Answer Happy

explain each line
import numpy as np
import pandas as pd
yelp = pd.read_csv('yelp.csv')
yelp.head() #show the first 5 rows
yelp.info()
yelp.describe()
yelp['text length'] = yelp['text'].apply(len)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
%matplotlib inline
g = sns.FacetGrid(yelp,col='stars')
g.map(plt.hist,'text length')
sns.boxplot(x='stars',y='text length',data=yelp,palette='rainbow')
sns.countplot(x='stars',data=yelp,palette='rainbow')
stars = yelp.groupby('stars').mean()
stars
stars.corr()
sns.heatmap(stars.corr(),cmap='coolwarm',annot=True)
yelp_class = yelp[(yelp.stars==1) | (yelp.stars==5)]
X = yelp_class['text'] #create object colled x contain
text column of yelp_class
y = yelp_class['stars']
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(X) Use the fit_transform method
on the CountVectorizer object and pass in X (the 'text' column)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3,random_state=101)
from sklearn.naive_bayes import MultinomialNB
nb = MultinomialNB()
nb.fit(X_train,y_train)
predictions = nb.predict(X_test)
from sklearn.metrics import confusion_matrix,classification_report
print(confusion_matrix(y_test,predictions))
print('\n')
print(classification_report(y_test,predictions))
from sklearn.feature_extraction.text import TfidfTransformer
# import TfidfTransformer from sklearn which used to
convert a collection of raw documents to a matrix of TF-IDF
features.
from sklearn.pipeline import Pipeline # Import
Pipeline from sklearn
pipeline = Pipeline([
('bow', CountVectorizer()),
('tfidf', TfidfTransformer()),
('classifier', MultinomialNB()),
])