• Vutharkar Nagaveni Computer Science, Rai University, Saroda, Gujrat
  • Dr.Vimal Pandya Computer Science, Director Navgujarat College Of Computer Applications,


Decision tree, WEKA, dataset, classification algorithm, Email, Support vector Machine


Email  is the  system for sending messages from one individual to another via telecommunications links between computers or terminals using dedicated software applications. Nowadays, Email  is used most  common and effective mode  of communication  way  to communicate in personal, individual and professional  level. As increase of email users there will be  increase of spam emails from the  past few years. This paper explore   how email data was classified using three different classifiers (Naive Bayes classifier ,Support Vector Classifier,J48 Classifier) for detecting spam using WEKA. This  experiment was performed based on dataset to find spam  in different parameters like finding Accuracy,Recall,Precision,Fmeasures and False Position Rate  etc. The final classification result should be ‘1’ if it is finally spam present , otherwise, it should be ‘0’ for no spam. Finally  this  paper shows that  J48 classifier is best  and efficient algorithm for detection of spam emails for dataset that  classified as binary tree among other algorithms.


Bhat, Sajid Yousuf, Muhammad Abulaish, and Abdulrahman A. Mirza. "Spammer Classification Using Ensemble Methods over Structural Social Network Features." In Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02, pp. 454-458. IEEE Computer Society, 2014.

Trivedi, Shrawan Kumar, and Shubhamoy Dey. "Interplay between Probabilistic Classifiers and Boosting Algorithms for Detecting Complex Unsolicited Emails."Journal of Advances in Computer Networks 1, no. 2 (2013): 132-136.

Sneha Singh,Sandeep Kaur .”IMPROVED SPAMBASE DATASET PREDICTION USING SVM RBF KERNEL WITH ADAPTIVE BOOST “,IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 ,Volume: 04 Issue: 06 | June-2015.

Ghada Hammad AL-Rawashdeh Dr.Rabiei Bin Mamat “Comparison of four email classification algorithms using WEKA”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 17, No. 2, February 2019.

Sunil Ray, “ 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R “,,Sept 11th, 2017

Diksha S. Jawale,Ashwini G. Mahajan,Kalyani R. Shinkar and Vaishnavi V. Katdare “Hybrid spam detection using machine learning”,IJARIIT,ISSN: 2454-132X,Impact factor: 4.295,Volume 4, Issue 2,2018

Gaganjot Kaur,Amit Chhabra,”Improved J48 Classification Algorithm for the Prediction of Diabetes” ,International Journal of Computer Applications (0975 – 8887) Volume 98 – No.22, July 2014.

Emmanuel Gbenga Dada , “Machine learning for email spam filtering: review, approaches and open research problems “, Received 3 September 2018; Received in revised form 25 February 2019; Accepted 20 May 2019 ,2405-8440,Published by Elsevier Ltd.

K.F.Bindhia,Y.Vijayalakshmi, P.Manimegalai and Suvanam Sasidhar Babu,”Classification Using Decision Tree Approach towards Information Retrieval Keywords Techniques and a Data Mining Implementation Using WEKA Data Set “,International Journal of Pure and Applied Mathematics Volume 116 No. 22 2017, 19-29 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: .

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How to Cite

Vutharkar Nagaveni, & Dr.Vimal Pandya. (2019). EMAIL CLASSIFICATIONS FOR SPAM MAIL DETECTION BY COMPARING THREE DIFFERENT ALGORITHMS USING WEKA. International Education and Research Journal (IERJ), 5(11). Retrieved from