SMS spam continues to become a problem on the Internet. Spammed SMS may contain many copies of the same message, commercial advertisement or other irrelevant posts like pornographic content. In previous research, different filtering techniques are used to detect these SMS such as using Random Forest, Na¨ıve Bayesian, Support Vector Machine (SVM) and Neutral Network. In this research, we test Na¨ıve Bayes algorithm for SMS spam filtering on one dataset and test its performance, i.e., Spam Data. The performance of the dataset is evaluated based on their accuracy, precision.
LENGTH ANALYSIS BETWEEN SPAM AND HAM MESSAGES
LENGTH ANALYSIS BETWEEN SPAM AND HAM MESSAGES
LENGTH ANALYSIS BETWEEN SPAM AND HAM MESSAGES
LENGTH ANALYSIS BETWEEN SPAM AND HAM MESSAGES
comparision
Done backend, flask and some frontend work.
Done backend, flask and some frontend work.
Done backend, flask and some frontend work.
Deals with frontend work and flask.
Done the coding part and backend.
Deals with frontend work and flask.
Done backend, flask and some frontend work.
Deals with frontend work and flask.
