NLP Project .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Natural Language Processing Project\n",
    "\n",
    "In this NLP project you will be attempting to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews.\n",
    "\n",
    "[Yelp Review Data Set from Kaggle]\n",
    "\n",
    "Each observation in this dataset is a review of a particular business by a particular user.\n",
    "\n",
    "The \"stars\" column is the number of stars (1 through 5) assigned by the reviewer to the business. (Higher stars is better.) In other words, it is the rating of the business by the person who wrote the review.\n",
    "\n",
    "The \"cool\" column is the number of \"cool\" votes this review received from other Yelp users. \n",
    "\n",
    "All reviews start with 0 \"cool\" votes, and there is no limit to how many \"cool\" votes a review can receive. In other words, it is a rating of the review itself, not a rating of the business.\n",
    "\n",
    "The \"useful\" and \"funny\" columns are similar to the \"cool\" column.\n",
    "\n",
    "Let's get started! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    " **Import the usual suspects. :) **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLTK Downloader\n",
      "---------------------------------------------------------------------------\n",
      "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
      "---------------------------------------------------------------------------\n",
      "Downloader> d\n",
      "\n",
      "Download which package (l=list; x=cancel)?\n",
      "  Identifier> stopwords\n",
      "    Downloading package stopwords to\n",
      "        C:\\Users\\prade\\AppData\\Roaming\\nltk_data...\n",
      "      Package stopwords is already up-to-date!\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
      "---------------------------------------------------------------------------\n",
      "Downloader> q\n"
     ]
    }
   ],
   "source": [
    "nltk.download_shell()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "**Reading data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yelp = pd.read_csv('yelp.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Checking the head, info , and describe methods on yelp.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
       "      <td>review</td>\n",
       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
       "      <td>review</td>\n",
       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
       "      <td>2012-06-14</td>\n",
       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "      <td>review</td>\n",
       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id        date               review_id  stars  \\\n",
       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
       "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
       "\n",
       "                                                text    type  \\\n",
       "0  My wife took me here on my birthday for breakf...  review   \n",
       "1  I have no idea why some people give bad review...  review   \n",
       "2  love the gyro plate. Rice is so good and I als...  review   \n",
       "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
       "4  General Manager Scott Petello is a good egg!!!...  review   \n",
       "\n",
       "                  user_id  cool  useful  funny  \n",
       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
       "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0  \n",
       "3  uZetl9T0NcROGOyFfughhg     1       2      0  \n",
       "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 10 columns):\n",
      "business_id    10000 non-null object\n",
      "date           10000 non-null object\n",
      "review_id      10000 non-null object\n",
      "stars          10000 non-null int64\n",
      "text           10000 non-null object\n",
      "type           10000 non-null object\n",
      "user_id        10000 non-null object\n",
      "cool           10000 non-null int64\n",
      "useful         10000 non-null int64\n",
      "funny          10000 non-null int64\n",
      "dtypes: int64(4), object(6)\n",
      "memory usage: 781.3+ KB\n"
     ]
    }
   ],
   "source": [
    "yelp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stars</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.777500</td>\n",
       "      <td>0.876800</td>\n",
       "      <td>1.409300</td>\n",
       "      <td>0.701300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.214636</td>\n",
       "      <td>2.067861</td>\n",
       "      <td>2.336647</td>\n",
       "      <td>1.907942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              stars          cool        useful         funny\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000\n",
       "mean       3.777500      0.876800      1.409300      0.701300\n",
       "std        1.214636      2.067861      2.336647      1.907942\n",
       "min        1.000000      0.000000      0.000000      0.000000\n",
       "25%        3.000000      0.000000      0.000000      0.000000\n",
       "50%        4.000000      0.000000      1.000000      0.000000\n",
       "75%        5.000000      1.000000      2.000000      1.000000\n",
       "max        5.000000     77.000000     76.000000     57.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a new column called \"text length\" which is the number of words in the text column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yelp['text length'] = yelp['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>text length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
       "      <td>review</td>\n",
       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
       "      <td>review</td>\n",
       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n",
       "      <td>2012-06-14</td>\n",
       "      <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n",
       "      <td>4</td>\n",
       "      <td>love the gyro plate. Rice is so good and I als...</td>\n",
       "      <td>review</td>\n",
       "      <td>0hT2KtfLiobPvh6cDC8JQg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id        date               review_id  stars  \\\n",
       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
       "2  6oRAC4uyJCsJl1X0WZpVSA  2012-06-14  IESLBzqUCLdSzSqm0eCSxQ      4   \n",
       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
       "\n",
       "                                                text    type  \\\n",
       "0  My wife took me here on my birthday for breakf...  review   \n",
       "1  I have no idea why some people give bad review...  review   \n",
       "2  love the gyro plate. Rice is so good and I als...  review   \n",
       "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
       "4  General Manager Scott Petello is a good egg!!!...  review   \n",
       "\n",
       "                  user_id  cool  useful  funny  text length  \n",
       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0          889  \n",
       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0         1345  \n",
       "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0           76  \n",
       "3  uZetl9T0NcROGOyFfughhg     1       2      0          419  \n",
       "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0          469  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "Let's explore the data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using FacetGrid from the seaborn library to create a grid of 5 histograms of text length based off of the star ratings. Reference the seaborn documentation for hints on this**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13adea35cc0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAADQCAYAAADxn5GHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13adeb462e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "d=sns.FacetGrid(yelp, col='stars')\n",
    "d.map(plt.hist,'text length',bins=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a boxplot of text length for each star category.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13adea3bc18>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEFCAYAAAD5bXAgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEQligiAiIkFMEEREJOj/A/cCXDCQaLv9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13ade9ebc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='stars',y='text length',data=yelp,palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**countplot of the number of occurrences for each type of star rating.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13adf4db390>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEFCAYAAAD5bXAgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niEAAAIwIBADAiEAAAIwIBADA6P8Be6i3qFts2eEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13adeb46f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='stars',data=yelp,palette='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Using groupby to get the mean values of the numerical columns, you should be able to create this dataframe with the operation:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>text length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stars</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.576769</td>\n",
       "      <td>1.604806</td>\n",
       "      <td>1.056075</td>\n",
       "      <td>826.515354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.719525</td>\n",
       "      <td>1.563107</td>\n",
       "      <td>0.875944</td>\n",
       "      <td>842.256742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.788501</td>\n",
       "      <td>1.306639</td>\n",
       "      <td>0.694730</td>\n",
       "      <td>758.498289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.954623</td>\n",
       "      <td>1.395916</td>\n",
       "      <td>0.670448</td>\n",
       "      <td>712.923142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.944261</td>\n",
       "      <td>1.381780</td>\n",
       "      <td>0.608631</td>\n",
       "      <td>624.999101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           cool    useful     funny  text length\n",
       "stars                                           \n",
       "1      0.576769  1.604806  1.056075   826.515354\n",
       "2      0.719525  1.563107  0.875944   842.256742\n",
       "3      0.788501  1.306639  0.694730   758.498289\n",
       "4      0.954623  1.395916  0.670448   712.923142\n",
       "5      0.944261  1.381780  0.608631   624.999101"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars = yelp.groupby('stars').mean()\n",
    "stars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using the corr() method on that groupby dataframe to produce this dataframe:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>text length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cool</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.743329</td>\n",
       "      <td>-0.944939</td>\n",
       "      <td>-0.857664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>useful</th>\n",
       "      <td>-0.743329</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.894506</td>\n",
       "      <td>0.699881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funny</th>\n",
       "      <td>-0.944939</td>\n",
       "      <td>0.894506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.843461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text length</th>\n",
       "      <td>-0.857664</td>\n",
       "      <td>0.699881</td>\n",
       "      <td>0.843461</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 cool    useful     funny  text length\n",
       "cool         1.000000 -0.743329 -0.944939    -0.857664\n",
       "useful      -0.743329  1.000000  0.894506     0.699881\n",
       "funny       -0.944939  0.894506  1.000000     0.843461\n",
       "text length -0.857664  0.699881  0.843461     1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**using seaborn to create a heatmap based off that .corr() dataframe:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13adf67d278>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD3CAYAAAAjdY4DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4IrVsTroAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13adf6241d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(stars.corr(),cmap='coolwarm',annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NLP Classification Task\n",
    "\n",
    "Let's move on to the actual task. \n",
    "\n",
    "**Creating a dataframe called yelp_class that contains the columns of yelp dataframe but for only the 1 or 5 star reviews.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp_class = yelp[(yelp['stars']==1)|(yelp['stars']==5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>business_id</th>\n",
       "      <th>date</th>\n",
       "      <th>review_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>text</th>\n",
       "      <th>type</th>\n",
       "      <th>user_id</th>\n",
       "      <th>cool</th>\n",
       "      <th>useful</th>\n",
       "      <th>funny</th>\n",
       "      <th>text length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9yKzy9PApeiPPOUJEtnvkg</td>\n",
       "      <td>2011-01-26</td>\n",
       "      <td>fWKvX83p0-ka4JS3dc6E5A</td>\n",
       "      <td>5</td>\n",
       "      <td>My wife took me here on my birthday for breakf...</td>\n",
       "      <td>review</td>\n",
       "      <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZRJwVLyzEJq1VAihDhYiow</td>\n",
       "      <td>2011-07-27</td>\n",
       "      <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n",
       "      <td>5</td>\n",
       "      <td>I have no idea why some people give bad review...</td>\n",
       "      <td>review</td>\n",
       "      <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n",
       "      <td>2010-05-27</td>\n",
       "      <td>G-WvGaISbqqaMHlNnByodA</td>\n",
       "      <td>5</td>\n",
       "      <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>uZetl9T0NcROGOyFfughhg</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6ozycU1RpktNG2-1BroVtw</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n",
       "      <td>5</td>\n",
       "      <td>General Manager Scott Petello is a good egg!!!...</td>\n",
       "      <td>review</td>\n",
       "      <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>zp713qNhx8d9KCJJnrw1xA</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>riFQ3vxNpP4rWLk_CSri2A</td>\n",
       "      <td>5</td>\n",
       "      <td>Drop what you're doing and drive here. After I...</td>\n",
       "      <td>review</td>\n",
       "      <td>wFweIWhv2fREZV_dYkz_1g</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id        date               review_id  stars  \\\n",
       "0  9yKzy9PApeiPPOUJEtnvkg  2011-01-26  fWKvX83p0-ka4JS3dc6E5A      5   \n",
       "1  ZRJwVLyzEJq1VAihDhYiow  2011-07-27  IjZ33sJrzXqU-0X6U8NwyA      5   \n",
       "3  _1QQZuf4zZOyFCvXc0o6Vg  2010-05-27  G-WvGaISbqqaMHlNnByodA      5   \n",
       "4  6ozycU1RpktNG2-1BroVtw  2012-01-05  1uJFq2r5QfJG_6ExMRCaGw      5   \n",
       "6  zp713qNhx8d9KCJJnrw1xA  2010-02-12  riFQ3vxNpP4rWLk_CSri2A      5   \n",
       "\n",
       "                                                text    type  \\\n",
       "0  My wife took me here on my birthday for breakf...  review   \n",
       "1  I have no idea why some people give bad review...  review   \n",
       "3  Rosie, Dakota, and I LOVE Chaparral Dog Park!!...  review   \n",
       "4  General Manager Scott Petello is a good egg!!!...  review   \n",
       "6  Drop what you're doing and drive here. After I...  review   \n",
       "\n",
       "                  user_id  cool  useful  funny  text length  \n",
       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0          889  \n",
       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0         1345  \n",
       "3  uZetl9T0NcROGOyFfughhg     1       2      0          419  \n",
       "4  vYmM4KTsC8ZfQBg-j5MWkw     0       0      0          469  \n",
       "6  wFweIWhv2fREZV_dYkz_1g     7       7      4         1565  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp_class.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Creating two objects X and y. X will be the 'text' column of yelp_class and y will be the 'stars' column of yelp_class. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = yelp_class['text']\n",
    "y=yelp_class['stars']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing CountVectorizer and create a CountVectorizer object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Using the fit_transform method on the CountVectorizer object and pass in X (the 'text' column). Save this result by overwriting X.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=cv.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split\n",
    "\n",
    "Let's split our data into training and testing data.\n",
    "\n",
    "** Using train_test_split to split up the data into X_train, X_test, y_train, y_test. Use test_size=0.3 and random_state=101 **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Model\n",
    "\n",
    "Time to train a model!\n",
    "\n",
    "** Import MultinomialNB and create an instance of the estimator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now fit nb using the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluations\n",
    "\n",
    "Time to see how our model did!\n",
    "\n",
    "**Using the predict method off of nb to predict labels from X_test.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Creating a confusion matrix and classification report using these predictions and y_test **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[156  77]\n",
      " [ 37 956]] \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.81      0.67      0.73       233\n",
      "          5       0.93      0.96      0.94       993\n",
      "\n",
      "avg / total       0.90      0.91      0.90      1226\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,prediction),'\\n')\n",
    "print(classification_report(y_test,prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Great! Let's see what happens if we try to include TF-IDF to this process using a pipeline.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Text Processing\n",
    "\n",
    "** Import TfidfTransformer from sklearn. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import Pipeline from sklearn. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a pipeline with the following steps:CountVectorizer(), TfidfTransformer(),MultinomialNB()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline([('bag_words',CountVectorizer()),\n",
    "                ('tfidf', TfidfTransformer()),\n",
    "                ('classfier',MultinomialNB())\n",
    "                ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the Pipeline\n",
    "\n",
    "**Time to use the pipeline! **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split\n",
    "\n",
    "**Redo the train test split on the yelp_class object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = yelp_class['text']\n",
    "y= yelp_class['stars']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now fit the pipeline to the training data. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bag_words', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        ...tf=False, use_idf=True)), ('classfier', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluation\n",
    "\n",
    "** using the pipeline to predict from the X_test and create a classification report and confusion matrix. You should notice strange results.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1 232]\n",
      " [  0 993]] \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       1.00      0.00      0.01       233\n",
      "          5       0.81      1.00      0.90       993\n",
      "\n",
      "avg / total       0.85      0.81      0.73      1226\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predict),'\\n')\n",
    "print(classification_report(y_test,predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Nobuo shows his unique talents with everything on the menu. Carefully crafted features with much to drink. Start with the pork belly buns and a stout. Then go on until you can no longer.'"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp_class['text'].iloc[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# removing stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def text_process(m):\n",
    "    nop = [c for c in m if c not in string.punctuation]\n",
    "    nop = ''.join(nop)\n",
    "    return [w for w in m.split() if w.lower() not in stopwords.words('english')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [wife, took, birthday, breakfast, excellent., ...\n",
       "1    [idea, people, give, bad, reviews, place., goe...\n",
       "3    [Rosie,, Dakota,, LOVE, Chaparral, Dog, Park!!...\n",
       "4    [General, Manager, Scott, Petello, good, egg!!...\n",
       "6    [Drop, you're, drive, here., ate, go, back, ne...\n",
       "Name: text, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp_class['text'].head(5).apply(text_process)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipe2 = Pipeline([('bag', CountVectorizer(analyzer=text_process)),\n",
    "                 ('tfid',TfidfTransformer()),\n",
    "                 ('classfier',MultinomialNB())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bag', CountVectorizer(analyzer=<function text_process at 0x0000013ADF92DF28>,\n",
       "        binary=False, decode_error='strict', dtype=<class 'numpy.int64'>,\n",
       "        encoding='utf-8', input='content', lowercase=True, max_df=1.0,\n",
       "        max_features=None, min_df=1, ngram_range=(1, 1), preprocesso...tf=False, use_idf=True)), ('classfier', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predpipe2 = pipe2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0 233]\n",
      " [  0 993]] \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.00      0.00      0.00       233\n",
      "          5       0.81      1.00      0.89       993\n",
      "\n",
      "avg / total       0.66      0.81      0.72      1226\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prade\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predpipe2),'\\n')\n",
    "print(classification_report(y_test,predpipe2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipe3 = Pipeline([('bag', CountVectorizer(analyzer=text_process)),\n",
    "                 ('tfid',TfidfTransformer()),\n",
    "                 ('classfier',SVC())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('bag', CountVectorizer(analyzer=<function text_process at 0x0000013ADF92DF28>,\n",
       "        binary=False, decode_error='strict', dtype=<class 'numpy.int64'>,\n",
       "        encoding='utf-8', input='content', lowercase=True, max_df=1.0,\n",
       "        max_features=None, min_df=1, ngram_range=(1, 1), preprocesso...,\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False))])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe3.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predpipe3 = pipe3.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0 233]\n",
      " [  0 993]] \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.00      0.00      0.00       233\n",
      "          5       0.81      1.00      0.89       993\n",
      "\n",
      "avg / total       0.66      0.81      0.72      1226\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\prade\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predpipe3),'\\n')\n",
    "print(classification_report(y_test,predpipe3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looks like Tf-Idf actually made things worse! That is it for this project."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}