🚀 Phishing URL Detection System – Machine Learning Project

Excited to share my latest cybersecurity & machine learning project where I built an intelligent system to detect phishing URLs in real time.

🔍 Project Overview This application analyzes website URLs using advanced feature extraction and a trained Random Forest model to determine whether a link is safe or phishing, along with a probability-based risk score.

🧠 Key Features • Real-time phishing detection via REST API • Machine learning model trained on multiple public datasets • Feature engineering using URL structure, entropy, domain patterns, and HTTPS signals • Risk probability scoring for better security decisions • Full-stack implementation with Python (Flask) backend and HTML/CSS/JS frontend

📊 Datasets Used • OpenPhish community phishing feed • PhishTank verified phishing URLs • Kaggle phishing website & URL datasets • Mendeley phishing detection research dataset

Combining multiple trusted datasets helped create a balanced and more accurate detection model, reducing false positives on legitimate sites like YouTube or Google.

💡 Tech Stack Python | Scikit-learn | Flask | HTML/CSS/JS | Pandas | Cybersecurity | Machine Learning

This project strengthened my skills in ML model training, data preprocessing, API development, and secure web application design.

Looking forward to feedback and collaboration opportunities in AI, cybersecurity, and full-stack development!

#MachineLearning #CyberSecurity #PhishingDetection #Python #HTML #AI #DataScience #OpenToWork