A Machine Learning approach for Fake Profile Classification in Social Networking

This study suggests a machine learning-based detection system built with Python and Django to tackle the growing problem of fraudulent profiles on social networking sites. Malicious actors are progressively setting up phony identities for spamming, phishing, and disseminating false information as social media usage keeps growing. In order to accurately identify bogus profiles, the suggested system analyzes user attributes, behavioral patterns, and network properties using a variety of supervised learning algorithms, such as Random Forests, Support Vector Machines, and Decision Trees. Our approach makes advantage of Django's powerful web framework to produce an intuitive, scalable profile monitoring and analysis interface. According to experimental data, the overall detection accuracy is 92%, with 90% precision and 88% recall rates. The system greatly outperforms traditional rule-based approaches in both detection accuracy and processing efficiency, particularly when handling large datasets. The Django implementation provides real-time monitoring capabilities, reducing manual verification efforts while maintaining high detection reliability. This research contributes to enhancing online security by providing an effective tool for identifying and mitigating fake profile threats on social networking platforms.