https://github.com/sayande01/phising_url_detection_randomforest

This project builds a robust binary classification system to detect phishing websites using URL features like SSL state, web traffic, and domain patterns. Achieving 97% accuracy, 96.8% precision, and 0.9953 ROC-AUC, it ensures reliable, efficient detection with interpretable insights.

binary-classification logistic-regression random-forest-classifier roc-analysis svc

Last synced: 06 Apr 2025