Voting Classification Method for Email Spam Prediction

E-mail customers get several hundred spam messages on regular basis with a fresh content, from fresh addresses which are produced by automated programming tools. In real time, it is quite unrealistic to filter spam through outdated methodologies like dark-white catalogs. The use of text mining schemes on electronic mails can make filter the spam emails more competently. The email spam detection techniques have various phases. The pre-processing will clean up the data-set and feature extraction phase will identify traits affecting the assigned set to the greatest extent possible. The combination of multiple classifiers is used in this phase for the classification such as SVM, NB, KNN and Random Forest. The parameters used for the evaluation of developed architecture include recall, accuracy, and precision. This work implements new methodology in the python software The results of proposed model show high improvement for the email spam prediction.