Email froud detection

This project aims to detect fraudulent emails by analyzing their content using machine learning techniques. The process involves tokenizing email text into individual words or phrases, which are then used as features for training a classification model. Common algorithms like Logistic Regression, Naive Bayes, or advanced models like LSTM (Long Short-Term Memory) can be employed to distinguish between legitimate and fraudulent emails. Key steps include preprocessing (e.g., removing stop words, stemming), feature extraction (e.g., TF-IDF or word embeddings), and model evaluation using metrics like precision, recall, and F1-score. The final system can flag suspicious emails in real-time, helping users avoid phishing scams, spam, and other fraudulent activities.