Processing Yelp Star Classifiers with NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This uses the [Yelp Review Data Set from Kaggle](https://www.kaggle.com/c/yelp-recsys-2013).\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Science imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Viz imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Config matplotlib\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"patch.force_edgecolor\"] = True # in matplotlib, edge borders are turned off by default. \n",
    "sns.set_style(\"darkgrid\") # set a grey grid as a background\n",
    "\n",
    "# ML imports\n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "# Linear Regression \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# Logistic Regression\n",
    "#\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# turn off warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# \n",
    "import nltk\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing yelp data from a csv\n",
    "\n",
    "**Read the yelp.csv file and set it as a dataframe called yelp.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp = pd.read_csv(\"yelp.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exploratory DataFrame Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": 6,
     "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp['text length'] = yelp['text'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporatory Data Analysis using Seaborn\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.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11f7f3f28>"
      ]
     },
     "execution_count": 14,
     "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',bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a boxplot of text length for each star category.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1207b70f0>"
      ]
     },
     "execution_count": 17,
     "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": "markdown",
   "metadata": {},
   "source": [
    "**Creating a countplot of the number of occurrences for each type of star rating.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x120ded860>"
      ]
     },
     "execution_count": 21,
     "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.countplot(x='stars',data=yelp,palette='rainbow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12578fc88>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8APhyRBwFzAcez8wzgTuAhU3WKknaX9OXm34GfKhl/7TM7K23VwDnAKcDfZm5MzM3AWuA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\nISFJKjIkJElF/xcZofnjpeOA5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12265cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='stars',y=yelp['stars'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using groupby to get the mean values of the numerical columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.groupby('stars').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using the corr() method on that groupby dataframe to produce this dataframe:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.groupby('stars').mean().corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Then use seaborn to create a heatmap based off that .corr() dataframe:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bef8ba8>"
      ]
     },
     "execution_count": 31,
     "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(yelp.groupby('stars').mean().corr(),cmap='coolwarm',linewidth=1,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying with NLP\n",
    "\n",
    " Get only reviews that were either 1 star or 5 stars.\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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp_class = yelp[(yelp['stars'] == 1) | (yelp['stars'] == 5) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>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",
       "  </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",
       "\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",
       "\n",
       "                  user_id  cool  useful  funny  text length  \n",
       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0          889  \n",
       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0         1345  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp_class.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create two objects X and y. X will be the 'text' column of yelp_class and y will be the 'stars' column of yelp_class. (Your features and target/labels)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=yelp_class['text']\n",
    "y=yelp_class['stars']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a CountVectorizer object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer()\n",
    "cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Use 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": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = cv.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Train Test Split step\n",
    "\n",
    "**spliting 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": 52,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Training the Model\n",
    "\n",
    "**creating an instance of the estimator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**fitting nb using the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions and Evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding a confusion matrix and classification report using these predictions and y_test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              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",
      "   micro avg       0.93      0.93      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",
      "\n",
      "\n",
      "Confusion Matrix\n",
      "[[159  69]\n",
      " [ 22 976]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))\n",
    "print('\\n')\n",
    "print('Confusion Matrix')\n",
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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",
      "avg / total       0.92      0.93      0.92      1226\n",
      "\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Text Processing\n",
    "\n",
    "**including TF-IDF to this process using a pipeline.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**creating a pipeline with the following steps:CountVectorizer(), TfidfTransformer(),MultinomialNB()**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline2 = Pipeline([\n",
    "    ('bow',CountVectorizer()),\n",
    "    ('tfidf',TfidfTransformer()),\n",
    "    ('classifier',MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Pipeline\n",
    "\n",
    "**The pipeline has all the pre-process steps in it already, so re-spliting the original data**"
   ]
  },
  {
   "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": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2=yelp_class['text']\n",
    "y2=yelp_class['stars']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X2, y2, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now fit the pipeline to the training data. Remember you can't use the same training data as last time because that data has already been vectorized. We need to pass in just the text and labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('bow', 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",
       "        strip_...obs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False))])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.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.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions2 = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.82      0.43      0.56       228\n",
      "           5       0.88      0.98      0.93       998\n",
      "\n",
      "   micro avg       0.88      0.88      0.88      1226\n",
      "   macro avg       0.85      0.70      0.74      1226\n",
      "weighted avg       0.87      0.88      0.86      1226\n",
      "\n",
      "\n",
      "\n",
      "Confusion Matrix\n",
      "[[ 97 131]\n",
      " [ 22 976]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions1))\n",
    "print('\\n')\n",
    "print('Confusion Matrix')\n",
    "print(confusion_matrix(y_test,predictions1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.88      0.37      0.52       228\n",
      "           5       0.87      0.99      0.93       998\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      1226\n",
      "   macro avg       0.87      0.68      0.73      1226\n",
      "weighted avg       0.87      0.87      0.85      1226\n",
      "\n",
      "\n",
      "\n",
      "Confusion Matrix\n",
      "[[ 85 143]\n",
      " [ 12 986]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions2))\n",
    "print('\\n')\n",
    "print('Confusion Matrix')\n",
    "print(confusion_matrix(y_test,predictions2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice that Tf-Idf actually gave worse predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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