CountVectorizer and Bayes Model.py

import pandas as pd
import numpy as np
import nltk
from nltk.corpus import stopwords
%matplotlib inline
import string
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import confusion_matrix, classification_report
import pickle


def text_process(text):
    '''
    Takes in a string of text, then performs the following:
    1. Remove all punctuation
    2. Remove all stopwords
    3. Return the cleaned text as a list of words
    '''
    nopunc = [char for char in text if char not in string.punctuation]
    nopunc = ''.join(nopunc)
    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]
yelp = pd.read_csv('yelp.csv')
yelp_class = yelp[(yelp['stars'] == 1) | (yelp['stars'] == 5)]
X = yelp_class['text']
y = yelp_class['stars']
Count_transformer = CountVectorizer(analyzer=text_process).fit(X)
X = Count_transformer.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
nb = MultinomialNB()
nb.fit(X_train, y_train)
preds = nb.predict(X_test)


print(classification_report(y_test, preds))