Count Vectorized Spam and Ham Discernment of Short Message Service using Machine Learning Classification
M. Shyamala Devi1, Kamma Rahul2, Manubolu Satheesh3, Koruprolu Rajasekhar4, Pittala Ganesh Kumar5
1M. Shyamala Devi, Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
2Kamma Rahul, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
3Manubolu Satheesh, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
4Koruprolu Rajasekhar, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
5Pittala Ganesh Kumar, III Year B.Tech Student, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, TamilNadu, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 557-561 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7287118419/2019©BEIESP | DOI: 10.35940/ijrte.D7287.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the growing volume and the amount of spam message, the demand for identifying the effective method for spam detection is in claim. The growth of mobile phone and Smartphone has led to the drastic increase in the SMS spam messages. The advancement and the clean process of mobile message servicing channel have attracted the hackers to perform their hacking through SMS messages. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the owners. With this background, this paper focuses on predicting the Spam SMS messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of Spam message detection is achieved in four ways. Firstly, the distribution of the target variable Spam Type the dataset is identified and represented by the graphical notations. Secondly, the top word features for the Spam and Ham messages in the SMS messages is extracted using Count Vectorizer and it is displayed using spam and Ham word cloud. Thirdly, the extracted Counter vectorized feature importance SMS Spam Message detection dataset is fitted to various classifiers like KNN classifier, Random Forest classifier, Linear SVM classifier, Ada Boost classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Multinomial Naive Bayes classifier have achieved the effective prediction with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.20%..
Keywords: Machine Learning, Recall, FScore, Accuracy and AUC Score.
Scope of the Article: Machine Learning.