Spam filtering system based on machine learning

With the process of the continuous growth of Internet technology, more and more people are participating in Internet activities. In daily email exchanges, there are often a certain amount of spam or uncivilized language that causes trouble to email users. However, since it is necessary to avoid misjudging normal emails as spam, it is extremely important to compare and analyze the accuracy results of different models. This paper employs various machine learning algorithms, such as support vector machines, random forest, naive Bayes to test spam detection, and discusses the optimal solution to this problem based on the results. Besides, A comprehensive explanation of the principles of various algorithms in the field of machine learning is provided in this paper. After performing necessary data processing and model handling, their advantages and disadvantages are analyzed to seek the optimal solution for the spam classification problem. Additionally, based on the existing results, this paper proposes future perspectives and possible improvement methods.