• K.Uma Shankar
• G.Jaya Bharathi

In data mining, e-mail spam is a serious threat to business and industry. Reducing spam and preventing the accumulation of spam stored in the user's mailbox is a challenge for users. Identifying the best algorithm for classifying spam is an important task. In this context, we use decision tree algorithms to filter spam, because the primary job is to organize spam or ham mail. The algorithms are created; filtering algorithms previously tested is applied. The results of the different algorithms are evaluated from the accuracy, the error rate, the accuracy and the actual speed of the error. Comparing the above algorithms based on their performance shows that our proposed algorithm performs better than other classifiers before and after applying weka filters.

e-mail spam, decision tree algorithm, data mining, and classification

"Email Spam Classification Based on Supervised Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.365-372, July-2018, Available :http://www.jetir.org/papers/JETIR1807773.pdf

"Email Spam Classification Based on Supervised Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp365-372, July-2018, Available at : http://www.jetir.org/papers/JETIR1807773.pdf