This project focuses on detecting phishing emails using Natural Language Processing (NLP) techniques and machine learning models such as XGBoost and Random Forest.
The goal is to classify emails as either phishing or legitimate by analyzing their textual content.
To improve the model's performance and address class imbalance in the dataset, oversampling techniques were applied.
Built using Python, the system achieved an impressive accuracy rate of 98%, demonstrating its effectiveness in identifying phishing attempts.
This project provides a robust solution for enhancing email security by detecting phishing attacks.
1. Implement phishing email detection using NLP techniques to analyze email content.
2. Train machine learning models, including XGBoost and Random Forest, to classify emails as phishing or legitimate.
3. Utilize oversampling methods to balance the dataset and improve the model's accuracy.
4. Achieve high accuracy in phishing detection, with a target accuracy rate of 98% or higher.
5. Build the entire system using Python for flexibility and ease of implementation.
6. Test and validate the model on diverse datasets to ensure consistent performance.
7. Develop a practical solution for improving email security by identifying phishing emails in real-time.
