Logistic Regression Open or Closed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>PostId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Body</th>\n",
       "      <th>Title</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Reputation</th>\n",
       "      <th>experienceInTime</th>\n",
       "      <th>WebsiteUrl</th>\n",
       "      <th>...</th>\n",
       "      <th>locationFilled</th>\n",
       "      <th>aboutMeFilled</th>\n",
       "      <th>profileImageUrlFilled</th>\n",
       "      <th>tagWeightSum</th>\n",
       "      <th>gunningFog</th>\n",
       "      <th>fleschReadingEase</th>\n",
       "      <th>daleChallReadabilityScore</th>\n",
       "      <th>smogIndex</th>\n",
       "      <th>colemanLiauIndex</th>\n",
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       "      <td>&lt;p&gt;I tried to Google for this, but was (surpri...</td>\n",
       "      <td>Can anyone point me to (or post) an array of e...</td>\n",
       "      <td>&lt;javascript&gt;&lt;html&gt;&lt;arrays&gt;&lt;tags&gt;</td>\n",
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       "      <td>&lt;p&gt;I am at a decision fork where i have to pic...</td>\n",
       "      <td>RESTful API with either Tomcat or Node.js?</td>\n",
       "      <td>&lt;java&gt;&lt;node.js&gt;&lt;rest&gt;&lt;tomcat&gt;&lt;typescript&gt;</td>\n",
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       "      <td>102</td>\n",
       "      <td>&lt;p&gt;Does anyone know of a good tool to test Sch...</td>\n",
       "      <td>Tool for testing Schema.org markup</td>\n",
       "      <td>&lt;testing&gt;&lt;markup&gt;&lt;schema.org&gt;</td>\n",
       "      <td>1639</td>\n",
       "      <td>900</td>\n",
       "      <td>http://www.agendarecife.com</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-160.004636</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>25982771</td>\n",
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       "      <td>102</td>\n",
       "      <td>&lt;p&gt;The &lt;a href=\"http://www.postgresql.org/docs...</td>\n",
       "      <td>Inserting into simple Postgres views</td>\n",
       "      <td>&lt;sql&gt;&lt;postgresql&gt;</td>\n",
       "      <td>2669</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-188.646160</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",
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       "      <td>10142243</td>\n",
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       "      <td>102</td>\n",
       "      <td>&lt;p&gt;Which frameworks would you recommend for wr...</td>\n",
       "      <td>Frameworks for Unittests for Multithreading in...</td>\n",
       "      <td>&lt;java&gt;&lt;multithreading&gt;&lt;unit-testing&gt;&lt;concurrency&gt;</td>\n",
       "      <td>245</td>\n",
       "      <td>648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-274.409048</td>\n",
       "      <td>21.25</td>\n",
       "      <td>45.43</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   Unnamed: 0    PostId   UserId  Comment  \\\n",
       "0           0  21170255     5921      102   \n",
       "1           1  38290647  1601251      102   \n",
       "2           2   6496114    52924      102   \n",
       "3           3  25982771  1968462      102   \n",
       "4           4  10142243   383479      102   \n",
       "\n",
       "                                                Body  \\\n",
       "0  <p>I tried to Google for this, but was (surpri...   \n",
       "1  <p>I am at a decision fork where i have to pic...   \n",
       "2  <p>Does anyone know of a good tool to test Sch...   \n",
       "3  <p>The <a href=\"http://www.postgresql.org/docs...   \n",
       "4  <p>Which frameworks would you recommend for wr...   \n",
       "\n",
       "                                               Title  \\\n",
       "0  Can anyone point me to (or post) an array of e...   \n",
       "1         RESTful API with either Tomcat or Node.js?   \n",
       "2                 Tool for testing Schema.org markup   \n",
       "3               Inserting into simple Postgres views   \n",
       "4  Frameworks for Unittests for Multithreading in...   \n",
       "\n",
       "                                                Tags  Reputation  \\\n",
       "0                   <javascript><html><arrays><tags>       17527   \n",
       "1          <java><node.js><rest><tomcat><typescript>         151   \n",
       "2                      <testing><markup><schema.org>        1639   \n",
       "3                                  <sql><postgresql>        2669   \n",
       "4  <java><multithreading><unit-testing><concurrency>         245   \n",
       "\n",
       "   experienceInTime                         WebsiteUrl        ...          \\\n",
       "0              1953                                NaN        ...           \n",
       "1              1425                                NaN        ...           \n",
       "2               900        http://www.agendarecife.com        ...           \n",
       "3               620  https://katrinahoffert.github.io/        ...           \n",
       "4               648                                NaN        ...           \n",
       "\n",
       "  locationFilled aboutMeFilled  profileImageUrlFilled  tagWeightSum  \\\n",
       "0              1             1                      0   -207.161031   \n",
       "1              0             0                      0   -536.990588   \n",
       "2              1             1                      0   -160.004636   \n",
       "3              1             1                      1   -188.646160   \n",
       "4              0             0                      0   -274.409048   \n",
       "\n",
       "   gunningFog fleschReadingEase  daleChallReadabilityScore  smogIndex  \\\n",
       "0       71.70            -80.10                      13.96        0.0   \n",
       "1       52.99            -26.31                      12.32        0.0   \n",
       "2       17.06             64.54                       7.83        0.0   \n",
       "3       26.89             32.23                       9.16        0.0   \n",
       "4       21.25             45.43                       8.34        0.0   \n",
       "\n",
       "   colemanLiauIndex  userProfileFilled  \n",
       "0              9.83                  2  \n",
       "1             11.98                  0  \n",
       "2             14.97                  3  \n",
       "3             17.02                  4  \n",
       "4              9.41                  0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 35,
     "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(\"openCloseData8.csv\",index_col=False)\n",
    "openCloseData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0    PostId   UserId  Comment  \\\n",
      "0           0  21170255     5921      102   \n",
      "1           1  38290647  1601251      102   \n",
      "2           2   6496114    52924      102   \n",
      "3           3  25982771  1968462      102   \n",
      "4           4  10142243   383479      102   \n",
      "\n",
      "                                                Body  \\\n",
      "0  <p>I tried to Google for this, but was (surpri...   \n",
      "1  <p>I am at a decision fork where i have to pic...   \n",
      "2  <p>Does anyone know of a good tool to test Sch...   \n",
      "3  <p>The <a href=\"http://www.postgresql.org/docs...   \n",
      "4  <p>Which frameworks would you recommend for wr...   \n",
      "\n",
      "                                               Title  \\\n",
      "0  Can anyone point me to (or post) an array of e...   \n",
      "1         RESTful API with either Tomcat or Node.js?   \n",
      "2                 Tool for testing Schema.org markup   \n",
      "3               Inserting into simple Postgres views   \n",
      "4  Frameworks for Unittests for Multithreading in...   \n",
      "\n",
      "                                                Tags  Reputation  \\\n",
      "0                   <javascript><html><arrays><tags>       17527   \n",
      "1          <java><node.js><rest><tomcat><typescript>         151   \n",
      "2                      <testing><markup><schema.org>        1639   \n",
      "3                                  <sql><postgresql>        2669   \n",
      "4  <java><multithreading><unit-testing><concurrency>         245   \n",
      "\n",
      "   experienceInTime                         WebsiteUrl        ...          \\\n",
      "0              1953                                NaN        ...           \n",
      "1              1425                                NaN        ...           \n",
      "2               900        http://www.agendarecife.com        ...           \n",
      "3               620  https://katrinahoffert.github.io/        ...           \n",
      "4               648                                NaN        ...           \n",
      "\n",
      "  locationFilled aboutMeFilled  profileImageUrlFilled  tagWeightSum  \\\n",
      "0              1             1                      0   -207.161031   \n",
      "1              0             0                      0   -536.990588   \n",
      "2              1             1                      0   -160.004636   \n",
      "3              1             1                      1   -188.646160   \n",
      "4              0             0                      0   -274.409048   \n",
      "\n",
      "   gunningFog fleschReadingEase  daleChallReadabilityScore  smogIndex  \\\n",
      "0       71.70            -80.10                      13.96        0.0   \n",
      "1       52.99            -26.31                      12.32        0.0   \n",
      "2       17.06             64.54                       7.83        0.0   \n",
      "3       26.89             32.23                       9.16        0.0   \n",
      "4       21.25             45.43                       8.34        0.0   \n",
      "\n",
      "   colemanLiauIndex  userProfileFilled  \n",
      "0              9.83                  2  \n",
      "1             11.98                  0  \n",
      "2             14.97                  3  \n",
      "3             17.02                  4  \n",
      "4              9.41                  0  \n",
      "\n",
      "[5 rows x 42 columns]\n"
     ]
    }
   ],
   "source": [
    "print(openCloseData.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996, 42)\n",
      "(159996, 41)\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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PostId', 'UserId', 'Comment', 'Body', 'Title', 'Tags', 'Reputation',\n",
       "       'experienceInTime', 'WebsiteUrl', 'Location', 'AboutMe', 'Views',\n",
       "       'Upvotes', 'Downvotes', 'ProfileImageUrl', 'tagCount', 'closed',\n",
       "       'lowerUpperRatio', 'sentenceCount', 'firstLineLength', 'codeLength',\n",
       "       'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
       "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
       "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
       "       'punctuationCount', 'websiteUrlFilled', 'locationFilled',\n",
       "       'aboutMeFilled', 'profileImageUrlFilled', 'tagWeightSum', 'gunningFog',\n",
       "       'fleschReadingEase', 'daleChallReadabilityScore', 'smogIndex',\n",
       "       'colemanLiauIndex', 'userProfileFilled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openCloseData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996, 41)\n"
     ]
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/33961028/remove-non-numeric-rows-in-one-column-with-pandas\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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#openCloseData['codeTextRatio']=openCloseData['codeLength']/(openCloseData['bodyWordCount']+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ADDING MORE FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tagWeightData=pd.read_csv(\"tagWeightData.csv\")\n",
    "# print(tagWeightData.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import re\n",
    "# idx=0\n",
    "# def calculateTagWeight(tags):\n",
    "#     forClosed=re.findall('<(.*?)>',tags)\n",
    "#     forOpen=tags.split('|')\n",
    "#     #print(forClosed)\n",
    "#     #print(forOpen)\n",
    "#     if(len(forClosed) ):  #if we pass tags for open in close it won't work, but vice versa will work hence \n",
    "#         # this particular order of if-else \n",
    "#         tagList=forClosed\n",
    "#     elif(len(forOpen) ):\n",
    "#         tagList=forOpen\n",
    "#     #print(tagList)\n",
    "#     wt=0\n",
    "# #     for t in tagList:\n",
    "# #         #print(t)\n",
    "# #         try:\n",
    "            \n",
    "# #         except:\n",
    "# #             print(t)\n",
    "# #         print(wt)\n",
    "# #     print(\"check\")\n",
    "# #     if(idx%1000==0)print(idx)\n",
    "# #     idx+=1\n",
    "#     try:\n",
    "#         for t in tagList:\n",
    "#             wt=wt+tagWeightData[tagWeightData['TagName']==t][['tagWeight']].values[0]\n",
    "#     except:\n",
    "#         #print(tagList)\n",
    "#         pass\n",
    "#     return wt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import time\n",
    "# tic=time.time()\n",
    "# openCloseData['tagWeightSum']=openCloseData['Tags'].apply(calculateTagWeight) #tagClosingWeightSum\n",
    "# toc=time.time()\n",
    "# print(str((toc-tic)/60)+\"mins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PostId', 'UserId', 'Comment', 'Body', 'Title', 'Tags', 'Reputation',\n",
       "       'experienceInTime', 'WebsiteUrl', 'Location', 'AboutMe', 'Views',\n",
       "       'Upvotes', 'Downvotes', 'ProfileImageUrl', 'tagCount', 'closed',\n",
       "       'lowerUpperRatio', 'sentenceCount', 'firstLineLength', 'codeLength',\n",
       "       'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
       "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
       "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
       "       'punctuationCount', 'websiteUrlFilled', 'locationFilled',\n",
       "       'aboutMeFilled', 'profileImageUrlFilled', 'tagWeightSum', 'gunningFog',\n",
       "       'fleschReadingEase', 'daleChallReadabilityScore', 'smogIndex',\n",
       "       'colemanLiauIndex', 'userProfileFilled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "openCloseData.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# openCloseData['tagWeightSum'][0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# openCloseData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def getInitialEle(ar):\n",
    "#     #print(type(ar))\n",
    "#     return float(ar)\n",
    "\n",
    "# openCloseData['tagWeightSum']=openCloseData['tagWeightSum'].apply(getInitialEle)\n",
    "# print(openCloseData.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# openCloseData.to_csv(\"openCloseData[35 cols].csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996, 27)\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "X = openCloseData.loc[:,['Reputation',\n",
    "       'experienceInTime', 'Views',\n",
    "       'Upvotes', 'Downvotes', 'tagCount', 'closed',\n",
    "       'lowerUpperRatio', 'sentenceCount', 'firstLineLength', 'codeLength',\n",
    "       'urlCount', 'SOUrlCount', 'titleLengthInChars',\n",
    "       'countOfInterrogativeSent', 'sentencesStartWithYouCount',\n",
    "       'sentencesStartWithICount', 'shortWordCount', 'bodyWordCount',\n",
    "       'punctuationCount', 'tagWeightSum', 'gunningFog',\n",
    "       'fleschReadingEase', 'daleChallReadabilityScore', 'smogIndex',\n",
    "       'colemanLiauIndex', 'userProfileFilled'] ]\n",
    "\n",
    "\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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reputation</th>\n",
       "      <th>experienceInTime</th>\n",
       "      <th>Views</th>\n",
       "      <th>Upvotes</th>\n",
       "      <th>Downvotes</th>\n",
       "      <th>tagCount</th>\n",
       "      <th>closed</th>\n",
       "      <th>lowerUpperRatio</th>\n",
       "      <th>sentenceCount</th>\n",
       "      <th>firstLineLength</th>\n",
       "      <th>...</th>\n",
       "      <th>shortWordCount</th>\n",
       "      <th>bodyWordCount</th>\n",
       "      <th>punctuationCount</th>\n",
       "      <th>tagWeightSum</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1</td>\n",
       "      <td>26.153846</td>\n",
       "      <td>6</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>168</td>\n",
       "      <td>50</td>\n",
       "      <td>-207.161031</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>1</td>\n",
       "      <td>16.448276</td>\n",
       "      <td>10</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>113</td>\n",
       "      <td>19</td>\n",
       "      <td>-536.990588</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>1</td>\n",
       "      <td>18.916667</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "      <td>-160.004636</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>1</td>\n",
       "      <td>12.272727</td>\n",
       "      <td>5</td>\n",
       "      <td>210</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>94</td>\n",
       "      <td>45</td>\n",
       "      <td>-188.646160</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>245</td>\n",
       "      <td>648</td>\n",
       "      <td>78</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>68</td>\n",
       "      <td>11</td>\n",
       "      <td>-274.409048</td>\n",
       "      <td>21.25</td>\n",
       "      <td>45.43</td>\n",
       "      <td>8.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Reputation  experienceInTime  Views  Upvotes  Downvotes  tagCount  closed  \\\n",
       "0       17527              1953   2765     2005        119         4       1   \n",
       "1         151              1425     32       16          0         5       1   \n",
       "2        1639               900    208       75          1         3       1   \n",
       "3        2669               620    658      282         11         2       1   \n",
       "4         245               648     78       14          0         4       1   \n",
       "\n",
       "   lowerUpperRatio  sentenceCount  firstLineLength        ...          \\\n",
       "0        26.153846              6               69        ...           \n",
       "1        16.448276             10               75        ...           \n",
       "2        18.916667              4               79        ...           \n",
       "3        12.272727              5              210        ...           \n",
       "4        98.000000              2               92        ...           \n",
       "\n",
       "   shortWordCount  bodyWordCount  punctuationCount  tagWeightSum  gunningFog  \\\n",
       "0              84            168                50   -207.161031       71.70   \n",
       "1              51            113                19   -536.990588       52.99   \n",
       "2              15             47                17   -160.004636       17.06   \n",
       "3              46             94                45   -188.646160       26.89   \n",
       "4              31             68                11   -274.409048       21.25   \n",
       "\n",
       "   fleschReadingEase  daleChallReadabilityScore  smogIndex  colemanLiauIndex  \\\n",
       "0             -80.10                      13.96        0.0              9.83   \n",
       "1             -26.31                      12.32        0.0             11.98   \n",
       "2              64.54                       7.83        0.0             14.97   \n",
       "3              32.23                       9.16        0.0             17.02   \n",
       "4              45.43                       8.34        0.0              9.41   \n",
       "\n",
       "   userProfileFilled  \n",
       "0                  2  \n",
       "1                  0  \n",
       "2                  3  \n",
       "3                  4  \n",
       "4                  0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reputation</th>\n",
       "      <th>experienceInTime</th>\n",
       "      <th>Views</th>\n",
       "      <th>Upvotes</th>\n",
       "      <th>Downvotes</th>\n",
       "      <th>tagCount</th>\n",
       "      <th>closed</th>\n",
       "      <th>lowerUpperRatio</th>\n",
       "      <th>sentenceCount</th>\n",
       "      <th>firstLineLength</th>\n",
       "      <th>...</th>\n",
       "      <th>shortWordCount</th>\n",
       "      <th>bodyWordCount</th>\n",
       "      <th>punctuationCount</th>\n",
       "      <th>tagWeightSum</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Reputation, experienceInTime, Views, Upvotes, Downvotes, tagCount, closed, lowerUpperRatio, sentenceCount, firstLineLength, codeLength, urlCount, SOUrlCount, titleLengthInChars, countOfInterrogativeSent, sentencesStartWithYouCount, sentencesStartWithICount, shortWordCount, bodyWordCount, punctuationCount, tagWeightSum, gunningFog, fleschReadingEase, daleChallReadabilityScore, smogIndex, colemanLiauIndex, userProfileFilled]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 27 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[np.isinf(X).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159996,)\n"
     ]
    }
   ],
   "source": [
    "y = X.closed\n",
    "X.drop(['closed'],inplace=True,axis=1)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MEAN NORMALIZATION of X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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', 'tagWeightSum', 'gunningFog', 'fleschReadingEase',\n",
       "       'daleChallReadabilityScore', 'smogIndex', 'colemanLiauIndex',\n",
       "       'userProfileFilled'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "   Reputation  experienceInTime  Views  Upvotes  Downvotes  tagCount  \\\n",
      "0       17527              1953   2765     2005        119         4   \n",
      "1         151              1425     32       16          0         5   \n",
      "2        1639               900    208       75          1         3   \n",
      "3        2669               620    658      282         11         2   \n",
      "4         245               648     78       14          0         4   \n",
      "\n",
      "   lowerUpperRatio  sentenceCount  firstLineLength  codeLength  \\\n",
      "0        26.153846              6               69          40   \n",
      "1        16.448276             10               75           0   \n",
      "2        18.916667              4               79           0   \n",
      "3        12.272727              5              210         661   \n",
      "4        98.000000              2               92           0   \n",
      "\n",
      "         ...          shortWordCount  bodyWordCount  punctuationCount  \\\n",
      "0        ...                      84            168                50   \n",
      "1        ...                      51            113                19   \n",
      "2        ...                      15             47                17   \n",
      "3        ...                      46             94                45   \n",
      "4        ...                      31             68                11   \n",
      "\n",
      "   tagWeightSum  gunningFog  fleschReadingEase  daleChallReadabilityScore  \\\n",
      "0   -207.161031       71.70             -80.10                      13.96   \n",
      "1   -536.990588       52.99             -26.31                      12.32   \n",
      "2   -160.004636       17.06              64.54                       7.83   \n",
      "3   -188.646160       26.89              32.23                       9.16   \n",
      "4   -274.409048       21.25              45.43                       8.34   \n",
      "\n",
      "   smogIndex  colemanLiauIndex  userProfileFilled  \n",
      "0        0.0              9.83                  2  \n",
      "1        0.0             11.98                  0  \n",
      "2        0.0             14.97                  3  \n",
      "3        0.0             17.02                  4  \n",
      "4        0.0              9.41                  0  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "print(type(X))\n",
    "print(X.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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', 'tagWeightSum', 'gunningFog',\n",
    "       'fleschReadingEase', 'daleChallReadabilityScore', 'smogIndex',\n",
    "       'colemanLiauIndex', 'userProfileFilled'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "   Reputation  experienceInTime     Views   Upvotes  Downvotes  tagCount  \\\n",
      "0    1.432738          2.465146  1.435231  2.289324   0.290707  0.892056   \n",
      "1   -0.172483          1.563097 -0.156726 -0.270553  -0.065531  1.707299   \n",
      "2   -0.035019          0.666172 -0.054207 -0.194619  -0.062537  0.076813   \n",
      "3    0.060134          0.187813  0.207916  0.071794  -0.032601 -0.738430   \n",
      "4   -0.163799          0.235649 -0.129931 -0.273127  -0.065531  0.892056   \n",
      "\n",
      "   lowerUpperRatio  sentenceCount  firstLineLength  codeLength  \\\n",
      "0        -0.148165       0.003536        -0.329537   -0.366570   \n",
      "1        -0.524678       0.364392        -0.265402   -0.394312   \n",
      "2        -0.428920      -0.176891        -0.222645   -0.394312   \n",
      "3        -0.686662      -0.086677         1.177640    0.064136   \n",
      "4         2.638998      -0.357319        -0.083685   -0.394312   \n",
      "\n",
      "         ...          shortWordCount  bodyWordCount  punctuationCount  \\\n",
      "0        ...                1.233166       1.204831          0.046470   \n",
      "1        ...                0.287032       0.402707         -0.170823   \n",
      "2        ...               -0.745114      -0.559842         -0.184842   \n",
      "3        ...                0.143679       0.125610          0.011423   \n",
      "4        ...               -0.286382      -0.253576         -0.226899   \n",
      "\n",
      "   tagWeightSum  gunningFog  fleschReadingEase  daleChallReadabilityScore  \\\n",
      "0      0.176386    4.754973          -3.624203                   3.398255   \n",
      "1     -1.362468    3.068809          -2.172798                   2.505256   \n",
      "2      0.396399   -0.169238           0.278589                   0.060400   \n",
      "3      0.262769    0.716652          -0.593225                   0.784600   \n",
      "4     -0.137367    0.208369          -0.237052                   0.338101   \n",
      "\n",
      "   smogIndex  colemanLiauIndex  userProfileFilled  \n",
      "0  -1.016662          0.001299           0.413572  \n",
      "1  -1.016662          0.372791          -1.386184  \n",
      "2  -1.016662          0.889424           1.313449  \n",
      "3  -1.016662          1.243637           2.213327  \n",
      "4  -1.016662         -0.071271          -1.386184  \n",
      "\n",
      "[5 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "print(type(X))\n",
    "print(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conduct Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Original number of features:', X.shape[1])\n",
    "# from sklearn.decomposition import PCA\n",
    "# pca = PCA(n_components=0.99)\n",
    "# X = pca.fit_transform(X)\n",
    "# print('Reduced number of features:', X.shape[1])\n",
    "# print(type(X))\n",
    "# X = pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>codeLength</th>\n",
       "      <th>...</th>\n",
       "      <th>shortWordCount</th>\n",
       "      <th>bodyWordCount</th>\n",
       "      <th>punctuationCount</th>\n",
       "      <th>tagWeightSum</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.432738</td>\n",
       "      <td>2.465146</td>\n",
       "      <td>1.435231</td>\n",
       "      <td>2.289324</td>\n",
       "      <td>0.290707</td>\n",
       "      <td>0.892056</td>\n",
       "      <td>-0.148165</td>\n",
       "      <td>0.003536</td>\n",
       "      <td>-0.329537</td>\n",
       "      <td>-0.366570</td>\n",
       "      <td>...</td>\n",
       "      <td>1.233166</td>\n",
       "      <td>1.204831</td>\n",
       "      <td>0.046470</td>\n",
       "      <td>0.176386</td>\n",
       "      <td>4.754973</td>\n",
       "      <td>-3.624203</td>\n",
       "      <td>3.398255</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>0.001299</td>\n",
       "      <td>0.413572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.172483</td>\n",
       "      <td>1.563097</td>\n",
       "      <td>-0.156726</td>\n",
       "      <td>-0.270553</td>\n",
       "      <td>-0.065531</td>\n",
       "      <td>1.707299</td>\n",
       "      <td>-0.524678</td>\n",
       "      <td>0.364392</td>\n",
       "      <td>-0.265402</td>\n",
       "      <td>-0.394312</td>\n",
       "      <td>...</td>\n",
       "      <td>0.287032</td>\n",
       "      <td>0.402707</td>\n",
       "      <td>-0.170823</td>\n",
       "      <td>-1.362468</td>\n",
       "      <td>3.068809</td>\n",
       "      <td>-2.172798</td>\n",
       "      <td>2.505256</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>0.372791</td>\n",
       "      <td>-1.386184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.035019</td>\n",
       "      <td>0.666172</td>\n",
       "      <td>-0.054207</td>\n",
       "      <td>-0.194619</td>\n",
       "      <td>-0.062537</td>\n",
       "      <td>0.076813</td>\n",
       "      <td>-0.428920</td>\n",
       "      <td>-0.176891</td>\n",
       "      <td>-0.222645</td>\n",
       "      <td>-0.394312</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.745114</td>\n",
       "      <td>-0.559842</td>\n",
       "      <td>-0.184842</td>\n",
       "      <td>0.396399</td>\n",
       "      <td>-0.169238</td>\n",
       "      <td>0.278589</td>\n",
       "      <td>0.060400</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>0.889424</td>\n",
       "      <td>1.313449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.060134</td>\n",
       "      <td>0.187813</td>\n",
       "      <td>0.207916</td>\n",
       "      <td>0.071794</td>\n",
       "      <td>-0.032601</td>\n",
       "      <td>-0.738430</td>\n",
       "      <td>-0.686662</td>\n",
       "      <td>-0.086677</td>\n",
       "      <td>1.177640</td>\n",
       "      <td>0.064136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143679</td>\n",
       "      <td>0.125610</td>\n",
       "      <td>0.011423</td>\n",
       "      <td>0.262769</td>\n",
       "      <td>0.716652</td>\n",
       "      <td>-0.593225</td>\n",
       "      <td>0.784600</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>1.243637</td>\n",
       "      <td>2.213327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.163799</td>\n",
       "      <td>0.235649</td>\n",
       "      <td>-0.129931</td>\n",
       "      <td>-0.273127</td>\n",
       "      <td>-0.065531</td>\n",
       "      <td>0.892056</td>\n",
       "      <td>2.638998</td>\n",
       "      <td>-0.357319</td>\n",
       "      <td>-0.083685</td>\n",
       "      <td>-0.394312</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.286382</td>\n",
       "      <td>-0.253576</td>\n",
       "      <td>-0.226899</td>\n",
       "      <td>-0.137367</td>\n",
       "      <td>0.208369</td>\n",
       "      <td>-0.237052</td>\n",
       "      <td>0.338101</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>-0.071271</td>\n",
       "      <td>-1.386184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Reputation  experienceInTime     Views   Upvotes  Downvotes  tagCount  \\\n",
       "0    1.432738          2.465146  1.435231  2.289324   0.290707  0.892056   \n",
       "1   -0.172483          1.563097 -0.156726 -0.270553  -0.065531  1.707299   \n",
       "2   -0.035019          0.666172 -0.054207 -0.194619  -0.062537  0.076813   \n",
       "3    0.060134          0.187813  0.207916  0.071794  -0.032601 -0.738430   \n",
       "4   -0.163799          0.235649 -0.129931 -0.273127  -0.065531  0.892056   \n",
       "\n",
       "   lowerUpperRatio  sentenceCount  firstLineLength  codeLength  \\\n",
       "0        -0.148165       0.003536        -0.329537   -0.366570   \n",
       "1        -0.524678       0.364392        -0.265402   -0.394312   \n",
       "2        -0.428920      -0.176891        -0.222645   -0.394312   \n",
       "3        -0.686662      -0.086677         1.177640    0.064136   \n",
       "4         2.638998      -0.357319        -0.083685   -0.394312   \n",
       "\n",
       "         ...          shortWordCount  bodyWordCount  punctuationCount  \\\n",
       "0        ...                1.233166       1.204831          0.046470   \n",
       "1        ...                0.287032       0.402707         -0.170823   \n",
       "2        ...               -0.745114      -0.559842         -0.184842   \n",
       "3        ...                0.143679       0.125610          0.011423   \n",
       "4        ...               -0.286382      -0.253576         -0.226899   \n",
       "\n",
       "   tagWeightSum  gunningFog  fleschReadingEase  daleChallReadabilityScore  \\\n",
       "0      0.176386    4.754973          -3.624203                   3.398255   \n",
       "1     -1.362468    3.068809          -2.172798                   2.505256   \n",
       "2      0.396399   -0.169238           0.278589                   0.060400   \n",
       "3      0.262769    0.716652          -0.593225                   0.784600   \n",
       "4     -0.137367    0.208369          -0.237052                   0.338101   \n",
       "\n",
       "   smogIndex  colemanLiauIndex  userProfileFilled  \n",
       "0  -1.016662          0.001299           0.413572  \n",
       "1  -1.016662          0.372791          -1.386184  \n",
       "2  -1.016662          0.889424           1.313449  \n",
       "3  -1.016662          1.243637           2.213327  \n",
       "4  -1.016662         -0.071271          -1.386184  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train/Test Split  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(111997, 26)\n",
      "(111997,)\n",
      "(47999, 26)\n",
      "(47999,)\n"
     ]
    }
   ],
   "source": [
    "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>codeLength</th>\n",
       "      <th>...</th>\n",
       "      <th>shortWordCount</th>\n",
       "      <th>bodyWordCount</th>\n",
       "      <th>punctuationCount</th>\n",
       "      <th>tagWeightSum</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29416</th>\n",
       "      <td>-0.115114</td>\n",
       "      <td>0.452619</td>\n",
       "      <td>-0.010520</td>\n",
       "      <td>0.398696</td>\n",
       "      <td>-0.053556</td>\n",
       "      <td>-0.738430</td>\n",
       "      <td>0.363113</td>\n",
       "      <td>-0.447533</td>\n",
       "      <td>0.536288</td>\n",
       "      <td>-0.049610</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.773785</td>\n",
       "      <td>-0.749435</td>\n",
       "      <td>0.579190</td>\n",
       "      <td>0.783278</td>\n",
       "      <td>-0.080018</td>\n",
       "      <td>0.246750</td>\n",
       "      <td>-0.288087</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>-0.021163</td>\n",
       "      <td>1.313449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58063</th>\n",
       "      <td>-0.183569</td>\n",
       "      <td>0.678131</td>\n",
       "      <td>-0.169540</td>\n",
       "      <td>-0.230655</td>\n",
       "      <td>-0.065531</td>\n",
       "      <td>-1.553673</td>\n",
       "      <td>-0.593793</td>\n",
       "      <td>-0.447533</td>\n",
       "      <td>-0.532632</td>\n",
       "      <td>-0.394312</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.060492</td>\n",
       "      <td>-1.099452</td>\n",
       "      <td>-0.296994</td>\n",
       "      <td>0.772687</td>\n",
       "      <td>-0.264766</td>\n",
       "      <td>0.648524</td>\n",
       "      <td>0.626692</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>1.374955</td>\n",
       "      <td>-0.486306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62206</th>\n",
       "      <td>-0.116962</td>\n",
       "      <td>0.013553</td>\n",
       "      <td>-0.106049</td>\n",
       "      <td>-0.059482</td>\n",
       "      <td>0.003322</td>\n",
       "      <td>0.892056</td>\n",
       "      <td>1.022602</td>\n",
       "      <td>0.364392</td>\n",
       "      <td>-0.286780</td>\n",
       "      <td>-0.394312</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.429736</td>\n",
       "      <td>-0.428585</td>\n",
       "      <td>-0.086710</td>\n",
       "      <td>-0.013216</td>\n",
       "      <td>1.259180</td>\n",
       "      <td>-1.268874</td>\n",
       "      <td>1.606812</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>2.776257</td>\n",
       "      <td>1.313449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14790</th>\n",
       "      <td>-0.178488</td>\n",
       "      <td>-0.871412</td>\n",
       "      <td>-0.170123</td>\n",
       "      <td>-0.291145</td>\n",
       "      <td>-0.065531</td>\n",
       "      <td>-1.553673</td>\n",
       "      <td>-0.373039</td>\n",
       "      <td>-0.086677</td>\n",
       "      <td>0.322504</td>\n",
       "      <td>-0.394312</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.143029</td>\n",
       "      <td>-0.224408</td>\n",
       "      <td>-0.226899</td>\n",
       "      <td>0.813817</td>\n",
       "      <td>1.125801</td>\n",
       "      <td>-0.994729</td>\n",
       "      <td>0.403442</td>\n",
       "      <td>-1.016662</td>\n",
       "      <td>-0.028074</td>\n",
       "      <td>-0.486306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152752</th>\n",
       "      <td>-0.172206</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.132261</td>\n",
       "      <td>-0.273127</td>\n",
       "      <td>-0.062537</td>\n",
       "      <td>1.707299</td>\n",
       "      <td>0.170762</td>\n",
       "      <td>-0.176891</td>\n",
       "      <td>-0.190577</td>\n",
       "      <td>-0.345069</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.487078</td>\n",
       "      <td>-0.399417</td>\n",
       "      <td>-0.275965</td>\n",
       "      <td>-0.997714</td>\n",
       "      <td>-0.326949</td>\n",
       "      <td>0.165531</td>\n",
       "      <td>-0.070283</td>\n",
       "      <td>0.960018</td>\n",
       "      <td>0.206916</td>\n",
       "      <td>1.313449</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Reputation  experienceInTime     Views   Upvotes  Downvotes  tagCount  \\\n",
       "29416    -0.115114          0.452619 -0.010520  0.398696  -0.053556 -0.738430   \n",
       "58063    -0.183569          0.678131 -0.169540 -0.230655  -0.065531 -1.553673   \n",
       "62206    -0.116962          0.013553 -0.106049 -0.059482   0.003322  0.892056   \n",
       "14790    -0.178488         -0.871412 -0.170123 -0.291145  -0.065531 -1.553673   \n",
       "152752   -0.172206         -0.000114 -0.132261 -0.273127  -0.062537  1.707299   \n",
       "\n",
       "        lowerUpperRatio  sentenceCount  firstLineLength  codeLength  \\\n",
       "29416          0.363113      -0.447533         0.536288   -0.049610   \n",
       "58063         -0.593793      -0.447533        -0.532632   -0.394312   \n",
       "62206          1.022602       0.364392        -0.286780   -0.394312   \n",
       "14790         -0.373039      -0.086677         0.322504   -0.394312   \n",
       "152752         0.170762      -0.176891        -0.190577   -0.345069   \n",
       "\n",
       "              ...          shortWordCount  bodyWordCount  punctuationCount  \\\n",
       "29416         ...               -0.773785      -0.749435          0.579190   \n",
       "58063         ...               -1.060492      -1.099452         -0.296994   \n",
       "62206         ...               -0.429736      -0.428585         -0.086710   \n",
       "14790         ...               -0.143029      -0.224408         -0.226899   \n",
       "152752        ...               -0.487078      -0.399417         -0.275965   \n",
       "\n",
       "        tagWeightSum  gunningFog  fleschReadingEase  \\\n",
       "29416       0.783278   -0.080018           0.246750   \n",
       "58063       0.772687   -0.264766           0.648524   \n",
       "62206      -0.013216    1.259180          -1.268874   \n",
       "14790       0.813817    1.125801          -0.994729   \n",
       "152752     -0.997714   -0.326949           0.165531   \n",
       "\n",
       "        daleChallReadabilityScore  smogIndex  colemanLiauIndex  \\\n",
       "29416                   -0.288087  -1.016662         -0.021163   \n",
       "58063                    0.626692  -1.016662          1.374955   \n",
       "62206                    1.606812  -1.016662          2.776257   \n",
       "14790                    0.403442  -1.016662         -0.028074   \n",
       "152752                  -0.070283   0.960018          0.206916   \n",
       "\n",
       "        userProfileFilled  \n",
       "29416            1.313449  \n",
       "58063           -0.486306  \n",
       "62206            1.313449  \n",
       "14790           -0.486306  \n",
       "152752           1.313449  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LOGISTIC REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg=LogisticRegression()\n",
    "logreg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47999,)\n"
     ]
    }
   ],
   "source": [
    "y_pred=logreg.predict(X_test)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8864976353674034"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "np.mean(y_pred==y_test)\n",
    "\n",
    "#26 columns with Scaling: 0.8864976353674034"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.88      0.89     24085\n",
      "          1       0.88      0.89      0.89     23914\n",
      "\n",
      "avg / total       0.89      0.89      0.89     47999\n",
      "\n",
      "Precision: 0.8873913633946493\n",
      "Recall: 0.8873865140134614\n",
      "F1 Score:0.8873725937122617\n",
      "(0.8873913633946493, 0.8873865140134614, 0.8873725937122617, 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",
    "\n",
    "#26 cols with Scaling:\n",
    "# Precision: 0.8873913633946493\n",
    "# Recall: 0.8873865140134614\n",
    "# F1 Score:0.8873725937122617"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO REGULARIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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)))\n",
    "#for 23 features: highest accuracy obt was for lambda=650 c=0.0015384615384615385 Accuracy:0.8463926331798579\n",
    "#for 24 features: highest accuracy obt was for lambda=500 c=0.002 Accuracy:0.8551636492426925\n",
    "#if we increase lambda very much then too accuracy will reduce because of underfitting\n",
    "\n",
    "#After scaling:\n",
    "#29 features: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RIGDE REGULARIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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:0.8540594595720744\n",
      "lambda=0.01 c=100.0 Accuracy:0.8540594595720744\n",
      "lambda=0.1 c=10.0 Accuracy:0.8540177920373341\n",
      "lambda=1 c=1.0 Accuracy:0.85345528031834\n",
      "lambda=10 c=0.1 Accuracy:0.8510802308381424\n",
      "lambda=100 c=0.01 Accuracy:0.8373299443738411\n",
      "lambda=1000 c=0.001 Accuracy:0.7960999187483072\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)))\n",
    "    \n",
    "#for 24 features: highest accuracy obt was for:lambda=0.01 c=100.0 Accuracy:0.8533927790162295\n",
    "\n",
    "#After PCA: \n",
    "# 29 features to 26 features: lambda=0.001 c=1000.0 Accuracy:0.8504343840496676"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRID SEARCH CV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "# param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],'penalty':['l1','l2'] }\n",
    "# gridSearch=GridSearchCV(LogisticRegression(), param_grid,cv=5) #5 fold cross validation\n",
    "# gridSearch.fit(X_train,y_train) \n",
    "# print(gridSearch.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #print(gridSearch.best_score_)\n",
    "# y_pred=gridSearch.predict(X_test)\n",
    "# #Accuracy on never seen before test set\n",
    "# print(np.mean(y_pred==y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Best params obtained: {'C': 0.01, 'penalty': 'l1'}\n",
    "#Accuracy obt: 0.845038438300798\n",
    "#Accuracy obt for 24 features: 0.8547469738952895"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### STOCHASTIC GRADIENT DESCENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
       "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
       "       learning_rate='optimal', loss='log', max_iter=5000, n_iter=None,\n",
       "       n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\n",
       "       shuffle=True, tol=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "sgd=SGDClassifier(loss=\"log\",max_iter=5000) #logistic regression\n",
    "sgd.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47999,)\n"
     ]
    }
   ],
   "source": [
    "y_pred=sgd.predict(X_test)\n",
    "print(y_pred.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88601845871789"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy\n",
    "np.mean(y_pred==y_test)\n",
    "#for 23 features: with max_iter=5000 Accuracy is:0.8080793349861456\n",
    "#for 24 features: with max_iter=5000 Accuracy is:0.8219962915894081\n",
    "#After scaling:\n",
    "#24 features: with max_iter=5000 Accuracy is: 0.8508718931644409\n",
    "#29 features: with max_iter=5000 Accuracy is: 0.8507260567928498\n",
    "\n",
    "#PCA: \n",
    "#29 features to 26 features: 0.8446634304881352\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.88      0.81      0.84     24085\n",
      "          1       0.82      0.89      0.86     23914\n",
      "\n",
      "avg / total       0.85      0.85      0.85     47999\n",
      "\n",
      "Precision: 0.8533320912052453\n",
      "Recall: 0.8508790651450169\n",
      "F1 Score:0.850487823093065\n",
      "(0.8533320912052453, 0.8508790651450169, 0.850487823093065, 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sgd.get_params() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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