Higher Accuracy of Spam Mail Prediction using Random Forest Algorithm Comparing with Multinomial Naive Bayes Algorithm

Abstract

Aim: To make an innovative spam prediction of spam emails using Machine learning modeling techniques and to evaluate their performance. Materials and Methods: The experiment will primarily collect samples from two groups. The Random Forest Algorithm belongs to Group-1, while the Multinomial Naive Bayes Algorithm belongs to Group-2. The sample sizes were all taken at the same time for both the Algorithms. The G-Power in the test set will be at 80%. Result: Data is processed in the given model so that Machine learning can function effectively. Emails are used as inputs for the Multinomial Naive Bayes algorithm, which generates a probabilistic index and determines whether the email is spam or not. The Random Forest Algorithm outperforms the Multinomial Naive Bayes Algorithm, and our hypothesis is significant with a significance value of 0.002 (p<0.1). Conclusion:  These results were achieved through machine learning models such as Multinomial Naive Bayes, and Random Forest Algorithms. In this paper, here demonstrated that for the spam filtering method the most efficient algorithms are Random Forest Algorithm and MNB were given as they have the highest level of accuracy.