20-NLP Project.ipynb

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   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Natural Language Processing Project\n",
    "\n",
    "Welcome to the NLP Project for this section of the course. 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. This will be a simpler procedure than the lecture, since we will utilize the pipeline methods for more complex tasks.\n",
    "\n",
    "We will use 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.\n",
    "\n",
    "Let's get started! Just follow the directions below!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    " **Import the usual suspects. :) **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "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(\"data/yelp.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head, info , and describe methods on yelp.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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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",
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       "    <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",
       "  </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",
       "\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",
       "\n",
       "                  user_id  cool  useful  funny  \n",
       "0  rLtl8ZkDX5vH5nAx9C3q5Q     2       5      0  \n",
       "1  0a2KyEL0d3Yb1V6aivbIuQ     0       0      0  \n",
       "2  0hT2KtfLiobPvh6cDC8JQg     0       1      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yelp.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   "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: 781.4+ KB\n"
     ]
    }
   ],
   "source": [
    "yelp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
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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": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "Let's explore the data\n",
    "\n",
    "## Imports\n",
    "\n",
    "**Import the data visualization libraries if you haven't done so already.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use 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": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f8dc5e718d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(yelp,col='stars', palette=\"husl\")\n",
    "g.map(plt.hist,'text length')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a boxplot of text length for each star category.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8dc5ec6390>"
      ]
     },
     "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='stars', y='text length', data=yelp, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a 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 0x7f8dc509c710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"stars\", data=yelp, palette=\"husl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Use 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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stars = yelp.groupby('stars').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use the corr() method on that groupby dataframe to produce this dataframe:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead 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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Then use seaborn to create a heatmap based off that .corr() dataframe:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8dc5022710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(stars.corr(), annot=True, fmt=\"f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NLP Classification Task\n",
    "\n",
    "Let's move on to the actual task. To make things a little easier, go ahead and only grab reviews that were either 1 star or 5 stars.\n",
    "\n",
    "**Create 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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "yelp_class = yelp[(yelp.stars==1) | (yelp.stars==5)]"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = yelp_class['text']\n",
    "y = yelp_class['stars']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import CountVectorizer and create a CountVectorizer object.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "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": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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",
    "** Use 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": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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 a Model\n",
    "\n",
    "Time to train a model!\n",
    "\n",
    "** Import MultinomialNB and create an instance of the estimator and call is nb **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 22,
     "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",
    "**Use the predict method off of nb to predict labels from X_test.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Create a confusion matrix and classification report using these predictions and y_test **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8dbf3e2790>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test, pred),annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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",
      "    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(classification_report(y_test,predictions))"
   ]
  },
  {
   "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import  TfidfTransformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import Pipeline from sklearn. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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",
    "**Time to use the pipeline! Remember this pipeline has all your pre-process steps in it already, meaning we'll need to re-split the original data (Remember that we overwrote X as the CountVectorized version. What we need is just the text**"
   ]
  },
  {
   "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": 32,
   "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": "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": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluation\n",
    "\n",
    "** Now use 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": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "pred = pipeline.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8dbedff290>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test, pred),annot=True, fmt='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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",
      "    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(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like Tf-Idf actually made things worse! That is it for this project. But there is still a lot more you can play with:\n",
    "\n",
    "**Some other things to try....**\n",
    "Try going back and playing around with the pipeline steps and seeing if creating a custom analyzer like we did in the lecture helps (note: it probably won't). Or recreate the pipeline with just the CountVectorizer() and NaiveBayes. Does changing the ML model at the end to another classifier help at all?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}