Show HN: Production-ready ML loan prediction system (88.62% accuracy) (github.com)

A new production-ready AI loan prediction system has been launched, boasting an impressive accuracy of 88.62% using multiple trained machine learning models and a lightweight REST API. Built with Python 3.10+ and Flask, the system leverages PostgreSQL for production and SQLite for development. Features include thorough input validation, a comprehensive prediction history, and efficient database management using SQLAlchemy. Users can interact with the system through a public demo API that allows for loan approval predictions based on various applicant parameters. This development is significant for the AI/ML community as it offers a practical application of machine learning in finance, specifically in automating loan approval processes. The project's architecture is designed to ensure security and reliability, employing measures like input validation, rate limiting, and HTTPS enforcement. The model utilizes a Random Forest algorithm with 20 engineered features, enhancing its predictive capabilities. The API provides detailed analytics and response metrics, including an average response time of 150ms and a cache hit rate of 85%, making it not only accurate but also highly efficient for real-world applications.