The Naive Bayes Tutorial

The Naive Bayes Tutorial: Email spam filter
Author

Benedict Thekkel 

import pandas as pd
df = pd.read_csv("Data/spam.csv")
df.head()
Category Message
0 ham Go until jurong point, crazy.. Available only ...
1 ham Ok lar... Joking wif u oni...
2 spam Free entry in 2 a wkly comp to win FA Cup fina...
3 ham U dun say so early hor... U c already then say...
4 ham Nah I don't think he goes to usf, he lives aro... 
df.groupby('Category').describe()
Message
count unique top freq
Category
ham 4825 4516 Sorry, I'll call later 30
spam 747 641 Please call our customer service representativ... 4 
df['spam']=df['Category'].apply(lambda x: 1 if x=='spam' else 0)
df.head()
Category Message spam
0 ham Go until jurong point, crazy.. Available only ... 0
1 ham Ok lar... Joking wif u oni... 0
2 spam Free entry in 2 a wkly comp to win FA Cup fina... 1
3 ham U dun say so early hor... U c already then say... 0
4 ham Nah I don't think he goes to usf, he lives aro... 0 
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(df.Message,df.spam)
from sklearn.feature_extraction.text import CountVectorizer
v = CountVectorizer()
X_train_count = v.fit_transform(X_train.values)
X_train_count.toarray()[:2]
array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]])
from sklearn.naive_bayes import MultinomialNB
model = MultinomialNB()
model.fit(X_train_count,y_train)
MultinomialNB()
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emails = [
    'Hey mohan, can we get together to watch footbal game tomorrow?',
    'Upto 20% discount on parking, exclusive offer just for you. Dont miss this reward!'
]
emails_count = v.transform(emails)
model.predict(emails_count)
array([0, 1])
X_test_count = v.transform(X_test)
model.score(X_test_count, y_test)
0.9863603732950467

Sklearn Pipeline

from sklearn.pipeline import Pipeline
clf = Pipeline([
    ('vectorizer', CountVectorizer()),
    ('nb', MultinomialNB())
])
clf.fit(X_train, y_train)
Pipeline(steps=[('vectorizer', CountVectorizer()), ('nb', MultinomialNB())])
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clf.score(X_test,y_test)
0.9863603732950467
clf.predict(emails)
array([0, 1])
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