spamClassifier.py

import nltk
import pandas as pd

messages = pd.read_csv('D:/NLP/spam/SMSSpamCollection', sep='\t', names=['label','message'])
# print(messages['label'])
#data cleaning and preprocessing
import re
import nltk
nltk.download('stopwords')


from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

ps = PorterStemmer()
corpus = []
for i in range(0, len(messages)):
    review = re.sub('[^a-zA-Z]',' ', messages['message'][i])
    review = review.lower()
    review = review.split()
    
    review = [ps.stem(word) for word in review if not word in stopwords.words('english')]
    review = ' '.join(review)
    corpus.append(review)
    
#creating the Bag of Words model
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer(max_features=5000)
X = cv.fit_transform(corpus).toarray()

y = pd.get_dummies(messages['label'])
y = y.iloc[:,1].values

# Train test Split
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.20, random_state = 0)


#  Training model using Naive Bayes classifier
from sklearn.naive_bayes import MultinomialNB
spam_detect_model = MultinomialNB().fit(X_train, y_train)

y_pred = spam_detect_model.predict(X_test)



from sklearn.metrics import confusion_matrix
confusion_m = confusion_matrix(y_test,y_pred)

from sklearn.metrics import accuracy_score 
accuracy = accuracy_score(y_test, y_pred)