162381   -0.294237   \n",
      "15697        -0.453180       0.614348        -0.171328   -0.294237   \n",
      "68521        -0.550803      -0.046983        -0.734964   -0.177054   \n",
      "24565         0.009530      -0.311516         0.437042   -0.294237   \n",
      "37850         0.545214       0.349816        -0.412886   -0.294237   \n",
      "2229         -1.160553      -0.576049        -0.314473   -0.228680   \n",
      "3692         -0.723342       0.614348        -0.046075    3.226984   \n",
      "59290        -0.541949       0.349816        -0.547085   -0.294237   \n",
      "76229         0.203008      -0.179250         0.517561   -0.237694   \n",
      "8396         -0.852873       0.085283        -0.046075   -0.294237   \n",
      "19334        -0.535992      -0.311516         0.562294   -0.176234   \n",
      "66846        -0.615128      -0.046983        -0.225007   -0.294237   \n",
      "62378         0.534685       0.085283        -0.144488   -0.294237   \n",
      "11935        -0.184826       2.069277        -0.887056   -0.159845   \n",
      "\n",
      "             ...          sentencesStartWithICount  shortWordCount  \\\n",
      "19432        ...                          0.747676       -0.576192   \n",
      "4303         ...                          0.126952       -0.459346   \n",
      "53957        ...                          0.747676       -0.108808   \n",
      "41130        ...                          0.747676       -0.225654   \n",
      "31209        ...                         -0.493772       -0.342500   \n",
      "68086        ...                          0.126952        0.679902   \n",
      "590          ...                         -1.114496       -0.546980   \n",
      "49525        ...                          0.126952        0.008038   \n",
      "8348         ...                          0.747676        1.497823   \n",
      "47990        ...                          0.126952       -0.400923   \n",
      "68933        ...                          0.747676       -0.284077   \n",
      "9760         ...                          0.126952       -0.605403   \n",
      "17158        ...                          0.126952       -0.050385   \n",
      "68613        ...                          3.230573        7.544600   \n",
      "44059        ...                         -0.493772        0.270941   \n",
      "63907        ...                         -1.114496       -0.400923   \n",
      "27710        ...                         -1.114496       -0.167231   \n",
      "59386        ...                          0.126952        0.329364   \n",
      "42151        ...                          0.747676       -0.605403   \n",
      "9116         ...                         -0.493772       -0.751461   \n",
      "41829        ...                          1.368401        0.475422   \n",
      "2476         ...                          3.230573        1.322555   \n",
      "42873        ...                          1.989125        0.738325   \n",
      "14250        ...                         -1.114496       -0.926729   \n",
      "21547        ...                          0.126952       -0.517769   \n",
      "77111        ...                          0.126952       -0.576192   \n",
      "6232         ...                          1.368401        0.796748   \n",
      "1298         ...                         -1.114496       -0.400923   \n",
      "12655        ...                         -1.114496       -1.043575   \n",
      "15970        ...                          0.126952        0.037249   \n",
      "...          ...                               ...             ...   \n",
      "36911        ...                         -0.493772       -0.926729   \n",
      "59276        ...                          0.126952       -0.663826   \n",
      "49258        ...                          0.747676        0.387787   \n",
      "75424        ...                          0.126952        1.351766   \n",
      "33260        ...                         -1.114496       -0.809884   \n",
      "28485        ...                         -0.493772       -0.225654   \n",
      "1051         ...                         -0.493772       -0.955941   \n",
      "26982        ...                         -1.114496       -0.634615   \n",
      "74738        ...                         -0.493772       -0.634615   \n",
      "56775        ...                          3.230573        2.052842   \n",
      "72553        ...                          0.747676        0.183307   \n",
      "53737        ...                          0.126952       -0.342500   \n",
      "65722        ...                         -0.493772        0.621479   \n",
      "35288        ...                         -1.114496       -0.313288   \n",
      "19100        ...                          0.126952        0.270941   \n",
      "32685        ...                         -0.493772       -0.605403   \n",
      "76199        ...                         -0.493772       -0.254865   \n",
      "15697        ...                          1.989125        0.972017   \n",
      "68521        ...                          0.747676       -0.546980   \n",
      "24565        ...                         -0.493772       -0.400923   \n",
      "37850        ...                          0.126952       -0.225654   \n",
      "2229         ...                         -1.114496       -0.868306   \n",
      "3692         ...                          0.126952       -0.430134   \n",
      "59290        ...                          0.747676       -0.400923   \n",
      "76229        ...                          0.747676        1.118074   \n",
      "8396         ...                          0.126952        0.095672   \n",
      "19334        ...                          0.747676        0.329364   \n",
      "66846        ...                         -0.493772        0.270941   \n",
      "62378        ...                          0.126952       -0.021174   \n",
      "11935        ...                          0.747676        0.825959   \n",
      "\n",
      "       bodyWordCount  punctuationCount  gunningFog  fleschReadingEase  \\\n",
      "19432      -0.286691         -0.312945   -0.537362           0.317115   \n",
      "4303       -0.455419         -0.348989   -0.127134           0.532342   \n",
      "53957      -0.146084         -0.194515   -0.383251           0.130972   \n",
      "41130      -0.286691         -0.323243    0.560493          -0.686536   \n",
      "31209      -0.399176          2.313102   -0.590200           0.641970   \n",
      "68086       0.528828         -0.287199   -0.098513           0.146185   \n",
      "590        -0.567904         -0.282050   -0.702480           0.221806   \n",
      "49525       0.346039         -0.312945   -0.326010           0.441508   \n",
      "8348        1.302165          0.037195   -0.437557           0.473278   \n",
      "47990      -0.385115         -0.343839    0.239796          -0.148914   \n",
      "68933      -0.244509         -0.292348   -0.268769           0.195406   \n",
      "9760       -0.610086         -0.302647   -0.890349           0.198314   \n",
      "17158      -0.019538         -0.302647    0.065137          -0.043090   \n",
      "68613       7.784131          0.541808    1.029430          -1.118781   \n",
      "44059       0.064826          0.310098    0.109169           0.089358   \n",
      "63907      -0.441358         -0.343839    0.512792          -0.436630   \n",
      "27710      -0.314812         -0.091533    0.515728          -0.262569   \n",
      "59386       0.402282         -0.261454    1.846950          -1.375399   \n",
      "42151      -0.610086         -0.328392   -0.306930           0.433901   \n",
      "9116       -0.722571          1.901173   -0.424347           0.335460   \n",
      "41829       0.219493         -0.323243   -0.592401           0.491400   \n",
      "2476        1.414650         -0.168770   -0.500669           0.142382   \n",
      "42873       1.344346         -0.240857   -0.382517           0.274830   \n",
      "14250      -0.820996         -0.338690   -0.113925           0.176389   \n",
      "21547      -0.483540         -0.343839    0.208240           0.199209   \n",
      "77111      -0.596025         -0.354138   -0.232810           0.422491   \n",
      "6232        0.711617          1.504691   -0.852922           0.488268   \n",
      "1298       -0.385115         -0.338690    0.352077          -0.338189   \n",
      "12655      -1.045966          0.464571   -0.248221           1.486101   \n",
      "15970      -0.103902         -0.266603    0.051928          -0.152941   \n",
      "...              ...               ...         ...                ...   \n",
      "36911      -0.961602         -0.333541   -0.712754           0.592749   \n",
      "59276      -0.610086         -0.348989   -0.974009           0.955415   \n",
      "49258       0.908466         -0.148173   -0.054482           0.041032   \n",
      "75424       1.625560         -0.209963    0.074678          -0.024968   \n",
      "33260      -0.863178         -0.220261   -0.551305           0.433901   \n",
      "28485      -0.188266          0.165922    0.792393          -0.588095   \n",
      "1051       -0.919420         -0.343839   -0.739173           0.524735   \n",
      "26982      -0.666328         -0.148173   -0.171166           0.115982   \n",
      "74738      -0.722571         -0.333541   -0.537362           0.774641   \n",
      "56775       1.948955         -0.179068   -0.400864           0.292952   \n",
      "72553       0.374161         -0.292348    0.623605          -0.739560   \n",
      "53737      -0.202327         -0.261454    0.630944          -0.232141   \n",
      "65722       0.331979         -0.045191    0.291166          -0.114907   \n",
      "35288      -0.328873          0.387334    0.598654          -0.429023   \n",
      "19100       0.261675         -0.276901   -0.454436           0.018883   \n",
      "32685      -0.596025         -0.122428   -0.629094           0.801041   \n",
      "76199      -0.075781         -0.261454   -0.510209           0.422491   \n",
      "15697       1.006891         -0.235708    0.084952          -0.583620   \n",
      "68521      -0.441358         -0.168770    0.084218           0.131195   \n",
      "24565      -0.624146         -0.328392   -0.206391           0.237019   \n",
      "37850      -0.033599         -0.302647   -0.507274           0.492742   \n",
      "2229       -0.919420         -0.359287   -0.736238           0.358057   \n",
      "3692       -0.356994          2.632346    0.939899          -0.572881   \n",
      "59290      -0.314812         -0.307796   -0.599740           0.600356   \n",
      "76229       0.992830         -0.240857    1.218032          -1.027052   \n",
      "8396        0.374161         -0.256305    0.319787           0.074368   \n",
      "19334       0.191372         -0.019445    0.195765           0.176612   \n",
      "66846       0.275736         -0.246006    2.048762          -1.595102   \n",
      "62378       0.261675         -0.297497   -0.520483           0.096965   \n",
      "11935       0.894405         -0.065787   -0.604877           0.493637   \n",
      "\n",
      "       daleChallReadabilityScore  smogIndex  colemanLiauIndex  \\\n",
      "19432                  -0.190238   1.017663          0.179330   \n",
      "4303                   -2.454838  -0.741272         -0.633324   \n",
      "53957                  -0.217911   1.331136          0.971837   \n",
      "41130                   0.326331  -0.741272          0.184368   \n",
      "31209                  -0.854398  -0.741272         -0.322702   \n",
      "68086                  -0.305543   1.435628         -0.789475   \n",
      "590                    -0.808275  -0.741272         -0.675300   \n",
      "49525                  -0.222524   0.913172         -0.488927   \n",
      "8348                   -0.430073   0.791265         -0.742462   \n",
      "47990                  -0.121055  -0.741272          0.379136   \n",
      "68933                  -0.208687  -0.741272          0.115527   \n",
      "9760                   -0.909744   0.791265          0.118885   \n",
      "17158                   0.095720  -0.741272         -0.010401   \n",
      "68613                   0.958205   2.828843          0.449656   \n",
      "44059                   0.081883  -0.741272         -0.107785   \n",
      "63907                   0.759879  -0.741272          0.253208   \n",
      "27710                   0.317106  -0.741272          0.194442   \n",
      "59386                   1.170367  -0.741272         -0.183342   \n",
      "42151                   0.224862  -0.741272         -0.364678   \n",
      "9116                    0.132617  -0.741272          1.388239   \n",
      "41829                  -0.775990   1.017663         -0.937231   \n",
      "2476                   -0.480808   1.557534          0.066835   \n",
      "42873                  -0.314768   1.313721         -0.186700   \n",
      "14250                   0.552329  -0.741272          0.941614   \n",
      "21547                   0.151066  -0.741272         -0.262257   \n",
      "77111                   0.381677  -0.741272         -0.352925   \n",
      "6232                   -1.011213   0.512622         -0.188379   \n",
      "1298                    0.160291  -0.741272         -0.156477   \n",
      "12655                   1.018164  -0.741272          6.136561   \n",
      "15970                   0.238698  -0.741272          0.330444   \n",
      "...                          ...        ...               ...   \n",
      "36911                  -0.434686  -0.741272          1.015492   \n",
      "59276                  -1.117294  -0.201401         -1.244495   \n",
      "49258                  -0.014974   1.731686          0.535287   \n",
      "75424                   0.160291   1.522704         -0.205169   \n",
      "33260                  -0.383951  -0.741272          0.716623   \n",
      "28485                   0.704533  -0.741272         -0.205169   \n",
      "1051                   -0.646848  -0.741272          0.735092   \n",
      "26982                  -0.139504  -0.741272         -0.020475   \n",
      "74738                  -0.849785  -0.741272         -0.411691   \n",
      "56775                  -0.319380   1.400797         -0.284084   \n",
      "72553                   0.782940  -0.741272          0.632671   \n",
      "53737                   0.252535  -0.741272         -0.164873   \n",
      "65722                   0.081883  -0.741272         -0.262257   \n",
      "35288                   0.570778  -0.741272          0.301900   \n",
      "19100                  -0.222524   1.696855          0.377457   \n",
      "32685                  -0.600726  -0.741272         -0.383148   \n",
      "76199                  -0.305543   1.383382         -0.109464   \n",
      "15697                   0.206413   2.341218          0.212911   \n",
      "68521                  -0.305543  -0.741272          0.311975   \n",
      "24565                  -0.374727  -0.741272         -0.945626   \n",
      "37850                  -0.144116   0.425546         -0.413370   \n",
      "2229                   -0.591501  -0.741272         -0.979206   \n",
      "3692                    1.520895  -0.741272          2.103509   \n",
      "59290                  -0.549991   0.791265         -0.206849   \n",
      "76229                   1.207265  -0.741272         -0.144724   \n",
      "8396                    0.155678  -0.741272         -0.223639   \n",
      "19334                   0.072659  -0.741272         -0.243787   \n",
      "66846                   2.019015  -0.741272          0.254887   \n",
      "62378                  -0.633011   1.905838          0.115527   \n",
      "11935                  -0.799051   0.791265         -0.450309   \n",
      "\n",
      "       userProfileFilled  \n",
      "19432          -0.479770  \n",
      "4303           -1.383501  \n",
      "53957          -0.479770  \n",
      "41130           0.423961  \n",
      "31209          -0.479770  \n",
      "68086           0.423961  \n",
      "590            -0.479770  \n",
      "49525          -0.479770  \n",
      "8348            1.327692  \n",
      "47990           0.423961  \n",
      "68933           1.327692  \n",
      "9760           -0.479770  \n",
      "17158          -0.479770  \n",
      "68613          -0.479770  \n",
      "44059          -0.479770  \n",
      "63907           1.327692  \n",
      "27710          -0.479770  \n",
      "59386           2.231423  \n",
      "42151          -0.479770  \n",
      "9116           -0.479770  \n",
      "41829          -0.479770  \n",
      "2476            2.231423  \n",
      "42873          -0.479770  \n",
      "14250           1.327692  \n",
      "21547          -1.383501  \n",
      "77111           0.423961  \n",
      "6232            0.423961  \n",
      "1298            1.327692  \n",
      "12655          -1.383501  \n",
      "15970           1.327692  \n",
      "...                  ...  \n",
      "36911          -0.479770  \n",
      "59276          -0.479770  \n",
      "49258           1.327692  \n",
      "75424           1.327692  \n",
      "33260          -0.479770  \n",
      "28485           1.327692  \n",
      "1051            0.423961  \n",
      "26982          -0.479770  \n",
      "74738          -0.479770  \n",
      "56775           0.423961  \n",
      "72553           1.327692  \n",
      "53737          -0.479770  \n",
      "65722          -1.383501  \n",
      "35288           1.327692  \n",
      "19100           2.231423  \n",
      "32685          -0.479770  \n",
      "76199           2.231423  \n",
      "15697          -0.479770  \n",
      "68521          -1.383501  \n",
      "24565          -1.383501  \n",
      "37850          -0.479770  \n",
      "2229            1.327692  \n",
      "3692           -1.383501  \n",
      "59290          -1.383501  \n",
      "76229           2.231423  \n",
      "8396           -0.479770  \n",
      "19334          -0.479770  \n",
      "66846           2.231423  \n",
      "62378           0.423961  \n",
      "11935          -1.383501  \n",
      "\n",
      "[79996 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "#NEED TO RANDOMIZE DATAFRAME\n",
    "# X=X.sample(frac=1)\n",
    "# print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_score = cross_val_score(logreg,X,y,cv=k_fold,scoring='accuracy',n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.0793125   0.18701169  0.07100444  0.07025439  0.01675105]\n"
     ]
    }
   ],
   "source": [
    "print(val_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(range(1,6), val_score)\n",
    "plt.title('Cross Validation(5-fold) Logistic Regression')\n",
    "plt.xlabel('Split')\n",
    "plt.ylabel('Score')\n",
    "plt.xticks(range(1,6))\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1039 2364 1037 1476]\n",
      " [ 883 3296  995  942]\n",
      " [ 654 1939 1495 1827]\n",
      " [ 423  994  872 3763]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}