Higher Accuracy of Spam Mail Prediction using Decision Tree Algorithm Comparing with K-Nearest Neighbor 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 endeavor will primarily collect samples from two groups. The Decision Tree Algorithm belongs to Group-1, while the K-Nearest Neighbor Algorithm belongs to Group-2. For both Algorithms, the same sample sizes were used. The G-Power in the test set will be at 80%. Result: Data is trained in the given model so that Machine learning can function effectively. Emails are used as inputs after the K-Nearest Neighbor Algorithm is applied, which gives us a probabilistic index and determines whether the email is spam or not. The Decision Tree Algorithm outperforms the K-Nearest Neighbor Algorithm, and our hypothesis is insignificant with a significance value of 0.268. Conclusion: These results were achieved through machine learning models such as K-Nearest Neighbor Algorithm, and Decision Tree Algorithms. In this paper, we have demonstrated that for the spam filtering method the most efficient algorithms are the Decision Tree Algorithm and K-Nearest Neighbor Algorithm given as they have the highest level of accuracy.