Hybrid Model for Email Spam Prediction Using Random Forest for Feature Extraction

With the advancement in world wide web, the way to communicate among individuals, via internet, is changed and thus, various platforms become popular such as email. Numerous organizations and people make the deployment of email as major sources of communication. This platform is extensively utilized in spite of alternative means, such as electronic messages, and social networks. However, this technology is more prone to malicious activities. The malicious users target this free mail structure and send a huge number of useless messages, for attaining revenues, or stealing personal data or IDs, to harm its users. Thus, there is necessity to discover the methods for detecting the email spam. The spam is detected in email in different phases in which the data is pre-processed, features are extracted, and the mails are classified. This work introduced a new model to predict the email spam. This approach implements the random forest in order to extract the features. Eventually, the spam is predicted using logistic regression model. The proposed model is implemented in python using anaconda.