Kaggle-Yelp-Review-Classifiction-NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "**Read the yelp.csv file and set it as a dataframe called yelp.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp = pd.read_csv('yelp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   business_id  10000 non-null  object\n",
      " 1   date         10000 non-null  object\n",
      " 2   review_id    10000 non-null  object\n",
      " 3   stars        10000 non-null  int64 \n",
      " 4   text         10000 non-null  object\n",
      " 5   type         10000 non-null  object\n",
      " 6   user_id      10000 non-null  object\n",
      " 7   cool         10000 non-null  int64 \n",
      " 8   useful       10000 non-null  int64 \n",
      " 9   funny        10000 non-null  int64 \n",
      "dtypes: int64(4), object(6)\n",
      "memory usage: 781.4+ KB\n"
     ]
    }
   ],
   "source": [
    "yelp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a new column called \"text length\" which is the number of words in the text column.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp['text length'] = yelp['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We use FacetGrid from the seaborn library to create a grid of 5 histograms of text length based off of the star ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a244a6a10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(yelp, col = 'stars')\n",
    "g.map(plt.hist, 'text length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot of text length for each star category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24f33590>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x = 'stars', y = 'text length', data = yelp, palette = 'rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countplot of the number of occurrences for each type of star rating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a250e8c50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEECAYAAADDOvgIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAR3klEQVR4nO3df5BddXnH8fcum0DtbCK1qT8ogdEMzz9tqYklEYFEgaSR2jh26qCF0jAOYw1TUvH3BJN26A9nIKMCIo1ALMrIGBJb2wnsTFWIEZqaplNB+kQShdYKNdj8EKpxs9s/7sl42e/dcI333nOz+37NZHLO9zwnPNcZ97Pf8z3n3IHx8XEkSWo2WHcDkqT+YzhIkgqGgySpYDhIkgqGgySpMFR3A52wcOHC8dNOO63uNiTphPLoo4/uy8w5rY5NiXA47bTT2Lx5c91tSNIJJSKemOyYl5UkSQXDQZJUMBwkSQXDQZJUMBwkSQXDQZJUMBwkSQXDQZJU6MpDcBFxErABCOAIsBKYDXwR+FZVdmtm3hMRa4FLgFFgdWbuiIh5wEZgHHgEWJWZY93oVZJU6tbM4U0Amfk64MPAemA+sD4zl1R/7omI+cBiYCFwKXBLdf56YE1mng8MACu61Kc07Y2NjdbdQsdNxc/Ua12ZOWTmFyLiH6rdM4CngQVARMQKGrOH1cB5wEhmjgNPRsRQRMypah+ozt8KLAW2dKNXabobHBzi6/9+Q91tdNRrfuM9dbdwwuvamkNmjkbEp4GbgE3ADuC9mXkBsBdYC8wCDjSddojG5aeBKjCaxyRJPdLVBenMvAI4i8b6w0hm7qwObQFeDRwEhptOGQb2A2MtxiRJPdKVcIiIyyPig9XuczR+2G+OiHOqsQuBncB2YFlEDEbEXGAwM/cBuyJiSVW7HNjWjT4lSa1165Xdm4E7I+JBYAaN9YX/BG6OiMPAU8BVmXkwIrYBD9EIqlXV+dcCGyJiJvAYjctSkqQe6daC9LPAW1scOrdF7Tpg3YSx3TTuYpIk1cCH4CRJBcNBklQwHCRJBcNBklQwHCSpMj56uO4WOu54P1O3bmWVpBPOwNBMnvrrt9fdRke97AN3H9d5zhwkSQXDQZJUMBwkSQXDQZJUMBwkSQXDQZJUMBwkSQXDQZJUMBwkSQXDQZJUMBwkSQXDQZJUMBwkSQXDQZJUMBwkSYWufJ9DRJwEbAACOAKsBAaAjcA48AiwKjPHImItcAkwCqzOzB0RMa9VbTd6lSSVujVzeBNAZr4O+DCwvvqzJjPPpxEUKyJiPrAYWAhcCtxSnV/UdqlPSVILXQmHzPwCcFW1ewbwNLAAeKAa2wpcBJwHjGTmeGY+CQxFxJxJaiVJPdK1NYfMHI2ITwM3AZuAgcwcrw4fAmYDs4ADTacdHW9VK0nqka4uSGfmFcBZNNYffqHp0DCwHzhYbU8cH2sxJknqka6EQ0RcHhEfrHafo/HD/usRsaQaWw5sA7YDyyJiMCLmAoOZuQ/Y1aJWktQjXblbCdgM3BkRDwIzgNXAY8CGiJhZbW/KzCMRsQ14iEZQrarOv3ZibZf6lCS10JVwyMxngbe2OLS4Re06YN2Esd2taiVJveFDcJKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkguEgSSoYDpKkwlCn/8GImAHcAZwJnAxcD/wX8EXgW1XZrZl5T0SsBS4BRoHVmbkjIuYBG4Fx4BFgVWaOdbpPSdLkujFzuAx4JjPPB5YDNwPzgfWZuaT6c09EzAcWAwuBS4FbqvPXA2uq8weAFV3oUZJ0DB2fOQCfBzY17Y8CC4CIiBU0Zg+rgfOAkcwcB56MiKGImFPVPlCduxVYCmzpQp+SpEl0fOaQmT/MzEMRMUwjJNYAO4D3ZuYFwF5gLTALONB06iFgNjBQBUbzmCSph7qyIB0RpwNfBu7KzLuBLZm5szq8BXg1cBAYbjptGNgPjLUYkyT1UMfDISJeCowA78/MO6rh+yPinGr7QmAnsB1YFhGDETEXGMzMfcCuiFhS1S4HtnW6R0nSsXVjzeFDwKnAdRFxXTX2buCjEXEYeAq4KjMPRsQ24CEaIbWqqr0W2BARM4HHeP76hSSpBzoeDpl5DXBNi0PntqhdB6ybMLabxl1MkqSa+BCcJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCkOd/gcjYgZwB3AmcDJwPfBNYCMwDjwCrMrMsYhYC1wCjAKrM3NHRMxrVdvpPiVJk2tr5hAR75iw/yfHKL8MeCYzzweWAzcD64E11dgAsCIi5gOLgYXApcAt1flFbfsfR5LUCcecOUTE24DfBV4fEW+ohk8Cfg34+CSnfR7Y1LQ/CiwAHqj2twJLgQRGMnMceDIihiJiziS1W36WDyVJ+vm80GWl+4DvAS8BbqvGxoA9k52QmT8EiIhhGiGxBrihCgGAQ8BsYBbwTNOpR8cHWtRKknromJeVMvN/M/MrmbkUeAz4NvAELzzjOB34MnBXZt5NI1COGgb2Awer7YnjrWolST3U7prDLcAO4HPAPdXfk9W+FBgB3p+Zd1TDuyJiSbW9HNgGbAeWRcRgRMwFBjNz3yS1kqQeavdupYXAK9u8a+hDwKnAdRFxXTV2DfDxiJhJYwayKTOPRMQ24CEaIbWqqr0W2NBc22aPkqQOaTccHgdOAZ57ocLMvIZGGEy0uEXtOmDdhLHdrWolSb3TbjjMBZ6IiMer/fHMPLdLPUmSatZuOLytq11IkvpKu+FwRYuxP+9kI5Kk/tFuODxd/T0AzMd3MknSlNZWOGTmbc37EbG1O+1IkvpBW+EQEWc17b6cxgK1JGmKaveyUvPM4UfAe7rQiySpT7R7Wen1EfES4FXA3upJZknSFNXu6zN+H/gajaefH46Iy7ralSSpVu3edfRuYEFmvhl4Na2fgJYkTRHthsPY0VdxZ+YhGusOkqQpqt0F6T0RcSPwIHA+x/g+B0nSia/dmcPfAD8ALgZW0vjqT0nSFNVuOKwHtmTm1cBvVfuSpCmq3XAYzcxvAmTmXp7/bW2SpCmm3TWHJyLiL2l8Mc85wHe715IkqW7tzhxWAv8DvBH4PnBl1zqSJNWu3SekfwR8tMu9SJL6hK/eliQVDAdJUsFwkCQV2r1b6WcWEQuBj2TmkoiYD3wR+FZ1+NbMvCci1gKXAKPA6szcERHzgI3AOPAIsCozvXVWknqoK+EQEe8DLgeerYbmA+sz88ammvnAYmAhcDpwLz99wG5NZn4lIj4JrAC2dKNPSVJr3Zo57AHeAtxV7S8AIiJW0Jg9rAbOA0Yycxx4MiKGImJOVftAdd5WYCmGgyT1VFfWHDLzXuAnTUM7gPdm5gXAXmAtMAs40FRzCJgNDFSB0TwmSeqhXi1Ib8nMnUe3aXwnxEFguKlmGNjP81/NcXRMktRDvQqH+yPinGr7QmAnsB1YFhGDETEXGKy+fnRXRCypapcD23rUo6aR0fEjdbfQFVP1c6n3una30gR/DNwcEYeBp4CrMvNgRGyj8b6mQWBVVXstsCEiZgKPAZt61KOmkaGBk/jY/vvqbqPjrnnxb9fdgqaIroVDZn4HWFRt/ytwbouadcC6CWO7adzFJEmqiQ/BSZIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKhoMkqWA4SJIKQ936hyNiIfCRzFwSEfOAjcA48AiwKjPHImItcAkwCqzOzB2T1XarT0lSqSszh4h4H/Ap4JRqaD2wJjPPBwaAFRExH1gMLAQuBW6ZrLYbPUqSJtety0p7gLc07S8AHqi2twIXAecBI5k5nplPAkMRMWeSWklSD3UlHDLzXuAnTUMDmTlebR8CZgOzgANNNUfHW9VKknqoVwvSzWsGw8B+4GC1PXG8Va0kqYd6FQ67ImJJtb0c2AZsB5ZFxGBEzAUGM3PfJLWSpB7q2t1KE1wLbIiImcBjwKbMPBIR24CHaITUqslqe9SjJKnStXDIzO8Ai6rt3TTuTJpYsw5YN2GsZa1+fofHxpg5OPUebZmqn0uqU69mDuoDMwcHecPDD9fdRsd9adGiuluQphx/3ZIkFQwHSVLBcJAkFQwHSVLBcJAkFQwHSVLBcJAkFQwHSVLBcJAkFaZ8OBwenZpfIjdVP5ek/jDlX58xc2iQpdfvrbuNjhtZ88q6W5A0hU35mYMk6WdnOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCoaDJKlgOEiSCj19fUZE7AIOVLvfBm4DPgaMAiOZ+WcRMQh8Ajgb+DHwjsx8vJd9StJ017NwiIhTADJzSdPYvwG/B+wF/jEi5gNnAqdk5msjYhFwI7CiV31Kkno7czgbeFFEjFT/3XXAyZm5ByAi7gcuBF4O3AeQmQ9HxGt62KMkid6uOTwH3AAsA94J3FmNHXUImA3M4qeXngCORMSUf3usJPWTXv7Q3Q08npnjwO6IOAD8UtPxYWA/8KJq+6jBzBztXZuSpF7OHK6ksX5ARLyCRgg8GxGviogBGjOKbcB24I1V3SLgGz3sUZJEb2cOtwMbI+KrwDiNsBgDPgucRONupX+OiH8BLo6IrwEDwMoe9ihJoofhkJmHgbe3OLRoQt0YjTUJSVJNfAhOklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQwHCRJBcNBklQYqruBViJiEPgEcDbwY+Admfl4vV1J0vTRrzOHNwOnZOZrgQ8AN9bcjyRNK/0aDucB9wFk5sPAa+ptR5Kml768rATMAg407R+JiKHMHG1V/Oijj+6LiCd601p/iLvq7qB/RN0N9JFP1N1A39hQdwP9485j/j/kjMkO9Gs4HASGm/YHJwsGgMyc0/2WJGn66NfLStuBNwJExCLgG/W2I0nTS7/OHLYAF0fE14ABYGXN/UjStDIwPj5edw+SpD7Tr5eVJEk1MhwkSQXDQZJU6NcF6RNORCwEPpKZS+rupS4RMQO4AzgTOBm4PjP/vtamahIRJ9G42T6AI8DKzNxTb1f1iohfAXYCF2fmf9TdT10iYhc/fY7r25nZlzfcGA4dEBHvAy4Hnq27l5pdBjyTmZdHxEuAXcC0DAfgTQCZ+bqIWAKsB1bU2lGNql8cbgP+r+5e6hQRpwCcCL9EelmpM/YAb6m7iT7weeC6pv1JH1yc6jLzC8BV1e4ZwNM1ttMPbgA+Cfx33Y3U7GzgRRExEhFfqp7j6kuGQwdk5r3AT+ruo26Z+cPMPBQRw8AmYE3dPdUpM0cj4tPATTT+95iWIuKPgO9n5v1199IHnqMRlMuAdwKfjYi+vIJjOKijIuJ04MvAXZl5d9391C0zrwDOAjZExC/W3U9NrqTxUOtXgN8E/jYiXlZvS7XZDXwmM8czczfwDPDymntqqS8TSyemiHgpMAJcnZn/VHc/dYqIy4Ffzcy/ovHb4hiNhelpJzMvOLpdBcQ7M/Op+jqq1ZXArwPviohX0HjJ6Pfqbak1w0Gd9CHgVOC6iDi69rA8M6fjIuRm4M6IeBCYAazOzB/V3JPqdzuwMSK+CowDVx7rpaJ18vUZkqSCaw6SpILhIEkqGA6SpILhIEkqGA6SpILhIHVIRFxddw9SpxgOUudM69eFaGrxOQfpOETEWcBGGu/UGgW+BKwFPgV8oPr7xcAvAxsy89bq6eDv03hQcBVwZ9P5f5iZ3+3tp5Am58xBOj4X0/hugouAvwD+DvhBZr4LmAd8LjOXAr8DvLvpvLsz86LqvObzT+1h79ILMhyk43M7sA+4D7ia57+e/CngzRHxGRqXmmY0Hcs2zpdqZzhIx2cFsC0zL6TxPRbvBwaqY+8BHsrMy6pjA03njR3jfKlv+OI96fh8HfhMRIzS+IH/p8CZ1WzhduDWiPgDGq9kHo2Ik9s4X+obLkhLkgpeVpIkFQwHSVLBcJAkFQwHSVLBcJAkFQwHSVLBcJAkFf4fMBnhc9iZYzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'stars', data = yelp, palette = 'rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We find the mean values of the numerical columns"
   ]
  },
  {
   "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>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars = yelp.groupby('stars').mean()\n",
    "stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We find the corr() between numerical values"
   ]
  },
  {
   "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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap of correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2534dbd0>"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.heatmap(stars.corr(), cmap = 'coolwarm', annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NLP Classification\n",
    "\n",
    "\n",
    "\n",
    "### We want a dataframe that contains the columns of only the 1 or 5 star reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp_class = yelp[(yelp.stars==1) | (yelp.stars==5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We create two objects X and y as our features and target/labels\n",
    "### X will be the 'text' column of yelp_class and y will be the 'stars' column of yelp_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = yelp_class['text']\n",
    "y = yelp_class['stars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a CountVectorizer object from sklearn.feature_extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We use the fit_transform method on the CountVectorizer object and pass in X ('text' column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cv.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "\n",
    "### We import MultinomialNB and create an instance of the estimator and call is nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "nb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now fit nb using the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluations\n",
    "\n",
    "\n",
    "### We use the predict method off of nb to predict labels from X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix and classification report using these predictions and y_test **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[159  69]\n",
      " [ 22 976]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.88      0.70      0.78       228\n",
      "           5       0.93      0.98      0.96       998\n",
      "\n",
      "    accuracy                           0.93      1226\n",
      "   macro avg       0.91      0.84      0.87      1226\n",
      "weighted avg       0.92      0.93      0.92      1226\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we try to include TF-IDF to this process using a pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Text Processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import  TfidfTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We create a pipeline with the following steps:\n",
    "- CountVectorizer()\n",
    "- TfidfTransformer()\n",
    "- MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow', CountVectorizer()),  # strings to token integer counts\n",
    "    ('tfidf', TfidfTransformer()),  # integer counts to weighted TF-IDF scores\n",
    "    ('classifier', MultinomialNB()),  # train on TF-IDF vectors w/ Naive Bayes classifier\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the Pipeline\n",
    "\n",
    "### The pipeline has all the pre-process steps in it already\n",
    "### We just need to re-split the original data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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 = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now fit the pipeline to the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('bow',\n",
       "                 CountVectorizer(analyzer='word', binary=False,\n",
       "                                 decode_error='strict',\n",
       "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
       "                                 input='content', lowercase=True, max_df=1.0,\n",
       "                                 max_features=None, min_df=1,\n",
       "                                 ngram_range=(1, 1), preprocessor=None,\n",
       "                                 stop_words=None, strip_accents=None,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, vocabulary=None)),\n",
       "                ('tfidf',\n",
       "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
       "                                  sublinear_tf=False, use_idf=True)),\n",
       "                ('classifier',\n",
       "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0 228]\n",
      " [  0 998]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.00      0.00      0.00       228\n",
      "           5       0.81      1.00      0.90       998\n",
      "\n",
      "    accuracy                           0.81      1226\n",
      "   macro avg       0.41      0.50      0.45      1226\n",
      "weighted avg       0.66      0.81      0.73      1226\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Amogh/opt/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The TF-IDF actually made the predictions and accuracy worse!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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