Spam Email Identification

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 .