Machine Learning Algorithms for the Detection of Email Spam Based on Python Implementation

The importance of email in daily communication has been highlighted due to the widespread of spam emails, which are a common problem. As a result, a large number of people continue to be vulnerable to fraudulent schemes. Even while Gmail has a spam filtering system, it's not always effective. False positives can occur when potentially dangerous emails pass through unnoticed or when valid messages are incorrectly tagged as spam. The results of this study have important implications for professionals working in digital forensics, law enforcement and computer forensics. Python was used in conjunction with necessary libraries such as NumPy, Matplotlib, scikit-learn, Flask, and pandas to make the inquiry easier. The five machine learning algorithms DT, RNN, CNN, Navies Bayes, and Bert card based exhibit their effectiveness in detecting the spam. Analysis and experimental results show that when compared to other algorithms, RNN and Bert Based algorithms have better accuracy.