Sentiment_Analyzer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")                   \n",
    "import numpy as np                                  \n",
    "import pandas as pd                                 \n",
    "import nltk    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      5  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      4  1219017600   \n",
       "3                     3                       3      2  1307923200   \n",
       "4                     0                       0      5  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...  \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...  \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...  \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...  \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../Data Resources/Reviews.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'ProductId', 'UserId', 'ProfileName', 'HelpfulnessNumerator',\n",
       "       'HelpfulnessDenominator', 'Score', 'Time', 'Summary', 'Text'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Helpful %'] = np.where(df['HelpfulnessDenominator'] > 0, df['HelpfulnessNumerator'] / df['HelpfulnessDenominator'], -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>Helpful %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      5  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      4  1219017600   \n",
       "3                     3                       3      2  1307923200   \n",
       "4                     0                       0      5  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \\\n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
       "\n",
       "   Helpful %  \n",
       "0        1.0  \n",
       "1       -1.0  \n",
       "2        1.0  \n",
       "3        1.0  \n",
       "4       -1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        , -1.        ,  0.8       ,  0.        ,  0.5       ,\n",
       "        0.66666667,  0.25      ,  0.89473684,  0.83333333,  0.75      ,\n",
       "        0.33333333,  0.3       ,  0.11111111,  0.42857143,  0.875     ,\n",
       "        0.85714286,  0.2       ,  0.26315789,  0.6       ,  0.71428571,\n",
       "        0.53846154,  0.57142857,  0.91489362,  0.86666667,  0.82352941,\n",
       "        0.78571429,  0.74074074,  0.4       ,  0.375     ,  0.28571429,\n",
       "        0.14285714,  0.77777778,  0.125     ,  0.9       ,  0.94117647,\n",
       "        0.92307692,  0.7       ,  0.45454545,  0.88888889,  0.83870968,\n",
       "        0.9047619 ,  0.92857143,  0.90909091,  0.91666667,  0.84615385,\n",
       "        0.10526316,  0.98214286,  0.97826087,  0.7518797 ,  0.3125    ,\n",
       "        0.1       ,  0.18518519,  0.88      ,  0.69230769,  0.625     ,\n",
       "        0.54545455,  0.41666667,  0.45833333,  0.22222222,  0.81818182,\n",
       "        0.8125    ,  0.16666667,  0.93103448,  0.88235294,  0.23529412,\n",
       "        0.63636364,  0.81481481,  0.95652174,  0.64285714,  0.58333333,\n",
       "        0.94444444,  0.921875  ,  0.86574074,  0.96      ,  0.91304348,\n",
       "        0.64705882,  0.95833333,  0.09090909,  0.13333333,  0.52941176,\n",
       "        0.96969697,  0.36363636,  0.07142857,  0.72727273,  0.18181818,\n",
       "        0.96666667,  0.99074074,  0.97297297,  0.80645161,  0.64102564,\n",
       "        0.55555556,  0.4375    ,  0.76923077,  0.28      ,  0.15384615,\n",
       "        0.44444444,  0.5625    ,  0.53333333,  0.47058824,  0.47222222,\n",
       "        0.23076923,  0.25925926,  0.98876404,  0.88372093,  0.19047619,\n",
       "        0.94594595,  0.84313725,  0.96629213,  0.72222222,  0.05882353,\n",
       "        0.27272727,  0.97959184,  0.26666667,  0.30769231,  0.94736842,\n",
       "        0.27777778,  0.6875    ,  0.92      ,  0.90566038,  0.95      ,\n",
       "        0.9375    ,  0.9137931 ,  0.82857143,  0.86363636,  0.85      ,\n",
       "        0.96428571,  0.95238095,  0.08333333,  0.97560976,  0.93333333,\n",
       "        0.46666667,  0.96153846,  0.24      ,  0.92682927,  0.93548387,\n",
       "        0.86956522,  0.06666667,  0.98461538,  0.97      ,  0.97619048,\n",
       "        0.925     ,  0.88461538,  0.61538462,  0.09375   ,  0.79166667,\n",
       "        0.70588235,  0.45      ,  0.93939394,  0.90322581,  0.68      ,\n",
       "        0.95454545,  0.04166667,  0.89655172,  0.88571429,  0.38461538,\n",
       "        0.07692308,  0.12121212,  0.92237443,  0.92156863,  0.36585366,\n",
       "        0.88095238,  0.84      ,  0.61904762,  0.96129032,  0.96385542,\n",
       "        0.90588235,  0.87878788,  0.05555556,  0.80952381,  0.20689655,\n",
       "        0.07407407,  0.35      ,  0.77272727,  0.91428571,  0.04545455,\n",
       "        0.76470588,  0.70833333,  0.73333333,  0.93650794,  0.8671875 ,\n",
       "        0.75949367,  0.65957447,  0.57692308,  0.41176471,  0.40909091,\n",
       "        0.34693878,  0.30263158,  0.16176471,  0.65      ,  0.96296296,\n",
       "        0.96808511,  0.94915254,  0.98290598,  0.9893617 ,  0.95744681,\n",
       "        0.96268657,  0.98305085,  0.61111111,  0.59183673,  0.98913043,\n",
       "        0.98809524,  0.92982456,  0.78947368,  0.75757576,  0.82608696,\n",
       "        0.96491228,  0.84507042,  0.98412698,  0.96551724,  0.87341772,\n",
       "        0.73913043,  0.7037037 ,  0.98888889,  0.7826087 ,  0.17647059,\n",
       "        0.96226415,  0.94339623,  0.97058824,  0.57894737,  0.47368421,\n",
       "        0.5106383 ,  0.97777778,  0.92352941,  0.78378378,  0.97674419,\n",
       "        0.35714286,  0.94805195,  0.94285714,  0.86538462,  0.43478261,\n",
       "        0.99186992,  0.8627451 ,  0.97142857,  0.98484848,  0.73076923,\n",
       "        0.68181818,  0.63333333,  0.64583333,  0.96774194,  0.05263158,\n",
       "        0.36842105,  0.82926829,  0.92045455,  0.34782609,  0.85365854,\n",
       "        0.91803279,  0.97222222,  0.46153846,  0.2173913 ,  0.82051282,\n",
       "        0.29032258,  0.95754717,  0.91176471,  0.04761905,  0.65714286,\n",
       "        0.13636364,  0.77142857,  0.953125  ,  0.92592593,  0.0862069 ,\n",
       "        0.80555556,  0.20512821,  0.29411765,  0.9925187 ,  0.98564593,\n",
       "        0.99253731,  0.80487805,  0.82142857,  0.76      ,  0.21428571,\n",
       "        0.31914894,  0.02702703,  0.20833333,  0.92105263,  0.78125   ,\n",
       "        0.61290323,  0.97435897,  0.07894737,  0.72413793,  0.03125   ,\n",
       "        0.68421053,  0.97979798,  0.38888889,  0.975     ,  0.80769231,\n",
       "        0.06060606,  0.93023256,  0.97260274,  0.90769231,  0.31372549,\n",
       "        0.15789474,  0.32258065,  0.95959596,  0.21052632,  0.84210526,\n",
       "        0.32      ,  0.92631579,  0.03703704,  1.5       ,  0.11428571,\n",
       "        0.88333333,  0.1875    ,  0.96875   ,  0.64      ,  0.30434783,\n",
       "        0.93150685,  0.88709677,  0.75609756,  0.60606061,  0.54166667,\n",
       "        0.52380952,  0.98275862,  0.98630137,  0.76190476,  0.85106383,\n",
       "        0.79069767,  0.8974359 ,  0.93617021,  0.87234043,  0.0625    ,\n",
       "        0.075     ,  0.39393939,  0.74107143,  0.49090909,  0.90243902,\n",
       "        0.56521739,  0.27027027,  0.03846154,  0.31147541,  0.24528302,\n",
       "        0.97727273,  0.60714286,  0.98360656,  0.95918367,  0.94      ,\n",
       "        0.72      ,  0.15      ,  0.12903226,  0.35294118,  0.14084507,\n",
       "        0.13888889,  0.08219178,  0.03636364,  0.13043478,  0.55172414,\n",
       "        0.64516129,  0.98      ,  0.76271186,  0.98333333,  0.95384615,\n",
       "        0.85294118,  0.13513514,  0.32142857,  0.87912088,  0.82758621,\n",
       "        0.72881356,  0.73684211,  0.86111111,  0.81355932,  0.72839506,\n",
       "        0.73809524,  0.74193548,  0.51612903,  0.7109375 ,  0.69565217,\n",
       "        0.02941176,  0.17391304,  0.85185185,  0.06451613,  0.92727273,\n",
       "        0.08695652,  0.03333333,  0.475     ,  0.32352941,  0.22727273,\n",
       "        0.98113208,  0.42307692,  3.        ,  0.90625   ,  0.8404908 ,\n",
       "        0.72093023,  0.98181818,  0.69047619,  0.05660377,  0.93159609,\n",
       "        0.95604396,  0.95348837,  0.98823529,  0.95774648,  0.94520548,\n",
       "        0.62068966,  0.22058824,  0.25827815,  0.86842105,  0.82222222,\n",
       "        0.89041096,  0.78846154,  0.63157895,  0.98717949,  0.93406593,\n",
       "        0.11538462,  0.04      ,  0.86206897,  0.38095238,  0.95555556,\n",
       "        0.97402597,  0.94230769,  0.47619048,  0.99166667,  0.98387097,\n",
       "        0.93589744,  0.89915966,  0.88489209,  0.89285714,  0.8989899 ,\n",
       "        0.84415584,  0.02857143,  0.98496241,  0.96590909,  0.91240876,\n",
       "        0.94545455,  0.90526316,  0.67924528,  0.109375  ,  0.89090909,\n",
       "        0.98795181,  0.02777778,  0.94642857,  0.18918919,  0.67605634,\n",
       "        0.55      ,  0.53030303,  0.45098039,  0.05454545,  0.96363636,\n",
       "        0.06122449,  0.98039216,  0.99443414,  0.98688525,  0.27586207,\n",
       "        0.1025641 ,  0.11764706,  0.05128205,  0.81395349,  0.69387755,\n",
       "        0.98611111,  0.99466192,  0.98951782,  0.98723404,  0.97122302,\n",
       "        0.97183099,  0.75862069,  0.98550725,  0.97368421,  0.56      ,\n",
       "        0.98657718,  0.90196078,  0.77419355,  0.65625   ,  0.87012987,\n",
       "        0.25581395,  0.21153846,  0.71794872,  0.52      ,  0.02222222,\n",
       "        0.15625   ,  0.05      ,  0.10714286,  0.8902439 ,  0.79310345,\n",
       "        0.65384615,  0.94174757,  0.65116279,  0.59459459,  0.58823529,\n",
       "        0.0952381 ,  0.10638298,  0.20430108,  0.89361702,  0.65217391,\n",
       "        0.84090909,  0.92753623,  0.89156627,  0.89333333,  0.890625  ,\n",
       "        0.38709677,  0.60869565,  0.65853659,  0.42105263,  0.88405797,\n",
       "        0.92473118,  0.86486486,  0.02985075,  0.40625   ,  0.97916667,\n",
       "        0.52631579,  0.19230769,  0.98571429,  0.53571429,  0.12765957,\n",
       "        0.97333333,  0.67857143,  0.93506494,  0.88976378,  0.87037037,\n",
       "        0.81081081,  0.12244898,  0.51724138,  0.89502762,  0.51851852,\n",
       "        0.93181818,  0.82692308,  0.73529412,  0.22857143,  0.62962963,\n",
       "        0.31034483,  0.9787234 ,  0.96078431,  0.45714286,  0.98033708,\n",
       "        0.93877551,  0.86904762,  0.98268398,  0.98850575,  0.98148148,\n",
       "        0.56756757,  0.99145299,  0.17948718,  0.71641791,  0.91111111,\n",
       "        0.82653061,  0.98734177,  0.984375  ,  0.58064516,  0.47826087,\n",
       "        0.44      ,  0.39130435,  0.20454545,  0.98351648,  0.95714286,\n",
       "        0.96503497,  0.86263736,  0.12      ,  0.76595745,  0.86440678,\n",
       "        0.89873418,  0.91525424,  0.91071429,  0.88636364,  0.48275862,\n",
       "        0.03571429,  0.98591549,  0.69090909,  0.9516129 ,  0.48780488,\n",
       "        0.13793103,  0.08108108,  0.11904762,  0.80597015,  0.9273743 ,\n",
       "        0.89308176,  0.3960396 ,  0.98245614,  0.99415205,  0.98969072,\n",
       "        0.96721311,  0.64788732,  0.23333333,  0.99196787,  0.87603306,\n",
       "        0.86567164,  0.87096774,  0.83269962,  0.84057971,  0.82978723,\n",
       "        0.78333333,  0.80434783,  0.78787879,  0.95121951,  0.13157895,\n",
       "        0.96923077,  0.08571429,  0.98701299,  0.775     ,  0.90163934,\n",
       "        0.9245283 ,  0.34285714,  0.14814815,  0.83529412,  0.79487179,\n",
       "        0.74666667,  0.73239437,  0.74285714,  0.63855422,  0.63414634,\n",
       "        0.04347826,  0.84782609,  0.81632653,  0.94871795,  0.04301075,\n",
       "        0.22580645,  0.98924731,  0.84375   ,  0.94047619,  0.91891892,\n",
       "        0.85416667,  0.67307692,  0.97468354,  0.74545455,  0.7311828 ,\n",
       "        0.45945946,  0.97938144,  0.92405063,  0.97101449,  0.68493151,\n",
       "        0.58      ,  0.79104478,  0.68888889,  0.99278846,  0.87179487,\n",
       "        0.36111111,  0.36206897,  0.21621622,  0.08510638,  0.62745098,\n",
       "        0.48387097,  0.25806452,  0.98633257,  0.97761194,  0.9789916 ,\n",
       "        0.97530864,  0.96470588,  0.95588235,  0.89705882,  0.85483871,\n",
       "        0.72463768,  0.05405405,  0.8630137 ,  0.16129032,  0.29166667,\n",
       "        0.70454545,  0.23255814,  0.94623656,  0.95412844,  0.75555556,\n",
       "        0.67647059,  0.08      ,  0.98837209,  0.4137931 ,  0.59375   ,\n",
       "        0.52777778,  0.48      ,  0.46551724,  0.34146341,  0.36619718,\n",
       "        0.24137931,  0.30188679,  0.265625  ,  0.09756098,  0.13559322,\n",
       "        0.91440953,  0.91509434,  0.89622642,  0.86086957,  0.85436893,\n",
       "        0.85245902,  0.81609195,  0.8030303 ,  0.78      ,  0.77647059,\n",
       "        0.79545455,  0.76521739,  0.77966102,  0.72321429,  0.72972973,\n",
       "        0.67741935,  0.62222222,  0.98652291,  0.78873239,  0.26086957,\n",
       "        0.71875   ,  0.39285714,  0.87804878,  0.69444444,  0.79411765,\n",
       "        0.992     ,  0.97647059,  0.31578947,  0.31707317,  0.88679245,\n",
       "        0.79591837,  0.9261745 ,  0.8629174 ,  0.98666667,  0.26923077,\n",
       "        0.17857143,  0.38235294,  0.99180328,  0.15942029,  0.90277778,\n",
       "        0.36      ,  0.98507463,  0.7721519 ,  0.04651163,  0.68965517,\n",
       "        0.95890411,  0.06766917,  0.56603774,  0.69767442,  0.93442623,\n",
       "        0.97807757,  0.52173913,  0.75471698,  0.70967742,  0.98076923,\n",
       "        0.23809524,  0.95522388,  0.87142857,  0.74418605,  0.83783784,\n",
       "        0.75510204,  0.59090909,  0.89711934,  0.87301587,  0.89795918,\n",
       "        0.73493976,  0.99122807,  0.96644295,  0.95876289,  0.86046512,\n",
       "        0.07954545,  0.76666667,  0.16216216,  0.02739726,  0.09677419,\n",
       "        0.27659574,  0.83636364,  0.65306122,  0.53521127,  0.97580645,\n",
       "        0.93478261,  0.7755102 ,  0.98672566,  0.99619772,  0.9876161 ,\n",
       "        0.97169811,  0.92957746,  0.97534247,  0.97123894,  0.97191011,\n",
       "        0.97969543,  0.96478873,  0.95491803,  0.03508772,  0.73584906,\n",
       "        0.96341463,  0.69135802,  0.61764706,  0.74358974,  0.92428198,\n",
       "        0.93421053,  0.78723404,  0.37931034,  0.95762712,  0.92783505,\n",
       "        0.16      ,  0.34615385,  0.76388889,  0.63461538,  0.68518519,\n",
       "        0.67567568,  0.675     ,  0.98394495,  0.40540541,  0.57575758,\n",
       "        0.89189189,  0.86330935,  0.18604651,  0.98897059,  0.9673913 ,\n",
       "        0.9379562 ,  0.93243243,  0.98347107,  0.19444444,  0.91139241,\n",
       "        0.84444444,  0.93220339,  0.968     ,  0.53125   ,  0.71698113,\n",
       "        0.80672269,  0.02325581,  0.21875   ,  0.89519651,  0.71604938,\n",
       "        0.2826087 ,  0.07462687,  0.97887324,  0.58974359,  0.33928571,\n",
       "        0.21818182,  0.06779661,  0.28947368,  0.9625    ,  0.95081967,\n",
       "        0.91549296,  0.10344828,  0.99212598,  0.84848485,  0.07317073,\n",
       "        0.97831325,  0.97972973,  0.96511628,  0.9202454 ,  0.90140845,\n",
       "        0.14492754,  0.37837838,  0.46511628,  0.98765432,  0.98697068,\n",
       "        0.91875   ,  0.84274194,  0.84693878,  0.828125  ,  0.6969697 ,\n",
       "        0.02439024,  0.99090909,  0.94078947,  0.94666667,  0.98979592,\n",
       "        0.81132075,  0.87654321,  0.15277778,  0.68085106,  0.82022472,\n",
       "        0.88321168,  0.96703297,  0.08955224,  0.31818182,  0.96610169,\n",
       "        0.95302013,  0.94375   ,  0.81578947,  0.98319328,  0.98639456,\n",
       "        0.89719626,  0.99107143,  0.64864865,  0.88947368,  0.78688525,\n",
       "        0.825     ,  0.34482759,  0.95098039,  0.04444444,  0.95804196,\n",
       "        0.18461538,  0.97663551,  0.97196262,  0.72641509,  0.3877551 ,\n",
       "        0.85964912,  0.43333333,  0.34920635,  0.09195402,  0.98529412,\n",
       "        0.36666667,  0.9695122 ,  0.83928571,  0.75675676,  0.5862069 ,\n",
       "        0.98780488,  0.48648649,  0.03448276,  0.04285714,  0.96039604,\n",
       "        0.01098901,  0.43902439,  0.31428571,  0.95327103,  0.98695652,\n",
       "        0.94949495,  0.89230769,  0.83673469,  0.75438596,  0.24242424,\n",
       "        0.25961538,  0.75409836,  0.14634146,  0.9627907 ,  0.30232558,\n",
       "        0.97163121,  0.37037037,  0.0212766 ,  0.5952381 ,  0.995     ,\n",
       "        0.99029126,  0.99689441,  0.9691358 ,  0.74      ,  0.34210526,\n",
       "        0.38596491,  0.97857143,  0.90697674,  0.20588235,  0.97807018,\n",
       "        0.97905759,  0.77570093,  0.11666667,  0.85384615,  0.92380952,\n",
       "        0.76829268,  0.91612903,  0.91025641,  0.99019608,  0.58181818,\n",
       "        0.46875   ,  0.73170732,  0.97321429,  0.70731707,  0.59259259,\n",
       "        0.10810811,  0.89830508,  0.44827586,  0.97457627,  0.8961039 ,\n",
       "        0.54285714,  0.07843137,  0.98251748,  0.90234375,  0.02830189,\n",
       "        0.98947368,  0.98684211,  0.17073171,  0.69724771,  0.976     ,\n",
       "        0.98165138,  0.97590361,  0.90438247,  0.40740741,  0.8490566 ,\n",
       "        0.96410256,  0.9380531 ,  0.77358491,  0.17777778,  0.44117647,\n",
       "        0.03076923,  0.87755102,  0.0962963 ,  0.91836735,  0.87951807,\n",
       "        0.80851064,  0.99141104])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Helpful %'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         80-100%\n",
       "1             NaN\n",
       "2         80-100%\n",
       "3         80-100%\n",
       "4             NaN\n",
       "           ...   \n",
       "568449        NaN\n",
       "568450        NaN\n",
       "568451    80-100%\n",
       "568452    80-100%\n",
       "568453        NaN\n",
       "Name: Helpful %, Length: 568454, dtype: category\n",
       "Categories (6, object): [Empty < 0-20% < 20-40% < 40-60% < 60-80% < 80-100%]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df['Helpful %'] , bins = [-1, 0, 0.2, 0.4, 0.6, 0.8, 1.0], labels = ['Empty', '0-20%', '20-40%', '40-60%', '60-80%', '80-100%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['%upvote'] = pd.cut( df['Helpful %'] , bins = [-1, 0, 0.2, 0.4, 0.6, 0.8, 1.0], labels = ['Empty', '0-20%', '20-40%', '40-60%', '60-80%', '80-100%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>Helpful %</th>\n",
       "      <th>%upvote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80-100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80-100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80-100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      5  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      4  1219017600   \n",
       "3                     3                       3      2  1307923200   \n",
       "4                     0                       0      5  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \\\n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
       "\n",
       "   Helpful %  %upvote  \n",
       "0        1.0  80-100%  \n",
       "1       -1.0      NaN  \n",
       "2        1.0  80-100%  \n",
       "3        1.0  80-100%  \n",
       "4       -1.0      NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>Helpful %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th>%upvote</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">1</th>\n",
       "      <th>Empty</th>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "      <td>8060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "      <td>2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "      <td>4649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "      <td>6586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>5838</td>\n",
       "      <td>5838</td>\n",
       "      <td>5838</td>\n",
       "      <td>5836</td>\n",
       "      <td>5838</td>\n",
       "      <td>5838</td>\n",
       "      <td>5838</td>\n",
       "      <td>5838</td>\n",
       "      <td>5838</td>\n",
       "      <td>5838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "      <td>12531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th>Empty</th>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "      <td>4234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "      <td>737</td>\n",
       "      <td>762</td>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "      <td>1618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "      <td>3051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "      <td>7014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">3</th>\n",
       "      <th>Empty</th>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "      <td>5062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "      <td>1506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "      <td>3384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "      <td>2754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>11037</td>\n",
       "      <td>11037</td>\n",
       "      <td>11037</td>\n",
       "      <td>11037</td>\n",
       "      <td>11037</td>\n",
       "      <td>11037</td>\n",
       "      <td>11037</td>\n",
       "      <td>11036</td>\n",
       "      <td>11037</td>\n",
       "      <td>11037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">4</th>\n",
       "      <th>Empty</th>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "      <td>4780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "      <td>2941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "      <td>26707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">5</th>\n",
       "      <th>Empty</th>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "      <td>11638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "      <td>2275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "      <td>10312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "      <td>11060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>140661</td>\n",
       "      <td>140661</td>\n",
       "      <td>140661</td>\n",
       "      <td>140659</td>\n",
       "      <td>140661</td>\n",
       "      <td>140661</td>\n",
       "      <td>140661</td>\n",
       "      <td>140661</td>\n",
       "      <td>140661</td>\n",
       "      <td>140661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Id  ProductId  UserId  ProfileName  HelpfulnessNumerator  \\\n",
       "Score %upvote                                                                 \n",
       "1     Empty      8060       8060    8060         8060                  8060   \n",
       "      0-20%      2338       2338    2338         2338                  2338   \n",
       "      20-40%     4649       4649    4649         4649                  4649   \n",
       "      40-60%     6586       6586    6586         6586                  6586   \n",
       "      60-80%     5838       5838    5838         5836                  5838   \n",
       "      80-100%   12531      12531   12531        12531                 12531   \n",
       "2     Empty      4234       4234    4234         4234                  4234   \n",
       "      0-20%       762        762     762          762                   762   \n",
       "      20-40%     1618       1618    1618         1618                  1618   \n",
       "      40-60%     3051       3051    3051         3051                  3051   \n",
       "      60-80%     2486       2486    2486         2486                  2486   \n",
       "      80-100%    7014       7014    7014         7014                  7014   \n",
       "3     Empty      5062       5062    5062         5062                  5062   \n",
       "      0-20%       474        474     474          474                   474   \n",
       "      20-40%     1506       1506    1506         1506                  1506   \n",
       "      40-60%     3384       3384    3384         3384                  3384   \n",
       "      60-80%     2754       2754    2754         2754                  2754   \n",
       "      80-100%   11037      11037   11037        11037                 11037   \n",
       "4     Empty      4780       4780    4780         4780                  4780   \n",
       "      0-20%       116        116     116          116                   116   \n",
       "      20-40%      909        909     909          909                   909   \n",
       "      40-60%     3185       3185    3185         3185                  3185   \n",
       "      60-80%     2941       2941    2941         2941                  2941   \n",
       "      80-100%   26707      26707   26707        26707                 26707   \n",
       "5     Empty     11638      11638   11638        11638                 11638   \n",
       "      0-20%       432        432     432          432                   432   \n",
       "      20-40%     2275       2275    2275         2275                  2275   \n",
       "      40-60%    10312      10312   10312        10312                 10312   \n",
       "      60-80%    11060      11060   11060        11060                 11060   \n",
       "      80-100%  140661     140661  140661       140659                140661   \n",
       "\n",
       "               HelpfulnessDenominator    Time  Summary    Text  Helpful %  \n",
       "Score %upvote                                                              \n",
       "1     Empty                      8060    8060     8060    8060       8060  \n",
       "      0-20%                      2338    2338     2338    2338       2338  \n",
       "      20-40%                     4649    4649     4649    4649       4649  \n",
       "      40-60%                     6586    6586     6586    6586       6586  \n",
       "      60-80%                     5838    5838     5838    5838       5838  \n",
       "      80-100%                   12531   12531    12531   12531      12531  \n",
       "2     Empty                      4234    4234     4234    4234       4234  \n",
       "      0-20%                       762     762      737     762        762  \n",
       "      20-40%                     1618    1618     1618    1618       1618  \n",
       "      40-60%                     3051    3051     3051    3051       3051  \n",
       "      60-80%                     2486    2486     2486    2486       2486  \n",
       "      80-100%                    7014    7014     7014    7014       7014  \n",
       "3     Empty                      5062    5062     5062    5062       5062  \n",
       "      0-20%                       474     474      474     474        474  \n",
       "      20-40%                     1506    1506     1506    1506       1506  \n",
       "      40-60%                     3384    3384     3384    3384       3384  \n",
       "      60-80%                     2754    2754     2754    2754       2754  \n",
       "      80-100%                   11037   11037    11036   11037      11037  \n",
       "4     Empty                      4780    4780     4780    4780       4780  \n",
       "      0-20%                       116     116      116     116        116  \n",
       "      20-40%                      909     909      909     909        909  \n",
       "      40-60%                     3185    3185     3185    3185       3185  \n",
       "      60-80%                     2941    2941     2941    2941       2941  \n",
       "      80-100%                   26707   26707    26707   26707      26707  \n",
       "5     Empty                     11638   11638    11638   11638      11638  \n",
       "      0-20%                       432     432      432     432        432  \n",
       "      20-40%                     2275    2275     2275    2275       2275  \n",
       "      40-60%                    10312   10312    10312   10312      10312  \n",
       "      60-80%                    11060   11060    11060   11060      11060  \n",
       "      80-100%                  140661  140661   140661  140661     140661  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Score', '%upvote']).agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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></th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th>%upvote</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">1</th>\n",
       "      <th>Empty</th>\n",
       "      <td>8060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>4649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>6586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>5838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>12531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2</th>\n",
       "      <th>Empty</th>\n",
       "      <td>4234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>1618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>3051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>2486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>7014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">3</th>\n",
       "      <th>Empty</th>\n",
       "      <td>5062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>1506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>3384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>2754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>11037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">4</th>\n",
       "      <th>Empty</th>\n",
       "      <td>4780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>3185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>2941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>26707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">5</th>\n",
       "      <th>Empty</th>\n",
       "      <td>11638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>2275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>10312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>11060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>140661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Id\n",
       "Score %upvote        \n",
       "1     Empty      8060\n",
       "      0-20%      2338\n",
       "      20-40%     4649\n",
       "      40-60%     6586\n",
       "      60-80%     5838\n",
       "      80-100%   12531\n",
       "2     Empty      4234\n",
       "      0-20%       762\n",
       "      20-40%     1618\n",
       "      40-60%     3051\n",
       "      60-80%     2486\n",
       "      80-100%    7014\n",
       "3     Empty      5062\n",
       "      0-20%       474\n",
       "      20-40%     1506\n",
       "      40-60%     3384\n",
       "      60-80%     2754\n",
       "      80-100%   11037\n",
       "4     Empty      4780\n",
       "      0-20%       116\n",
       "      20-40%      909\n",
       "      40-60%     3185\n",
       "      60-80%     2941\n",
       "      80-100%   26707\n",
       "5     Empty     11638\n",
       "      0-20%       432\n",
       "      20-40%     2275\n",
       "      40-60%    10312\n",
       "      60-80%    11060\n",
       "      80-100%  140661"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Score', '%upvote']).agg({'Id':'count'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Score</th>\n",
       "      <th>%upvote</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Empty</td>\n",
       "      <td>8060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0-20%</td>\n",
       "      <td>2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>20-40%</td>\n",
       "      <td>4649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>40-60%</td>\n",
       "      <td>6586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>60-80%</td>\n",
       "      <td>5838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score %upvote    Id\n",
       "0      1   Empty  8060\n",
       "1      1   0-20%  2338\n",
       "2      1  20-40%  4649\n",
       "3      1  40-60%  6586\n",
       "4      1  60-80%  5838"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s=df.groupby(['Score', '%upvote']).agg({'Id':'count'}).reset_index()\n",
    "df_s.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Pivot Table For Better Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>%upvote</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Empty</th>\n",
       "      <td>8060</td>\n",
       "      <td>4234</td>\n",
       "      <td>5062</td>\n",
       "      <td>4780</td>\n",
       "      <td>11638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0-20%</th>\n",
       "      <td>2338</td>\n",
       "      <td>762</td>\n",
       "      <td>474</td>\n",
       "      <td>116</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-40%</th>\n",
       "      <td>4649</td>\n",
       "      <td>1618</td>\n",
       "      <td>1506</td>\n",
       "      <td>909</td>\n",
       "      <td>2275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-60%</th>\n",
       "      <td>6586</td>\n",
       "      <td>3051</td>\n",
       "      <td>3384</td>\n",
       "      <td>3185</td>\n",
       "      <td>10312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-80%</th>\n",
       "      <td>5838</td>\n",
       "      <td>2486</td>\n",
       "      <td>2754</td>\n",
       "      <td>2941</td>\n",
       "      <td>11060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-100%</th>\n",
       "      <td>12531</td>\n",
       "      <td>7014</td>\n",
       "      <td>11037</td>\n",
       "      <td>26707</td>\n",
       "      <td>140661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id                            \n",
       "Score        1     2      3      4       5\n",
       "%upvote                                   \n",
       "Empty     8060  4234   5062   4780   11638\n",
       "0-20%     2338   762    474    116     432\n",
       "20-40%    4649  1618   1506    909    2275\n",
       "40-60%    6586  3051   3384   3185   10312\n",
       "60-80%    5838  2486   2754   2941   11060\n",
       "80-100%  12531  7014  11037  26707  140661"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s.pivot(index='%upvote',columns='Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Heatmap For Better Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'How helpful users find among user scores')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.heatmap(df_s.pivot(index='%upvote',columns='Score'),annot=True)\n",
    "plt.title('How helpful users find among user scores')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key message from above:\n",
    "\n",
    "### Reviews are skewed towards positive.\n",
    "### More than half of the reviews are with zero votes\n",
    "### Many people agree with score 5 reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(568454, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>ProfileName</th>\n",
       "      <th>HelpfulnessNumerator</th>\n",
       "      <th>HelpfulnessDenominator</th>\n",
       "      <th>Score</th>\n",
       "      <th>Time</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>Helpful %</th>\n",
       "      <th>%upvote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>B001E4KFG0</td>\n",
       "      <td>A3SGXH7AUHU8GW</td>\n",
       "      <td>delmartian</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1303862400</td>\n",
       "      <td>Good Quality Dog Food</td>\n",
       "      <td>I have bought several of the Vitality canned d...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80-100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B00813GRG4</td>\n",
       "      <td>A1D87F6ZCVE5NK</td>\n",
       "      <td>dll pa</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1346976000</td>\n",
       "      <td>Not as Advertised</td>\n",
       "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>B000LQOCH0</td>\n",
       "      <td>ABXLMWJIXXAIN</td>\n",
       "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1219017600</td>\n",
       "      <td>\"Delight\" says it all</td>\n",
       "      <td>This is a confection that has been around a fe...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80-100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>B000UA0QIQ</td>\n",
       "      <td>A395BORC6FGVXV</td>\n",
       "      <td>Karl</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1307923200</td>\n",
       "      <td>Cough Medicine</td>\n",
       "      <td>If you are looking for the secret ingredient i...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80-100%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>B006K2ZZ7K</td>\n",
       "      <td>A1UQRSCLF8GW1T</td>\n",
       "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1350777600</td>\n",
       "      <td>Great taffy</td>\n",
       "      <td>Great taffy at a great price.  There was a wid...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   ProductId          UserId                      ProfileName  \\\n",
       "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
       "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
       "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
       "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
       "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
       "\n",
       "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
       "0                     1                       1      5  1303862400   \n",
       "1                     0                       0      1  1346976000   \n",
       "2                     1                       1      4  1219017600   \n",
       "3                     3                       3      2  1307923200   \n",
       "4                     0                       0      5  1350777600   \n",
       "\n",
       "                 Summary                                               Text  \\\n",
       "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
       "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
       "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
       "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
       "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
       "\n",
       "   Helpful %  %upvote  \n",
       "0        1.0  80-100%  \n",
       "1       -1.0      NaN  \n",
       "2        1.0  80-100%  \n",
       "3        1.0  80-100%  \n",
       "4       -1.0      NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 1, 4, 2, 3])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 1, 4, 2])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[df['Score'] != 3]\n",
    "X = df2['Text']\n",
    "y_dict = {1:0, 2:0, 4:1, 5:1}\n",
    "y = df2['Score'].map(y_dict)\n",
    "df2['Score'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting text into vectors using NLP (Bag of Words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c = CountVectorizer(stop_words = 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_c = c.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features: 114969\n"
     ]
    }
   ],
   "source": [
    "print('features: {}'.format(X_c.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " train records: 394360\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_c, y)\n",
    "print(' train records: {}'.format(X_train.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy: 0.9354298842180535\n"
     ]
    }
   ],
   "source": [
    "ml =log.fit(X_train, y_train)\n",
    "acc = ml.score(X_test, y_test)\n",
    "print ('Model Accuracy: {}'.format(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetching Top 20 Positive & Top 20 negative words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['00',\n",
       " '000',\n",
       " '0000',\n",
       " '000001',\n",
       " '00001',\n",
       " '000013',\n",
       " '0000soo',\n",
       " '0001',\n",
       " '000111052',\n",
       " '0002251337',\n",
       " '0003',\n",
       " '0004',\n",
       " '000iu',\n",
       " '000kwh',\n",
       " '000mg',\n",
       " '000mi',\n",
       " '000s',\n",
       " '000su',\n",
       " '000usd',\n",
       " '000v',\n",
       " '001',\n",
       " '00100',\n",
       " '00127',\n",
       " '00128',\n",
       " '00129',\n",
       " '00130',\n",
       " '00131',\n",
       " '00132',\n",
       " '00134',\n",
       " '00136',\n",
       " '00139',\n",
       " '001bru',\n",
       " '002',\n",
       " '0020100604',\n",
       " '00202',\n",
       " '00227',\n",
       " '0023',\n",
       " '003',\n",
       " '004',\n",
       " '00493',\n",
       " '005',\n",
       " '00533',\n",
       " '0060187654',\n",
       " '0060721855',\n",
       " '0060928115',\n",
       " '0060959584',\n",
       " '0061658197',\n",
       " '006176793x',\n",
       " '0067575986',\n",
       " '0069615',\n",
       " '007',\n",
       " '00703',\n",
       " '00704',\n",
       " '0071468633',\n",
       " '0071477845',\n",
       " '0071486011',\n",
       " '0071499849',\n",
       " '008',\n",
       " '0099',\n",
       " '00a',\n",
       " '00am',\n",
       " '00b',\n",
       " '00gr',\n",
       " '00lb',\n",
       " '00m',\n",
       " '00pm',\n",
       " '00s',\n",
       " '00something',\n",
       " '00z',\n",
       " '01',\n",
       " '010',\n",
       " '0100',\n",
       " '01014',\n",
       " '01069',\n",
       " '011',\n",
       " '012',\n",
       " '013',\n",
       " '01317',\n",
       " '01318',\n",
       " '014',\n",
       " '0140444254',\n",
       " '0140446680',\n",
       " '0143114964',\n",
       " '014mg',\n",
       " '015',\n",
       " '017',\n",
       " '0174',\n",
       " '018',\n",
       " '0188',\n",
       " '019',\n",
       " '01915',\n",
       " '0199232768',\n",
       " '0199535892',\n",
       " '0199536066',\n",
       " '02',\n",
       " '020',\n",
       " '02027c',\n",
       " '02043',\n",
       " '02115',\n",
       " '022313',\n",
       " '022413',\n",
       " '022813',\n",
       " '023',\n",
       " '024600017558',\n",
       " '025',\n",
       " '025913',\n",
       " '025968680a',\n",
       " '02604',\n",
       " '026220',\n",
       " '027',\n",
       " '0273',\n",
       " '02762',\n",
       " '028',\n",
       " '02jan2012',\n",
       " '02oq8o2bbaxaacmkmmmf4tby',\n",
       " '03',\n",
       " '030',\n",
       " '0300',\n",
       " '030113',\n",
       " '0303',\n",
       " '030712',\n",
       " '030713',\n",
       " '0307261581',\n",
       " '0307460169',\n",
       " '0307474291',\n",
       " '0307720497',\n",
       " '0307887960',\n",
       " '0307949435',\n",
       " '030813',\n",
       " '031231521x',\n",
       " '0312362919',\n",
       " '0312377428',\n",
       " '0312545525',\n",
       " '0312629818',\n",
       " '0312649940',\n",
       " '031513',\n",
       " '0316074284',\n",
       " '031612737x',\n",
       " '0316129445',\n",
       " '031613',\n",
       " '031613290x',\n",
       " '0316735507',\n",
       " '032',\n",
       " '032513',\n",
       " '032712',\n",
       " '032813',\n",
       " '034',\n",
       " '03430',\n",
       " '034549458x',\n",
       " '035',\n",
       " '03510',\n",
       " '035273',\n",
       " '0373',\n",
       " '0373892322',\n",
       " '0373892349',\n",
       " '03755',\n",
       " '0375757996',\n",
       " '0376024933',\n",
       " '0377',\n",
       " '038',\n",
       " '038542017x',\n",
       " '03885',\n",
       " '039',\n",
       " '0393066304',\n",
       " '0393070212',\n",
       " '03jan12',\n",
       " '03oz',\n",
       " '04',\n",
       " '040',\n",
       " '040413',\n",
       " '040513',\n",
       " '040612',\n",
       " '041113',\n",
       " '041213',\n",
       " '041224721302',\n",
       " '041313',\n",
       " '041913',\n",
       " '042013',\n",
       " '042113',\n",
       " '042513',\n",
       " '0425204138',\n",
       " '042608460503',\n",
       " '042813',\n",
       " '043',\n",
       " '0439',\n",
       " '044',\n",
       " '044000025298',\n",
       " '0440129613',\n",
       " '0446675385',\n",
       " '04472700',\n",
       " '045',\n",
       " '0451155505',\n",
       " '0451184963',\n",
       " '0452285801',\n",
       " '0461',\n",
       " '04691',\n",
       " '0470041153',\n",
       " '0470194960',\n",
       " '047028188x',\n",
       " '0470425245',\n",
       " '0470440856',\n",
       " '0470913029',\n",
       " '0470916575',\n",
       " '0471168572',\n",
       " '0471202711',\n",
       " '0472066978',\n",
       " '0475',\n",
       " '048',\n",
       " '04820',\n",
       " '04830',\n",
       " '049',\n",
       " '04mg',\n",
       " '04oz',\n",
       " '05',\n",
       " '050',\n",
       " '050411',\n",
       " '050913',\n",
       " '050oz',\n",
       " '051',\n",
       " '051013',\n",
       " '051213',\n",
       " '05140',\n",
       " '051513',\n",
       " '0517452502',\n",
       " '051813',\n",
       " '051913',\n",
       " '052',\n",
       " '0520269926',\n",
       " '052100071336',\n",
       " '052213',\n",
       " '052313',\n",
       " '052413',\n",
       " '052913',\n",
       " '053229',\n",
       " '0545044251',\n",
       " '055',\n",
       " '0553380788',\n",
       " '056',\n",
       " '05608',\n",
       " '0563214546',\n",
       " '05715',\n",
       " '059',\n",
       " '0595318452',\n",
       " '05oz',\n",
       " '06',\n",
       " '060',\n",
       " '060113',\n",
       " '060713',\n",
       " '060813',\n",
       " '061',\n",
       " '0610',\n",
       " '061113',\n",
       " '061313',\n",
       " '061413',\n",
       " '061513',\n",
       " '062',\n",
       " '062213',\n",
       " '0625',\n",
       " '06254',\n",
       " '062613',\n",
       " '062713',\n",
       " '062813',\n",
       " '063',\n",
       " '0631',\n",
       " '06312',\n",
       " '064',\n",
       " '065',\n",
       " '066',\n",
       " '067',\n",
       " '0674005392',\n",
       " '0679767959',\n",
       " '06799',\n",
       " '0684800012',\n",
       " '0688',\n",
       " '0688092616',\n",
       " '0688157920',\n",
       " '06g',\n",
       " '06l33t',\n",
       " '06oz',\n",
       " '06sep11',\n",
       " '06sep12',\n",
       " '07',\n",
       " '070',\n",
       " '07002',\n",
       " '07003',\n",
       " '07036',\n",
       " '070590080010',\n",
       " '07065',\n",
       " '07078',\n",
       " '0709',\n",
       " '070913',\n",
       " '0712',\n",
       " '071313',\n",
       " '071813',\n",
       " '071913',\n",
       " '0735814414',\n",
       " '0738213861',\n",
       " '073821423x',\n",
       " '0738551856',\n",
       " '0738713597',\n",
       " '074',\n",
       " '0740765353',\n",
       " '0740779729',\n",
       " '0740784137',\n",
       " '0743223225',\n",
       " '0743246268',\n",
       " '0743292545',\n",
       " '0745',\n",
       " '075',\n",
       " '0761135812',\n",
       " '076114479x',\n",
       " '0761160981',\n",
       " '0762426020',\n",
       " '0762435259',\n",
       " '0764135775',\n",
       " '0764544659',\n",
       " '0764559176',\n",
       " '0764569112',\n",
       " '0764578650',\n",
       " '07652',\n",
       " '0767908236',\n",
       " '0768ppm',\n",
       " '0777',\n",
       " '0785383239',\n",
       " '078799622x',\n",
       " '0790700506',\n",
       " '0790742780',\n",
       " '0792165055',\n",
       " '07m2402',\n",
       " '08',\n",
       " '080',\n",
       " '0804835942',\n",
       " '0805089586',\n",
       " '0806',\n",
       " '080942925x',\n",
       " '0810',\n",
       " '0811845354',\n",
       " '0811867811',\n",
       " '0816524882',\n",
       " '0816650187',\n",
       " '08204',\n",
       " '08218118960519973573719117007586000668448380401921',\n",
       " '0825305845',\n",
       " '083',\n",
       " '083613561x',\n",
       " '083619263x',\n",
       " '0836192648',\n",
       " '0836194942',\n",
       " '084',\n",
       " '0848734378',\n",
       " '0848806832',\n",
       " '085',\n",
       " '0865477043',\n",
       " '0872203492',\n",
       " '088',\n",
       " '0880015047',\n",
       " '08873',\n",
       " '089',\n",
       " '089036241397',\n",
       " '089036241434',\n",
       " '0892810998',\n",
       " '0892817267',\n",
       " '0894803123',\n",
       " '0895264641',\n",
       " '08m07c0',\n",
       " '08m13c8',\n",
       " '08oz',\n",
       " '09',\n",
       " '090',\n",
       " '090214',\n",
       " '090227921',\n",
       " '091',\n",
       " '091012',\n",
       " '0913',\n",
       " '0913990604',\n",
       " '092',\n",
       " '09244791666',\n",
       " '0929',\n",
       " '0929173252',\n",
       " '0929173309',\n",
       " '093',\n",
       " '0937',\n",
       " '0939165422',\n",
       " '0939165562',\n",
       " '0941599604',\n",
       " '0943151201',\n",
       " '0947',\n",
       " '095',\n",
       " '0961959533',\n",
       " '0963796836',\n",
       " '0967089735',\n",
       " '0967089751',\n",
       " '0969276818',\n",
       " '097',\n",
       " '0970245823',\n",
       " '097221643x',\n",
       " '0972722726',\n",
       " '0976896931',\n",
       " '0979201802',\n",
       " '0979676460',\n",
       " '097977280x',\n",
       " '0979780802',\n",
       " '098',\n",
       " '0980137659',\n",
       " '0981623506',\n",
       " '098214251x',\n",
       " '0982207700',\n",
       " '0982207786',\n",
       " '0982428669',\n",
       " '0982428693',\n",
       " '098253311x',\n",
       " '0982708319',\n",
       " '09mg',\n",
       " '0arty',\n",
       " '0c',\n",
       " '0cm',\n",
       " '0d',\n",
       " '0f',\n",
       " '0g',\n",
       " '0g12',\n",
       " '0g5',\n",
       " '0gr',\n",
       " '0grams',\n",
       " '0iu',\n",
       " '0l',\n",
       " '0lb',\n",
       " '0lp',\n",
       " '0mcg',\n",
       " '0mg',\n",
       " '0mg0',\n",
       " '0n',\n",
       " '0ne',\n",
       " '0nly',\n",
       " '0o',\n",
       " '0on',\n",
       " '0oz',\n",
       " '0p',\n",
       " '0pen',\n",
       " '0ppm',\n",
       " '0r',\n",
       " '0ther',\n",
       " '0unce',\n",
       " '0ver',\n",
       " '0xk6hzpjrkaed855hewp',\n",
       " '0z',\n",
       " '10',\n",
       " '100',\n",
       " '1000',\n",
       " '10000',\n",
       " '100000',\n",
       " '1000000',\n",
       " '1000000000000000000000000',\n",
       " '100001',\n",
       " '10000km',\n",
       " '10000x',\n",
       " '1000g',\n",
       " '1000grams',\n",
       " '1000iu',\n",
       " '1000mg',\n",
       " '1000ml',\n",
       " '1000mph',\n",
       " '1000s',\n",
       " '1000th',\n",
       " '1000w',\n",
       " '1000watt',\n",
       " '1000x',\n",
       " '1001',\n",
       " '10010',\n",
       " '1002',\n",
       " '10041224721309',\n",
       " '100432',\n",
       " '1007',\n",
       " '100cal',\n",
       " '100calories',\n",
       " '100cals',\n",
       " '100count',\n",
       " '100ct',\n",
       " '100ea',\n",
       " '100f',\n",
       " '100ft',\n",
       " '100g',\n",
       " '100gm',\n",
       " '100gms',\n",
       " '100gr',\n",
       " '100gram',\n",
       " '100k',\n",
       " '100kg',\n",
       " '100lb',\n",
       " '100lbs',\n",
       " '100lu',\n",
       " '100m',\n",
       " '100mcg',\n",
       " '100mg',\n",
       " '100mile',\n",
       " '100ml',\n",
       " '100mph',\n",
       " '100o',\n",
       " '100pc',\n",
       " '100piece',\n",
       " '100pk',\n",
       " '100plus',\n",
       " '100psi',\n",
       " '100s',\n",
       " '100sheet',\n",
       " '100th',\n",
       " '100ug',\n",
       " '100w',\n",
       " '100watt',\n",
       " '100x',\n",
       " '100x3',\n",
       " '100year',\n",
       " '100º',\n",
       " '101',\n",
       " '1010',\n",
       " '101012',\n",
       " '1010mg',\n",
       " '1011',\n",
       " '1012',\n",
       " '10134',\n",
       " '10145camp09',\n",
       " '1015',\n",
       " '1017',\n",
       " '101812',\n",
       " '101mg',\n",
       " '102',\n",
       " '1020',\n",
       " '1020mg',\n",
       " '1021',\n",
       " '102308',\n",
       " '1024',\n",
       " '1025g',\n",
       " '102612',\n",
       " '102lbs',\n",
       " '103',\n",
       " '1030',\n",
       " '10312',\n",
       " '1035',\n",
       " '1035985',\n",
       " '104',\n",
       " '1040mg',\n",
       " '1047',\n",
       " '104oz',\n",
       " '105',\n",
       " '1050',\n",
       " '10504',\n",
       " '1050mgs',\n",
       " '1054',\n",
       " '1056',\n",
       " '105f',\n",
       " '105g',\n",
       " '105lbs',\n",
       " '105mg',\n",
       " '105º',\n",
       " '106',\n",
       " '10620',\n",
       " '107',\n",
       " '1072',\n",
       " '1073',\n",
       " '1074',\n",
       " '1076',\n",
       " '1077',\n",
       " '1078',\n",
       " '1079',\n",
       " '108',\n",
       " '1080',\n",
       " '1080p',\n",
       " '1082',\n",
       " '1083',\n",
       " '1084',\n",
       " '1085',\n",
       " '1086',\n",
       " '1087',\n",
       " '1088',\n",
       " '1089',\n",
       " '108g',\n",
       " '108mg',\n",
       " '109',\n",
       " '1090',\n",
       " '1091',\n",
       " '1092',\n",
       " '1093',\n",
       " '1095',\n",
       " '10954012',\n",
       " '1096',\n",
       " '1099',\n",
       " '10_',\n",
       " '10a',\n",
       " '10am',\n",
       " '10b',\n",
       " '10bucks',\n",
       " '10c',\n",
       " '10can',\n",
       " '10cents',\n",
       " '10days',\n",
       " '10ft',\n",
       " '10g',\n",
       " '10gm',\n",
       " '10gr',\n",
       " '10gram',\n",
       " '10grams',\n",
       " '10grms',\n",
       " '10hr',\n",
       " '10hrs',\n",
       " '10ib',\n",
       " '10in',\n",
       " '10ish',\n",
       " '10jul18',\n",
       " '10k',\n",
       " '10kg',\n",
       " '10lb',\n",
       " '10lbs',\n",
       " '10m',\n",
       " '10mar12',\n",
       " '10mcg',\n",
       " '10mg',\n",
       " '10mg0',\n",
       " '10mgs',\n",
       " '10min',\n",
       " '10mins',\n",
       " '10minutes',\n",
       " '10ml',\n",
       " '10mo',\n",
       " '10month',\n",
       " '10months',\n",
       " '10mos',\n",
       " '10oz',\n",
       " '10p',\n",
       " '10pc',\n",
       " '10pcs',\n",
       " '10pk',\n",
       " '10pks',\n",
       " '10pm',\n",
       " '10pound',\n",
       " '10s',\n",
       " '10sec',\n",
       " '10seconds',\n",
       " '10tbsp',\n",
       " '10th',\n",
       " '10ths',\n",
       " '10tiny',\n",
       " '10w',\n",
       " '10wks',\n",
       " '10x',\n",
       " '10x10',\n",
       " '10x12x12',\n",
       " '10x5',\n",
       " '10x7x3',\n",
       " '10year',\n",
       " '10years',\n",
       " '10yo',\n",
       " '10yr',\n",
       " '10yrs',\n",
       " '10º',\n",
       " '11',\n",
       " '110',\n",
       " '1100',\n",
       " '1100mg',\n",
       " '1100w',\n",
       " '1100yrs',\n",
       " '1101',\n",
       " '1103',\n",
       " '1105',\n",
       " '110576',\n",
       " '110583',\n",
       " '1106',\n",
       " '110712',\n",
       " '110812',\n",
       " '110912',\n",
       " '110lb',\n",
       " '110lbs',\n",
       " '110m3',\n",
       " '110mg',\n",
       " '110v',\n",
       " '111',\n",
       " '11105',\n",
       " '1111',\n",
       " '1112',\n",
       " '111412',\n",
       " '1116',\n",
       " '11164',\n",
       " '11173407',\n",
       " '1118',\n",
       " '111812',\n",
       " '112',\n",
       " '1120',\n",
       " '1120mg',\n",
       " '1121',\n",
       " '1122',\n",
       " '1123',\n",
       " '11231',\n",
       " '11279p',\n",
       " '112812',\n",
       " '112g',\n",
       " '113',\n",
       " '1130',\n",
       " '1130am',\n",
       " '11370',\n",
       " '113g',\n",
       " '114',\n",
       " '1140091',\n",
       " '114159',\n",
       " '114316',\n",
       " '115',\n",
       " '1150',\n",
       " '1150mg',\n",
       " '1152',\n",
       " '1155',\n",
       " '1157',\n",
       " '115g',\n",
       " '115iu',\n",
       " '115mg',\n",
       " '115mg5',\n",
       " '115s',\n",
       " '116',\n",
       " '11629',\n",
       " '1165',\n",
       " '1167',\n",
       " '117',\n",
       " '11713',\n",
       " '117lbs',\n",
       " '118',\n",
       " '1180mg',\n",
       " '1186',\n",
       " '118lbs',\n",
       " '119',\n",
       " '1190mg',\n",
       " '1197',\n",
       " '1198',\n",
       " '11am',\n",
       " '11aug12',\n",
       " '11g',\n",
       " '11gm',\n",
       " '11gms',\n",
       " '11gr',\n",
       " '11gs',\n",
       " '11in',\n",
       " '11ish',\n",
       " '11k',\n",
       " '11l',\n",
       " '11lb',\n",
       " '11lbs',\n",
       " '11m',\n",
       " '11mg',\n",
       " '11mo',\n",
       " '11month',\n",
       " '11mths',\n",
       " '11ounces',\n",
       " '11oz',\n",
       " '11pm',\n",
       " '11s',\n",
       " '11t',\n",
       " '11th',\n",
       " '11wks',\n",
       " '11x13',\n",
       " '11year',\n",
       " '11yo',\n",
       " '11yr',\n",
       " '11yrs',\n",
       " '12',\n",
       " '120',\n",
       " '1200',\n",
       " '1200cal',\n",
       " '1200candy',\n",
       " '1200mg',\n",
       " '1200v',\n",
       " '1200w',\n",
       " '1200x',\n",
       " '120112',\n",
       " '120312',\n",
       " '1203123',\n",
       " '120322fs1d',\n",
       " '120374',\n",
       " '1206',\n",
       " '1208084',\n",
       " '1209',\n",
       " '120c28',\n",
       " '120cals',\n",
       " '120ct',\n",
       " '120g',\n",
       " '120hz',\n",
       " '120ish',\n",
       " '120lb',\n",
       " '120lbs',\n",
       " '120mg',\n",
       " '120ml',\n",
       " '120s',\n",
       " '120th',\n",
       " '120v',\n",
       " '120z',\n",
       " '121',\n",
       " '1219bestby05dec2012',\n",
       " '122',\n",
       " '1220',\n",
       " '1220mg',\n",
       " '12290',\n",
       " '122nd',\n",
       " '123',\n",
       " '1230',\n",
       " '1234',\n",
       " '123411286',\n",
       " '12356',\n",
       " '124',\n",
       " '1240',\n",
       " '12424',\n",
       " '12442909',\n",
       " '1245',\n",
       " '1246',\n",
       " '124th',\n",
       " '125',\n",
       " '1250',\n",
       " '1250ml',\n",
       " '125g',\n",
       " '125lbs',\n",
       " '125mg',\n",
       " '125th',\n",
       " '125v',\n",
       " '126',\n",
       " '1260255555',\n",
       " '1260257694',\n",
       " '1266273814',\n",
       " '127',\n",
       " '1270744039',\n",
       " '1279',\n",
       " '127lb',\n",
       " '128',\n",
       " '1280',\n",
       " '128077',\n",
       " '128078',\n",
       " '1280mg',\n",
       " '1281996897',\n",
       " '128516',\n",
       " '128522',\n",
       " '128523',\n",
       " '128525',\n",
       " '128541',\n",
       " '1288868059',\n",
       " '1289',\n",
       " '128fl',\n",
       " '128g',\n",
       " '128gm',\n",
       " '128mg',\n",
       " '128oz',\n",
       " '129',\n",
       " '1290',\n",
       " '1290838229',\n",
       " '1290mg',\n",
       " '1291331352',\n",
       " '12931',\n",
       " '1294674431',\n",
       " '1297',\n",
       " '1297861877',\n",
       " '1298166499',\n",
       " '12am',\n",
       " '12cal',\n",
       " '12cans',\n",
       " '12ct',\n",
       " '12cup',\n",
       " '12cups',\n",
       " '12de07',\n",
       " '12ea',\n",
       " '12fl',\n",
       " '12g',\n",
       " '12gm',\n",
       " '12gms',\n",
       " '12gprotein',\n",
       " '12gram',\n",
       " '12grams',\n",
       " '12gs',\n",
       " '12h2o',\n",
       " '12hr',\n",
       " '12hrs',\n",
       " '12in',\n",
       " '12inch',\n",
       " '12ish',\n",
       " '12jul12',\n",
       " '12jul2011',\n",
       " '12k',\n",
       " '12lb',\n",
       " '12lbs',\n",
       " '12m',\n",
       " '12min',\n",
       " '12mo',\n",
       " '12month',\n",
       " '12ounce',\n",
       " '12ox',\n",
       " '12oz',\n",
       " '12ozbottle',\n",
       " '12ozs',\n",
       " '12pack',\n",
       " '12packages',\n",
       " '12packs',\n",
       " '12pak',\n",
       " '12pk',\n",
       " '12pks',\n",
       " '12pm',\n",
       " '12pounds',\n",
       " '12qt',\n",
       " '12qts',\n",
       " '12qty',\n",
       " '12s',\n",
       " '12seconds',\n",
       " '12th',\n",
       " '12v',\n",
       " '12w',\n",
       " '12wk',\n",
       " '12x',\n",
       " '12x10',\n",
       " '12x10x2',\n",
       " '12x12',\n",
       " '12x14',\n",
       " '12x16',\n",
       " '12x17',\n",
       " '12x18x6',\n",
       " '12x24',\n",
       " '12x25',\n",
       " '12x2oz',\n",
       " '12x32',\n",
       " '12x4',\n",
       " '12x5',\n",
       " '12x8',\n",
       " '12x9x4',\n",
       " '12years',\n",
       " '12yo',\n",
       " '12yr',\n",
       " '12yrs',\n",
       " '12½',\n",
       " '13',\n",
       " '130',\n",
       " '1300',\n",
       " '1300131716',\n",
       " '1300373343',\n",
       " '1300mg',\n",
       " '1300s',\n",
       " '1300w',\n",
       " '1300watt',\n",
       " '1302206977',\n",
       " '1306',\n",
       " '1308',\n",
       " '1308_12',\n",
       " '130cal',\n",
       " '130calories',\n",
       " '130db',\n",
       " '130g',\n",
       " '130ish',\n",
       " '130l',\n",
       " '130lb',\n",
       " '130lbs',\n",
       " '130mg',\n",
       " '130x',\n",
       " '131',\n",
       " '1310712',\n",
       " '1316',\n",
       " '132',\n",
       " '13214',\n",
       " '1325488826',\n",
       " '1326377612',\n",
       " '1328216581',\n",
       " '1328216609',\n",
       " '1328216637',\n",
       " '1328639422',\n",
       " '13289',\n",
       " '1329666521',\n",
       " '132mgs',\n",
       " '133',\n",
       " '1330',\n",
       " '1331447807',\n",
       " '1333mg',\n",
       " '1334249885',\n",
       " '1334324776',\n",
       " '1334324953',\n",
       " '1334767059',\n",
       " '1334882068',\n",
       " '1335572191',\n",
       " '1336984170',\n",
       " '1337',\n",
       " '1337202670',\n",
       " '1339675640',\n",
       " '133g',\n",
       " '133mg',\n",
       " '134',\n",
       " '1341338906',\n",
       " '1342132011',\n",
       " '1342563776',\n",
       " '13432',\n",
       " '1344360665',\n",
       " '1344600644',\n",
       " '1346527950',\n",
       " '1346955176',\n",
       " '1347903950',\n",
       " '1348333106',\n",
       " '1348518159',\n",
       " '1348706702',\n",
       " '1349143571',\n",
       " '1349906835',\n",
       " '1349906863',\n",
       " '1349919269',\n",
       " '134mg',\n",
       " '135',\n",
       " '1350',\n",
       " '13502',\n",
       " ...]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = c.get_feature_names()\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.421305839151205,\n",
       " -0.2285151669946349,\n",
       " 0.3054843258432471,\n",
       " -0.0023500591019729165,\n",
       " -0.010244638181118627,\n",
       " 0.0,\n",
       " 0.0002865212396455906,\n",
       " -0.005930094615722582,\n",
       " 0.0004659127456342307,\n",
       " 0.000312113583312637,\n",
       " 0.0,\n",
       " -0.00812608142973412,\n",
       " 0.002213805036886768,\n",
       " 0.015445051205313132,\n",
       " -0.14699407372443052,\n",
       " 0.0,\n",
       " 0.0026467310429112993,\n",
       " 0.0,\n",
       " 0.0014455116621380087,\n",
       " 4.339660196667949e-05,\n",
       " 0.08237334734040376,\n",
       " 0.01541232399020022,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " 0.03260484881508814,\n",
       " -0.0003535459622800598,\n",
       " 0.06549842383926104,\n",
       " 0.0001425735709169757,\n",
       " -0.08719736695469206,\n",
       " -0.0006255527816317562,\n",
       " 0.00016141507800337065,\n",
       " 0.0,\n",
       " 0.08716391455133672,\n",
       " 2.5023960606724886e-05,\n",
       " 0.0,\n",
       " 0.009133645802925796,\n",
       " 6.66030368445775e-05,\n",
       " 0.005865218415106347,\n",
       " -0.05729568527703772,\n",
       " 0.0,\n",
       " 0.011062066170037493,\n",
       " 7.849901168374572e-05,\n",
       " 0.0001425735709169757,\n",
       " -0.003441103041869929,\n",
       " 0.00014131814712657848,\n",
       " 0.0005256521207183424,\n",
       " 0.0005256521207183424,\n",
       " 0.0,\n",
       " 8.567429986533531e-05,\n",
       " 8.404209278043757e-06,\n",
       " 0.0012263374120738116,\n",
       " 0.022234307253988655,\n",
       " -0.05462701310371376,\n",
       " 0.006734121201199555,\n",
       " -0.03905977456755893,\n",
       " 0.011860127079946131,\n",
       " 0.0027933394023563285,\n",
       " 1.3229371424203163e-05,\n",
       " 0.0,\n",
       " 0.06481407870271672,\n",
       " 0.0008581556886865774,\n",
       " 0.005571909626053509,\n",
       " -0.006396007361356711,\n",
       " 0.0711453778191144,\n",
       " 0.10141277025416631,\n",
       " 0.003998499075588703,\n",
       " -0.0008675917836671024,\n",
       " -0.0012006100399533186,\n",
       " 0.0009576710030565481,\n",
       " 0.0012289876077486227,\n",
       " -0.000770260460387233,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.008249762143585034,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.013131753693374775,\n",
       " 4.059473821490657e-06,\n",
       " -0.00012368071385092963,\n",
       " 0.003899990533835106,\n",
       " 0.0826737697504245,\n",
       " 0.0005708782162778158,\n",
       " 0.0,\n",
       " -0.00012368071385092963,\n",
       " 0.0,\n",
       " 0.00046269473876869833,\n",
       " 0.0003462514368107959,\n",
       " 0.00012628531354128453,\n",
       " -0.5614148010792809,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0006443972825898349,\n",
       " -0.0008360796226829021,\n",
       " -0.03634587721647648,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " 0.0,\n",
       " 0.053740416279810466,\n",
       " 0.015319434554708066,\n",
       " 0.038723578063166654,\n",
       " 0.0,\n",
       " -0.08719736695469206,\n",
       " 0.0008431820391566294,\n",
       " -0.002449784733712852,\n",
       " 0.0030136593294375373,\n",
       " 0.0004882903252759518,\n",
       " 3.671722235233609e-05,\n",
       " 0.04217059190200322,\n",
       " 0.0,\n",
       " -0.707153062597991,\n",
       " -0.053687834225873886,\n",
       " 0.00010220876501431589,\n",
       " -0.01817293860823824,\n",
       " 0.0,\n",
       " -0.05142630196442719,\n",
       " -0.01817293860823824,\n",
       " 0.00014599502513909832,\n",
       " 0.00015919231530794694,\n",
       " 0.0,\n",
       " 0.0016025497216113074,\n",
       " 0.003048381295845694,\n",
       " 0.0261579670816953,\n",
       " -0.0545188158247157,\n",
       " 0.0,\n",
       " 8.528812921494068e-05,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 7.992088519283642e-05,\n",
       " 0.0001770575792448551,\n",
       " 0.0006443972825898349,\n",
       " 0.0,\n",
       " 0.0003051515722588772,\n",
       " 0.0,\n",
       " -0.01817293860823824,\n",
       " 0.005013276236117603,\n",
       " 0.0,\n",
       " -0.012998520331869262,\n",
       " -0.0545188158247157,\n",
       " 0.021534963130890223,\n",
       " -0.01817293860823824,\n",
       " 0.0028769650672261565,\n",
       " 0.020458081566603076,\n",
       " 0.04181981964719881,\n",
       " -0.0013852719643558651,\n",
       " 0.020405881027250793,\n",
       " 0.001713859140017705,\n",
       " -0.00812608142973412,\n",
       " -0.11586039631255782,\n",
       " 0.0,\n",
       " -0.0015066570923921551,\n",
       " 0.0,\n",
       " 0.0009580303670699092,\n",
       " -0.00812608142973412,\n",
       " 0.0,\n",
       " 0.0004799299122792196,\n",
       " -0.11174265691492026,\n",
       " 0.018865734727044156,\n",
       " 6.273829030336938e-05,\n",
       " -0.03419780185896688,\n",
       " 2.0000345716835075e-05,\n",
       " -0.06645246083603008,\n",
       " -0.31337499635339594,\n",
       " 0.0394082375280379,\n",
       " -0.03634587721647648,\n",
       " -0.01817293860823824,\n",
       " 0.0007034066073188315,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " 0.00227380237573653,\n",
       " -0.01817293860823824,\n",
       " -0.03634587721647648,\n",
       " -0.01817293860823824,\n",
       " -0.0545188158247157,\n",
       " -0.03634587721647648,\n",
       " 0.00017165689714135026,\n",
       " 9.070128718825008e-06,\n",
       " -0.01817293860823824,\n",
       " 0.006147572999211525,\n",
       " -0.0032594681618498857,\n",
       " 0.0014442805810273858,\n",
       " 0.051496396757865305,\n",
       " 0.0,\n",
       " 9.611252719865382e-05,\n",
       " 0.051496396757865305,\n",
       " 0.025853891966351652,\n",
       " -0.15204832455689132,\n",
       " 6.726094696263782e-05,\n",
       " 0.00011876788827310111,\n",
       " 0.0015546350876347295,\n",
       " 0.000626861148973598,\n",
       " -0.1545946641156479,\n",
       " 1.5513989934695962e-05,\n",
       " 0.00036624038508603553,\n",
       " 0.00011876788827310111,\n",
       " 0.0048126077979592854,\n",
       " 0.11286256802352852,\n",
       " 8.370978247266122e-05,\n",
       " 0.0,\n",
       " 0.016579431187714724,\n",
       " 0.030195607963220572,\n",
       " 0.011063789532917057,\n",
       " -0.00012368071385092963,\n",
       " -0.0373201494675496,\n",
       " 0.00018334601436774446,\n",
       " -0.00012368071385092963,\n",
       " 0.023561102886139648,\n",
       " 8.787504671285563e-06,\n",
       " 0.01215152471799619,\n",
       " -0.0004947228554037185,\n",
       " -0.002362478385180963,\n",
       " -0.01817293860823824,\n",
       " 0.038259544253898166,\n",
       " -0.00012368071385092963,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " 0.024934317652181827,\n",
       " -0.01817293860823824,\n",
       " 0.00972501635597532,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " -0.00015249082362041224,\n",
       " 0.0009487836225082138,\n",
       " 0.053740416279810466,\n",
       " -0.01817293860823824,\n",
       " -0.03634587721647648,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " -0.0016297340809249428,\n",
       " -0.05225350700328663,\n",
       " -0.03914855743365165,\n",
       " 0.00021455351882250455,\n",
       " 0.0,\n",
       " 0.0006305990027578299,\n",
       " 0.00046274143540015546,\n",
       " -0.10543745224478576,\n",
       " 0.0006386664823869348,\n",
       " 0.00022306430443159458,\n",
       " 0.0029275485428859353,\n",
       " -0.2660261270139377,\n",
       " 0.01657447509905952,\n",
       " -0.0545188158247157,\n",
       " -0.01817293860823824,\n",
       " -0.03634587721647648,\n",
       " -0.00012368071385092963,\n",
       " -0.33029798577521285,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " -0.03634587721647648,\n",
       " 0.02251581403223304,\n",
       " -0.03634587721647648,\n",
       " 0.07355260422600811,\n",
       " -0.15811914456475268,\n",
       " -0.01817293860823824,\n",
       " -0.03634587721647648,\n",
       " -0.03634587721647648,\n",
       " -0.00012368071385092963,\n",
       " 0.011034657422307935,\n",
       " 0.007545305169922533,\n",
       " 0.0014288586234603368,\n",
       " -0.0004947228554037185,\n",
       " 0.0012773329647738697,\n",
       " 0.003951770779725754,\n",
       " -0.017786876283393538,\n",
       " 0.00023591016725632333,\n",
       " 0.000297893011770579,\n",
       " 0.0010015322306766844,\n",
       " 0.0361006459314179,\n",
       " 8.594946377064522e-05,\n",
       " 0.010394756991126604,\n",
       " 4.059473821490657e-06,\n",
       " 0.0,\n",
       " 0.0032991846340708715,\n",
       " 9.598328349379337e-05,\n",
       " -0.024938287451282237,\n",
       " 0.984735389078415,\n",
       " 0.0,\n",
       " -0.05547957345510811,\n",
       " -0.0811479610479905,\n",
       " 0.0,\n",
       " -0.10436225191165506,\n",
       " 0.0,\n",
       " 0.017745822080320682,\n",
       " -0.006672581642195353,\n",
       " -0.01817293860823824,\n",
       " 0.00586525823325206,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " 3.1882052043205856e-05,\n",
       " 3.308479129424821e-05,\n",
       " 0.0021857589297595093,\n",
       " 0.030195607963220572,\n",
       " 0.006558101573771096,\n",
       " 0.039727686539486134,\n",
       " 7.465455547588775e-05,\n",
       " 0.0002016197972855961,\n",
       " 0.0005569525408109828,\n",
       " -0.1545946641156479,\n",
       " -0.07962384744977667,\n",
       " 0.0,\n",
       " 0.02045404718462889,\n",
       " 0.0172131415814467,\n",
       " 0.0002474409378212539,\n",
       " 0.00011751328366850699,\n",
       " 4.8319972770580345e-05,\n",
       " 0.0,\n",
       " 0.0019281226322276164,\n",
       " -0.020640865301221282,\n",
       " 0.0037810226429369005,\n",
       " 8.370978247266122e-05,\n",
       " 0.0009435652228002269,\n",
       " 9.00594132300437e-05,\n",
       " 0.0,\n",
       " 8.393401806484587e-05,\n",
       " 0.060752085986681206,\n",
       " -0.000950681308637687,\n",
       " 0.002265147121593618,\n",
       " 0.00011876788827310111,\n",
       " 0.0004715260089334759,\n",
       " 0.03723659678419877,\n",
       " 0.0003462514368107959,\n",
       " -0.0448624596213254,\n",
       " -0.8346622948687753,\n",
       " 0.02135982137830276,\n",
       " 0.00018285196598871322,\n",
       " 6.726094696263782e-05,\n",
       " 0.0014374342415159331,\n",
       " 0.0003596319916104233,\n",
       " -0.003812150507595691,\n",
       " 0.0,\n",
       " 0.002641054938994565,\n",
       " 0.0,\n",
       " 8.370978247266122e-05,\n",
       " -0.0006134384596694526,\n",
       " 0.0,\n",
       " 0.005774554198561478,\n",
       " 0.050437878044376515,\n",
       " 0.00031944740786333387,\n",
       " 0.00031944740786333387,\n",
       " 0.0,\n",
       " 0.00018755684591257953,\n",
       " 7.665813061990473e-05,\n",
       " 3.80255950931165e-05,\n",
       " 0.03723659678419877,\n",
       " 0.00013801716938524345,\n",
       " -0.1545946641156479,\n",
       " 0.0,\n",
       " -0.00012368071385092963,\n",
       " 0.0001425735709169757,\n",
       " -0.001675290785054061,\n",
       " -0.11108425328169584,\n",
       " -0.0008681963415862401,\n",
       " -0.0008681963415862401,\n",
       " 0.028910286569660108,\n",
       " 0.0,\n",
       " 9.493253179526987e-05,\n",
       " 0.01182225671124166,\n",
       " 0.2622824919921104,\n",
       " -0.06457078976852434,\n",
       " -0.01664469364774722,\n",
       " -0.33372502437765816,\n",
       " 0.052831176421333975,\n",
       " -0.061731412964237974,\n",
       " -0.14858244642343035,\n",
       " -0.00012368071385092963,\n",
       " 0.0006443972825898349,\n",
       " -0.03439832397308457,\n",
       " 4.939224798416369e-05,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.13687525721913252,\n",
       " -0.0007685731483157604,\n",
       " -0.0007685731483157604,\n",
       " 0.00827013499623384,\n",
       " -0.017935492659160588,\n",
       " 0.0015045875994103913,\n",
       " 0.0015045875994103913,\n",
       " 0.00043651384887613803,\n",
       " 0.006434298415577628,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.07893614866596597,\n",
       " -0.1545946641156479,\n",
       " 0.009087688299396582,\n",
       " 0.000172771149221815,\n",
       " 0.0,\n",
       " -0.022257844777745506,\n",
       " 0.0012673977440744795,\n",
       " 6.547177945330309e-05,\n",
       " 7.849901168374572e-05,\n",
       " 4.231132651152491e-06,\n",
       " -0.14883360068642262,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.005865218415106347,\n",
       " 0.0,\n",
       " 0.0012431304875113237,\n",
       " 0.0,\n",
       " 0.00045428857239940935,\n",
       " 0.03236907107687551,\n",
       " 0.003002503048999926,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0003268074795356821,\n",
       " 0.0007999496663728995,\n",
       " 4.059473821490657e-06,\n",
       " 0.0003182374903033429,\n",
       " 0.0,\n",
       " 0.10834303210564881,\n",
       " 0.0011768719459597623,\n",
       " -0.031268108363152386,\n",
       " 0.6214393779503307,\n",
       " 0.00015180705208034476,\n",
       " 0.00015180705208034476,\n",
       " 0.0003133313501115624,\n",
       " 0.0007568378301024021,\n",
       " 0.0012378867107501364,\n",
       " 0.034060865490962135,\n",
       " 0.00015307954097914102,\n",
       " 0.007441458105994233,\n",
       " 0.0024757734215002727,\n",
       " -0.019499998833715834,\n",
       " 0.0012378867107501364,\n",
       " 0.0015298644101347254,\n",
       " 0.020886776724038696,\n",
       " -0.09515530524864844,\n",
       " 0.029408442485291317,\n",
       " 0.008528451897796956,\n",
       " 0.06685833887541584,\n",
       " 0.0,\n",
       " 0.002237251789408348,\n",
       " 0.0,\n",
       " 0.00901328072286941,\n",
       " -0.046266929569520415,\n",
       " 5.669857292963175e-05,\n",
       " -0.04768860064502286,\n",
       " 5.921796679863907e-05,\n",
       " 0.21709584893029807,\n",
       " -0.23531461121607525,\n",
       " 0.11561031785921888,\n",
       " 0.12912859674715962,\n",
       " 0.12696696107297636,\n",
       " -0.01537903063258789,\n",
       " 0.005212458645564061,\n",
       " 0.03172996257814701,\n",
       " -0.0012225432369549587,\n",
       " 0.010778793262130945,\n",
       " 0.0013098975888832054,\n",
       " 3.89107673535716e-05,\n",
       " 0.0018130688382670336,\n",
       " 0.025323475394885334,\n",
       " -0.15767704960892984,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.005577708720250354,\n",
       " -0.00936056510123719,\n",
       " 0.017015070146084686,\n",
       " 0.006376762236670581,\n",
       " 0.023433086811264634,\n",
       " 0.03018665056092442,\n",
       " -0.0008221484849890143,\n",
       " -0.13783842785946596,\n",
       " 0.001136901187868265,\n",
       " 0.02364353670403868,\n",
       " 0.012479375899976306,\n",
       " 0.012516301314125046,\n",
       " 0.02734455610643438,\n",
       " 0.05892004116468895,\n",
       " 0.0,\n",
       " 0.1245567217386751,\n",
       " 0.0012884775674285537,\n",
       " 0.08193042672512235,\n",
       " 0.0012651434754648158,\n",
       " 0.14073560423403755,\n",
       " 0.15401111339817905,\n",
       " 0.0016077347663696335,\n",
       " -0.13742829759146027,\n",
       " -0.0007916216670367404,\n",
       " -0.008723846329916825,\n",
       " -0.00012368071385092963,\n",
       " -0.4352006409371438,\n",
       " 0.03884267602083051,\n",
       " 0.00041327659870220925,\n",
       " -0.3734712977125512,\n",
       " 0.002213805036886768,\n",
       " 0.062127002173219585,\n",
       " -0.14418682071659097,\n",
       " 0.13410881616478418,\n",
       " 0.0013668619548967338,\n",
       " 0.002943970614140962,\n",
       " 0.05167818823012734,\n",
       " 0.05167818823012734,\n",
       " 1.2792418273144438e-05,\n",
       " 0.00021113820230740389,\n",
       " 0.0,\n",
       " -0.25214417748989415,\n",
       " 0.001903561403312308,\n",
       " -0.12684286750660406,\n",
       " 0.0,\n",
       " 0.06180978241839457,\n",
       " 0.0,\n",
       " -0.09806231120009651,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0005042682312855404,\n",
       " -0.04736374854866295,\n",
       " 0.0,\n",
       " -0.12721057025767255,\n",
       " 0.0,\n",
       " 0.02799346960276884,\n",
       " 0.0009652212688912801,\n",
       " 0.0,\n",
       " 0.0007626909459131881,\n",
       " 0.010707691355080432,\n",
       " -0.01116675205840908,\n",
       " -0.03634587721647648,\n",
       " -0.07414058758927382,\n",
       " -0.4065213080891126,\n",
       " -0.09403407516960509,\n",
       " 0.0025666686961338847,\n",
       " 0.0,\n",
       " 0.01003675835943505,\n",
       " 0.00019404114116095247,\n",
       " 0.0,\n",
       " -0.01817293860823824,\n",
       " 0.001298376272312439,\n",
       " 0.091639432904153,\n",
       " 0.009497329737205644,\n",
       " 0.0004095395148148612,\n",
       " -0.0037709367312634585,\n",
       " 8.42691917899802e-05,\n",
       " -0.18582837216912695,\n",
       " 0.00031097560012444115,\n",
       " -0.03621028569793934,\n",
       " 0.07530029186506902,\n",
       " -0.5115697693796062,\n",
       " 0.08630322160406699,\n",
       " -0.09256555900176744,\n",
       " -0.06351607631323768,\n",
       " 0.005333798689454453,\n",
       " 0.0060391713653046904,\n",
       " -0.17042656232983694,\n",
       " 0.0741064304458307,\n",
       " 0.0037318098019037825,\n",
       " -0.10483076748652155,\n",
       " 0.028781897477411132,\n",
       " -0.06797814597131291,\n",
       " -0.013940868419329497,\n",
       " 0.08213604863704813,\n",
       " 0.08000698034181727,\n",
       " 0.02133519475781781,\n",
       " 0.010667597378908905,\n",
       " 0.01600139606836321,\n",
       " 0.06400558427345283,\n",
       " 0.007092889788019489,\n",
       " 0.02133519475781781,\n",
       " 0.071457003082655,\n",
       " 0.050610621551997956,\n",
       " 0.003400966222168564,\n",
       " 0.06748110292459977,\n",
       " 0.03376188323529197,\n",
       " 0.005333798689454453,\n",
       " 0.02666899344727229,\n",
       " 0.05333798689454458,\n",
       " 0.01600139606836321,\n",
       " 0.02133519475781781,\n",
       " 0.03200279213672642,\n",
       " 0.0,\n",
       " 0.013454444191341664,\n",
       " -0.1945629248679633,\n",
       " 0.06933938296290848,\n",
       " 0.03200279213672642,\n",
       " 0.005333798689454453,\n",
       " 0.005333798689454453,\n",
       " 0.01600139606836321,\n",
       " -0.0011291833409114362,\n",
       " 0.01600139606836321,\n",
       " 0.005333798689454453,\n",
       " 0.0,\n",
       " 0.0010380537964540903,\n",
       " -0.09188811344879888,\n",
       " 0.004142151393014228,\n",
       " 0.039737538275968654,\n",
       " -0.009636642534856575,\n",
       " 0.0006902456064138907,\n",
       " 0.00031955459782088773,\n",
       " -0.11431032415262467,\n",
       " 0.0009323876373149747,\n",
       " -0.22203448876811346,\n",
       " 0.0014954834535573886,\n",
       " 0.012301146893949962,\n",
       " 0.0005164728788242311,\n",
       " 0.1108451566801039,\n",
       " 0.001316707325198652,\n",
       " 0.02694979440816421,\n",
       " 0.011539052228959389,\n",
       " 0.0,\n",
       " 0.003642703643812929,\n",
       " 0.0017554046925312486,\n",
       " -0.0008405738659425235,\n",
       " 0.0917223531301816,\n",
       " 0.00033900691104940523,\n",
       " 0.39146897179461415,\n",
       " 0.1746049996936126,\n",
       " 0.006874760055924669,\n",
       " 0.0001150845783186114,\n",
       " 0.002213805036886768,\n",
       " 0.14193093804291146,\n",
       " 0.001136901187868265,\n",
       " 0.0,\n",
       " 0.043176762075108815,\n",
       " -0.22859987729299475,\n",
       " 0.0,\n",
       " 3.764369587359204e-05,\n",
       " -0.05716230292154903,\n",
       " 0.0071832350980686605,\n",
       " 0.11279431392675343,\n",
       " 0.0009242744974998868,\n",
       " 0.5103337820547639,\n",
       " 0.0003044885025513257,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.07573106461184434,\n",
       " 0.0004013567397484391,\n",
       " -0.5498825950095709,\n",
       " 0.00010550563906970285,\n",
       " 0.0010303255555147782,\n",
       " 0.0037334691722884753,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.26936081994897487,\n",
       " 0.012558323587387066,\n",
       " 0.0,\n",
       " 0.0003220958527454497,\n",
       " 0.0,\n",
       " 0.4210680145994085,\n",
       " 0.0009656774369697931,\n",
       " 0.010256742662875424,\n",
       " -0.004329597069516654,\n",
       " -0.00840185999760684,\n",
       " 0.0003444396471480729,\n",
       " 0.0013028079265407016,\n",
       " 0.02400717435793852,\n",
       " 0.04843206997895497,\n",
       " 0.22848480147606479,\n",
       " 2.078292431231107e-05,\n",
       " 0.06584713817388636,\n",
       " 0.36397732571408187,\n",
       " 0.16868437927177213,\n",
       " 0.0,\n",
       " 0.0030189132279139904,\n",
       " 0.018786164923936824,\n",
       " 0.005333798689454453,\n",
       " 0.010667597378908905,\n",
       " -0.08103838747668293,\n",
       " -0.005240235311193014,\n",
       " -0.002620117655596507,\n",
       " 0.02436997629424955,\n",
       " 0.0006443972825898349,\n",
       " -0.01817293860823824,\n",
       " -0.03570147993388644,\n",
       " -0.08469958191156676,\n",
       " 0.007398324393623925,\n",
       " -0.01061192820605373,\n",
       " -0.0869433084327605,\n",
       " 0.0326543334847962,\n",
       " 0.18663986191322185,\n",
       " 0.04100511087512636,\n",
       " 0.001188708061421508,\n",
       " 0.0023971786788864577,\n",
       " 0.0006443972825898349,\n",
       " 0.18382237951562005,\n",
       " -0.22806601637584545,\n",
       " 0.0,\n",
       " 0.02315729299669571,\n",
       " -0.01817293860823824,\n",
       " -0.34835932928716296,\n",
       " 0.002831487683926361,\n",
       " -0.09571057864919463,\n",
       " -0.06902981994554246,\n",
       " -0.10211188819839918,\n",
       " -0.06902981994554246,\n",
       " -0.001675290785054061,\n",
       " 0.0,\n",
       " -0.01817293860823824,\n",
       " 0.008896677168532164,\n",
       " 0.34953930010914075,\n",
       " -0.020754919940529793,\n",
       " 0.0008757857848004879,\n",
       " -0.15086995054221006,\n",
       " 0.0,\n",
       " -0.16366340054740605,\n",
       " -0.01393376885176231,\n",
       " 0.043014117552700896,\n",
       " 0.007106095412920037,\n",
       " -0.08161777379180381,\n",
       " 0.02581287186390886,\n",
       " 5.9202179658929504e-05,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.15511418876083338,\n",
       " 0.003313107106192315,\n",
       " 0.061017831821340254,\n",
       " 0.00048816991268805853,\n",
       " 0.0,\n",
       " -0.04928696977492104,\n",
       " -0.10543745224478576,\n",
       " 0.06301682025920201,\n",
       " 0.00032423948912370463,\n",
       " 0.205880915630808,\n",
       " 0.012479375899976306,\n",
       " 0.009451368012116565,\n",
       " 0.019122345090826064,\n",
       " 0.0,\n",
       " -0.0011291833409114362,\n",
       " 0.0,\n",
       " -0.43992407818146,\n",
       " 5.722729115981414e-05,\n",
       " 0.007509201624244637,\n",
       " -0.007176463652993087,\n",
       " 0.09623000955365922,\n",
       " 0.0,\n",
       " 0.6728182664646062,\n",
       " -0.10299719742995787,\n",
       " 0.004137765126551228,\n",
       " 0.00043443473790696663,\n",
       " 0.020558998396839633,\n",
       " 0.0929657030273407,\n",
       " 0.0007113558795507382,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.1297619392696707,\n",
       " -0.030807435322241332,\n",
       " 0.029945611351565857,\n",
       " 0.002213805036886768,\n",
       " 0.051661366029880404,\n",
       " 0.04913033845383474,\n",
       " 0.0,\n",
       " -0.05378897889705977,\n",
       " -0.12894540599614615,\n",
       " -0.2540025944991583,\n",
       " -0.004498953218371768,\n",
       " 0.0012577182342656422,\n",
       " -0.08630537925448502,\n",
       " 0.09346317516919904,\n",
       " 0.04516385956768936,\n",
       " 0.00043209481786089565,\n",
       " 0.221684101843707,\n",
       " 0.11123225975959032,\n",
       " 0.0,\n",
       " -0.1054369833852638,\n",
       " -0.11850236420889902,\n",
       " -0.016010054559122577,\n",
       " -0.01292581391429787,\n",
       " -0.000456926592138992,\n",
       " -0.11463709259234382,\n",
       " 0.0,\n",
       " 0.0018094197951653045,\n",
       " 0.0,\n",
       " -0.01817293860823824,\n",
       " -0.01817293860823824,\n",
       " -0.0015472191446227056,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.001675290785054061,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.1191669631340966,\n",
       " 0.06891794066718485,\n",
       " 0.0,\n",
       " 0.1385356103467531,\n",
       " 0.16009932761871634,\n",
       " 0.0016529714077914625,\n",
       " 0.0025109695995579064,\n",
       " 0.012639659189335678,\n",
       " 1.0024240669894544,\n",
       " 0.00010759057687724722,\n",
       " 0.096086960147269,\n",
       " 9.20430127164851e-05,\n",
       " -0.13570648469033356,\n",
       " 0.0,\n",
       " 0.02847774478555711,\n",
       " -0.0037709367312634585,\n",
       " 0.025110096850150053,\n",
       " 0.0,\n",
       " 0.1130718948744698,\n",
       " 0.00013995125019309628,\n",
       " 0.04054497104641685,\n",
       " -0.03925585117783803,\n",
       " -0.06826847568039518,\n",
       " 0.0,\n",
       " 0.01456448409416432,\n",
       " 0.00013995125019309628,\n",
       " -0.20394371540901945,\n",
       " -0.06525711475369611,\n",
       " 0.00013995125019309628,\n",
       " 0.0001597948222879599,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.04293569428028962,\n",
       " 0.05404604278337395,\n",
       " -0.10002349567479307,\n",
       " 0.0,\n",
       " 0.05401670672725707,\n",
       " 0.0017931682835468645,\n",
       " 0.005963080339914876,\n",
       " 0.00028011249647402195,\n",
       " 2.4821418086191547e-05,\n",
       " -0.03943263548390223,\n",
       " 0.00024224735785593934,\n",
       " 8.404209278043757e-06,\n",
       " 0.002904483245910637,\n",
       " 0.02955058247765412,\n",
       " 0.00023431363672421143,\n",
       " 0.08211071521124505,\n",
       " 0.0011334015082197482,\n",
       " 0.21989330082040828,\n",
       " 0.019366993297064275,\n",
       " 0.0094741139508536,\n",
       " -0.005706759201842404,\n",
       " 0.0,\n",
       " 0.0005870191500096138,\n",
       " 0.0,\n",
       " 0.00014110059040051194,\n",
       " 6.683212438123666e-05,\n",
       " 0.0094741139508536,\n",
       " -0.017183479169224847,\n",
       " 0.17570387446846478,\n",
       " -0.07823816888304581,\n",
       " 0.0,\n",
       " 0.026019717434418117,\n",
       " 0.0,\n",
       " 0.11713485734186707,\n",
       " 0.011946041956044626,\n",
       " -0.12488681424803323,\n",
       " -0.021669137716116917,\n",
       " 0.0003612178593167187,\n",
       " -0.021669137716116917,\n",
       " -0.037646174667011936,\n",
       " 0.0,\n",
       " 0.003826454845993701,\n",
       " -0.07823816888304581,\n",
       " 0.0027977102134839307,\n",
       " 0.00268474906757556,\n",
       " 4.231132651152491e-06,\n",
       " 0.0007830107430138584,\n",
       " 0.00987618677297071,\n",
       " -0.11320939056665373,\n",
       " 0.00934444026756973,\n",
       " 0.0,\n",
       " 0.12018480091366315,\n",
       " 0.003283016179814331,\n",
       " 0.06448011472440565,\n",
       " 0.27431458069965803,\n",
       " 0.008915669215052626,\n",
       " 0.0005063864760270446,\n",
       " 0.06891794066718485,\n",
       " 0.0,\n",
       " 0.02423458189126694,\n",
       " 0.005575763740509015,\n",
       " 0.0,\n",
       " 0.02694979440816421,\n",
       " -0.0010079960252964285,\n",
       " -0.013416780485932785,\n",
       " 0.0009640067490374515,\n",
       " 0.0006775843581182298,\n",
       " -0.14858244642343035,\n",
       " 9.368038367940582e-05,\n",
       " 0.0014787492056547449,\n",
       " -0.14977907249912922,\n",
       " 0.05236290370628593,\n",
       " 0.0,\n",
       " 0.0002740626748386159,\n",
       " 0.0005436782950868821,\n",
       " 0.0013940124148237721,\n",
       " 0.008096846077339775,\n",
       " -0.03796539997327603,\n",
       " 0.3060383828511289,\n",
       " 0.07478791679740443,\n",
       " -0.3729980523437108,\n",
       " -0.0066476332040346624,\n",
       " -0.27984113617887824,\n",
       " -0.07286895527228629,\n",
       " 0.001165181352775349,\n",
       " 0.10123851575920682,\n",
       " 0.0013459636640368977,\n",
       " 0.02025199886362689,\n",
       " 0.12668027132658866,\n",
       " 0.023035155906007663,\n",
       " -0.15146604613850131,\n",
       " 0.0024442368061435094,\n",
       " -0.08708963204188686,\n",
       " 0.0,\n",
       " 0.0946290645429608,\n",
       " 0.0013817424974441295,\n",
       " 0.0,\n",
       " 0.0,\n",
       " -0.042856246090935794,\n",
       " 0.0007195497045309736,\n",
       " 0.009598862528611479,\n",
       " 0.003033268303042051,\n",
       " -0.03619208113995409,\n",
       " 0.1133001638424347,\n",
       " -0.000619803113980526,\n",
       " 0.05937140969001273,\n",
       " 0.00011737159041038993,\n",
       " 0.0012192723795625502,\n",
       " 0.000874903479594223,\n",
       " 0.0006979467763976946,\n",
       " -0.003981194737873667,\n",
       " 0.04824361155526814,\n",
       " 0.035241850480603985,\n",
       " -0.00840185999760684,\n",
       " 0.06142676757003552,\n",
       " -0.2730072329312459,\n",
       " -0.016638079176545705,\n",
       " 0.006467393968404545,\n",
       " 0.0016036470924139334,\n",
       " -0.33416671325453096,\n",
       " 0.0775100966486556,\n",
       " -0.05666493698246942,\n",
       " 0.02590127969384104,\n",
       " 0.0,\n",
       " 0.0026235281126723055,\n",
       " 0.00045918563753694227,\n",
       " 0.0,\n",
       " 0.00035574565012814617,\n",
       " 0.0,\n",
       " 0.04999273153016653,\n",
       " -0.010409624915587522,\n",
       " -0.010409624915587522,\n",
       " -0.25090515411461906,\n",
       " 0.00015180705208034476,\n",
       " 0.0004773822612018232,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.08632510676482091,\n",
       " -0.05099782814028252,\n",
       " 0.018488071459152725,\n",
       " 0.03797790578350509,\n",
       " 0.0,\n",
       " 0.07668375705959986,\n",
       " -0.09812909798168552,\n",
       " 0.0021242822722766994,\n",
       " -0.25566910084590616,\n",
       " 0.0,\n",
       " -0.02207197225984029,\n",
       " 0.007666781365052544,\n",
       " 0.00026127377940339124,\n",
       " 0.0005225475588067825,\n",
       " 0.00026127377940339124,\n",
       " 0.15090136331568513,\n",
       " 0.0009146825823258761,\n",
       " 0.0,\n",
       " 0.00027214527917362183,\n",
       " 0.004623926342470048,\n",
       " 0.054140953959405075,\n",
       " -0.2441134118278139,\n",
       " 0.010503661752281438,\n",
       " 4.68867451980005e-05,\n",
       " -0.14442767050053282,\n",
       " -0.14442767050053282,\n",
       " 0.0,\n",
       " 0.06491843976364454,\n",
       " 0.0006269689247973598,\n",
       " 0.0003830888940481985,\n",
       " 0.008308432314949895,\n",
       " -0.024047036275639463,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 0.00032659937654618394,\n",
       " 0.004795891734289846,\n",
       " -0.014550962753093236,\n",
       " -0.004027576450670635,\n",
       " 0.0,\n",
       " 0.11777371350351885,\n",
       " 0.0,\n",
       " 0.0,\n",
       " 3.949071817577692e-05,\n",
       " 0.006222267957051765,\n",
       " 0.013086587926251714,\n",
       " -0.03030566725609311,\n",
       " 0.00029146083093384474,\n",
       " -0.00948129682816657,\n",
       " 2.4738913223243702e-06,\n",
       " 0.0008705270232944498,\n",
       " 0.0008705270232944498,\n",
       " 0.0,\n",
       " 2.7897251577787727e-05,\n",
       " -0.5975251451998174,\n",
       " 0.0008760360523832789,\n",
       " 0.004176680160201458,\n",
       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef = ml.coef_.tolist()[0]\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Word</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00</td>\n",
       "      <td>-0.421306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000</td>\n",
       "      <td>-0.228515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0000</td>\n",
       "      <td>0.305484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>000001</td>\n",
       "      <td>-0.002350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00001</td>\n",
       "      <td>-0.010245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114964</th>\n",
       "      <td>çaykur</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114965</th>\n",
       "      <td>çelem</td>\n",
       "      <td>-0.124060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114966</th>\n",
       "      <td>être</td>\n",
       "      <td>0.020237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114967</th>\n",
       "      <td>île</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114968</th>\n",
       "      <td>ît</td>\n",
       "      <td>0.000352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114969 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Word  Coefficient\n",
       "0           00    -0.421306\n",
       "1          000    -0.228515\n",
       "2         0000     0.305484\n",
       "3       000001    -0.002350\n",
       "4        00001    -0.010245\n",
       "...        ...          ...\n",
       "114964  çaykur     0.000000\n",
       "114965   çelem    -0.124060\n",
       "114966    être     0.020237\n",
       "114967     île     0.000000\n",
       "114968      ît     0.000352\n",
       "\n",
       "[114969 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame({'Word' : w, 'Coefficient' : coef})\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Word</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80600</th>\n",
       "      <td>pleasantly</td>\n",
       "      <td>3.945774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39072</th>\n",
       "      <td>downside</td>\n",
       "      <td>3.156989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94667</th>\n",
       "      <td>skeptical</td>\n",
       "      <td>2.957734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55029</th>\n",
       "      <td>hooked</td>\n",
       "      <td>2.574582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5865</th>\n",
       "      <td>addicting</td>\n",
       "      <td>2.531677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24949</th>\n",
       "      <td>cancelled</td>\n",
       "      <td>-3.013023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106852</th>\n",
       "      <td>unacceptable</td>\n",
       "      <td>-3.047599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34989</th>\n",
       "      <td>deceptive</td>\n",
       "      <td>-3.164321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113164</th>\n",
       "      <td>worst</td>\n",
       "      <td>-3.169090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107383</th>\n",
       "      <td>undrinkable</td>\n",
       "      <td>-3.754940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114969 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Word  Coefficient\n",
       "80600     pleasantly     3.945774\n",
       "39072       downside     3.156989\n",
       "94667      skeptical     2.957734\n",
       "55029         hooked     2.574582\n",
       "5865       addicting     2.531677\n",
       "...              ...          ...\n",
       "24949      cancelled    -3.013023\n",
       "106852  unacceptable    -3.047599\n",
       "34989      deceptive    -3.164321\n",
       "113164         worst    -3.169090\n",
       "107383   undrinkable    -3.754940\n",
       "\n",
       "[114969 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = coeff_df.sort_values(['Coefficient', 'Word'],ascending=False)\n",
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-Top 20 positive-\n",
      "       Word  Coefficient\n",
      " pleasantly     3.945774\n",
      "   downside     3.156989\n",
      "  skeptical     2.957734\n",
      "     hooked     2.574582\n",
      "  addicting     2.531677\n",
      "     delish     2.421138\n",
      "   soothing     2.328296\n",
      "   terrific     2.256851\n",
      "   drawback     2.229155\n",
      "     resist     2.204452\n",
      "       whim     2.152782\n",
      " economical     2.144775\n",
      "      holds     2.097696\n",
      "   hesitant     2.095810\n",
      "      saves     2.091049\n",
      "  delighted     2.003565\n",
      "  addictive     1.990702\n",
      "    trainer     1.986523\n",
      "        met     1.981993\n",
      "  excellent     1.978676\n",
      "\n",
      "\n",
      "-Top 20 negative-\n",
      "          Word  Coefficient\n",
      "        bummer    -2.387377\n",
      "    unfinished    -2.408736\n",
      "    flavorless    -2.415462\n",
      "    returnable    -2.432147\n",
      "         blech    -2.440293\n",
      "       defeats    -2.450141\n",
      "    unbearable    -2.481961\n",
      "       vomited    -2.491464\n",
      "   disapointed    -2.506090\n",
      "      mediocre    -2.537969\n",
      "         lousy    -2.555799\n",
      "        lacked    -2.628722\n",
      "   embarrassed    -2.791182\n",
      " disappointing    -2.835110\n",
      "        ripoff    -2.884592\n",
      "     cancelled    -3.013023\n",
      "  unacceptable    -3.047599\n",
      "     deceptive    -3.164321\n",
      "         worst    -3.169090\n",
      "   undrinkable    -3.754940\n"
     ]
    }
   ],
   "source": [
    "print('-Top 20 positive-')\n",
    "print(coeff_df.head(20).to_string(index=False))\n",
    "print('\\n')\n",
    "print('-Top 20 negative-')        \n",
    "print(coeff_df.tail(20).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automate Whole Process For NLP and ML algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "### create a function \n",
    "\n",
    "def text_fit(X, y, nlp_model,ml_model,coef_show=1):\n",
    "    \n",
    "    X_c = nlp_model.fit_transform(X)\n",
    "    print('features: {}'.format(X_c.shape[1]))\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_c, y)\n",
    "    print(' train records: {}'.format(X_train.shape[0]))\n",
    "    print(' test records: {}'.format(X_test.shape[0]))\n",
    "    ml =ml_model.fit(X_train, y_train)\n",
    "    acc = ml.score(X_test, y_test)\n",
    "    print ('Model Accuracy: {}'.format(acc))\n",
    "    \n",
    "    if coef_show == 1: \n",
    "        w = nlp_model.get_feature_names()\n",
    "        coef = ml.coef_.tolist()[0]\n",
    "        coeff_df = pd.DataFrame({'Word' : w, 'Coefficient' : coef})\n",
    "        coeff_df = coeff_df.sort_values(['Coefficient', 'Word'], ascending=[0, 1])\n",
    "        print('\\n')\n",
    "        print('-Top 20 positive-')\n",
    "        print(coeff_df.head(20).to_string(index=False))\n",
    "        print('\\n')\n",
    "        print('-Top 20 negative-')        \n",
    "        print(coeff_df.tail(20).to_string(index=False))\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c = CountVectorizer(stop_words = 'english')\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features: 114969\n",
      " train records: 394360\n",
      " test records: 131454\n",
      "Model Accuracy: 0.9360764982427313\n",
      "\n",
      "\n",
      "-Top 20 positive-\n",
      "       Word  Coefficient\n",
      " pleasantly     4.082539\n",
      "   downside     3.319253\n",
      "  skeptical     2.804335\n",
      "  addicting     2.598770\n",
      "     resist     2.479318\n",
      "       calm     2.440002\n",
      "     delish     2.371622\n",
      "       whim     2.367316\n",
      "   drawback     2.362314\n",
      "     hooked     2.197692\n",
      "   terrific     2.173592\n",
      "    easiest     2.152261\n",
      "   tastiest     2.143567\n",
      "    worries     2.132367\n",
      "  excellent     2.070613\n",
      "    trainer     2.037212\n",
      "   relaxing     2.033416\n",
      "      penny     2.028012\n",
      "      saves     2.015664\n",
      "    awesome     2.014492\n",
      "\n",
      "\n",
      "-Top 20 negative-\n",
      "           Word  Coefficient\n",
      "          lousy    -2.352364\n",
      "    disapointed    -2.426579\n",
      "        defeats    -2.467330\n",
      "     unbearable    -2.487244\n",
      "         lacked    -2.494559\n",
      "    unappealing    -2.517822\n",
      " disappointment    -2.540629\n",
      "         bummer    -2.576054\n",
      "        vomited    -2.681837\n",
      "     overpowers    -2.686684\n",
      "    embarrassed    -2.690554\n",
      "   unacceptable    -2.879316\n",
      "       mediocre    -2.992010\n",
      "         ripoff    -3.010243\n",
      "     returnable    -3.114917\n",
      "  disappointing    -3.194111\n",
      "      cancelled    -3.260936\n",
      "          worst    -3.430112\n",
      "      deceptive    -3.727187\n",
      "    undrinkable    -3.941053\n"
     ]
    }
   ],
   "source": [
    "\n",
    "text_fit(X, y, c, LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining a Predict Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "def predict(X, y, nlp_model,ml_model):\n",
    "    \n",
    "    X_c = nlp_model.fit_transform(X)\n",
    "    print('features: {}'.format(X_c.shape[1]))\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_c, y)\n",
    "    print(' train records: {}'.format(X_train.shape[0]))\n",
    "    print(' test records: {}'.format(X_test.shape[0]))\n",
    "    ml =ml_model.fit(X_train, y_train)\n",
    "    predictions=ml.predict(X_test)\n",
    "    cm=confusion_matrix(predictions,y_test)\n",
    "    print(cm)\n",
    "    acc=accuracy_score(predictions,y_test)\n",
    "    print(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "c = CountVectorizer(stop_words = 'english')\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features: 114969\n",
      " train records: 394360\n",
      " test records: 131454\n",
      "[[ 15069   2906]\n",
      " [  5585 107894]]\n",
      "0.9354070625465942\n"
     ]
    }
   ],
   "source": [
    "predict(X,y,c,lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy is around 93.9%. Now we can notice that some of those significant coefficients are not meaningful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features: 114969\n",
      " train records: 394360\n",
      " test records: 131454\n",
      "Model Accuracy: 0.842560895826677\n"
     ]
    }
   ],
   "source": [
    "### calling function for dummy classifier \n",
    "text_fit(X, y, c, DummyClassifier(),0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression model on TFIDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "features: 114969\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(stop_words = 'english')\n",
    "text_fit(X, y, tfidf, LogisticRegression())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(stop_words = 'english')\n",
    "predict(X, y, tfidf, LogisticRegression())"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Accurany is roughly the same - 93.5%. However we notice that the significant words make much more sense now, with higher coefficient magnitude as well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upvote prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df[df['Score'] == 5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data[data['%upvote'].isin(['0-20%', '20-40%', '60-80%', '80-100%'])]\n",
    "data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data2['Text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dict = {'0-20%': 0, '20-40%': 0, '60-80%': 1, '80-100%': 1}\n",
    "y = data2['%upvote'].map(y_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(y.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The target class 'y' is highly skewed , so as i have observed positive upvotes are too much higher than negative ones. We have to resample data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf=TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_c=tf.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X_c,y,train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## RandomOverSampler to handle imbalanced data\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os =  RandomOverSampler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_res, y_train_res = os.fit_resample(X_c, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_res.shape,y_train_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Original dataset shape {}'.format(Counter(y)))\n",
    "print('Resampled dataset shape {}'.format(Counter(y_train_res)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_class=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid={'C':10.0 **np.arange(-2,3),'penalty':['l1','l2']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "clf=GridSearchCV(estimator=log_class,param_grid=grid,cv=5,n_jobs=-1,scoring='f1_macro')\n",
    "clf.fit(X_train_res,y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(accuracy_score(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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}