Spam Email Identification

Spam Email Identification

Prof.Rupali Dupade, Anand Hulsure, Aditya Todkar, Abhishek korpad, Prathamesh Kamble

Department of CSE

JSPM’s JSCOE,Pune, India

Abstract- The increasing volume of spam emails poses a significant challenge to email users, demanding efficient and accurate  methods for spam detection. the hybrid model leverages the ability of SVM to identify optimal hyperplanes for class  separation and the ensemble learning capability of Random Forest to make robust predictions. Through a  comprehensive evaluation using a labeled dataset, the proposed hybrid model is compared against individual SVM  and random forest algorithms. Experimental results demonstrate that the hybridization approach achieves superior  precision recall, accuracy and F1 Score for knowing .The findings highlight the potential of combining  complementary machine learning algorithms .The proposed hybrid model holds promise for improving email  filtering techniques, contributing to a more efficient and reliable email experience for users. Future research  directions include exploring additional algorithms and refining the hybridization process to further increase the  accuracy .

Keywords– Hybrid Machine Learning, Email spam,SVM, Random Forest