SMS SPAM DETECTION USING MACHINE LEARNING

SMS SPAM DETECTION USING MACHINE LEARNING

[1] Mr. E.Sankar, Assistant Professor, CSE Department,
Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya,
Enathur, Kancheepuram.

[2] Y Y S Shekhar Babu, B.E, 4^th Year, CSE Branch,
Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya,
Enathur, Kancheepuram.

[3] M.Tridev, B.E, 4^th Year, CSE Branch,
Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya,
Enathur, Kancheepuram.

ABSTRACT:             In recent times, there has been considerable interest among people to use short communication service (SMS) as one of the essential and straightforward dispatches services on mobile bias. The increased popularity of this service also increased the number of mobile bias attacks similar as SMS spam dispatches. SMS spam dispatches constitute a real problem to mobile subscribers. this worries telecommunication service providers as it disturbs their guests and causes them to lose business. to enhanced SMS spam filtering performance by combining two of data mining task association and bracket. FP- growth in association is employed for mining frequent pattern on SMS and Naive Bayes Classifier is used to classify whether SMS is spam or ham. Training data was using SMS spam collection from former exploration. The result of using collaboration of Naive Bayes and FP- Growth performs the highest average accuracy of 98,506 and 0.025% better than without using FP- Growth for dataset SMS Spam Collection v. 1, and improves the perfection score; therefore, the bracket result is more accurate.

Keywords: Spam, Machine Learning, Naïve Bayes Algorithm, SMS (Short Message Service), Vectorizer.