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Building an end-to-end machine learning system to predict Formula 1 race winners. From collecting historical race data via FastF1, engineering predictive features, training gradient boosting models, to deploying a FastAPI service—this project showcases the complete ML lifecycle with a focus on production-ready architecture.
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I set out to explore how Genetic Algorithms, inspired by natural selection, can evolve intelligent behavior in classic reinforcement learning environments. Through three increasingly complex challenges, I built and evolved neural networks from scratch using simple yet powerful evolutionary strategies. Wether you’re curious about evolutionary computation, preparing for your next machine learning project, or just watching agents go from clueless to competent, this deep dive is for you.
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Designing and implementing a 5-layer decision support system using MILP, predictive scoring, Monte Carlo simulation, and genetic algorithms to optimize a complex supply chain, increasing profitability by strategically scheduling pig transport.