Efficient Spam Email Classification Using Machine Learning Algorithms

In today's digital age, since email is the main form of communication, the identification of email spam is a critical issue. In addition to consuming a lot of time and money, email spam is also a security and privacy risk. In this paper, we provide a means for email spam detection that employes machine learning Algorithms. The required features for training the ML models have been engineered after analysis of the email dataset of content-based filtering obtained from Kaggle website. We tested a Several types of algorithms for machine learning and analyzed their level of performance using the dataset. Our findings demonstrate how effective is the suggested approach in identifying email spam with highest accuracy of 99.8% and Rmse of 0.2. Here we applied, the various ML classifier algorithm such as Decision tree, Voting Classifier, Random Forest, Logistic Regression and so on to our dataset, compared among each other and found which suits best for the dataset with the highest accuracy. This method can be useful in email clients or servers to detect spam emails automatically and enhanced