As developers, we are always looking for interesting datasets to test our machine learning skills. Recently, I decided to tackle a complex and highly dynamic environment: local horse racing. Predicting sports or racing outcomes is notoriously difficult due to the sheer number of variables (weather conditions, past performance, jockey stats, etc.). This challenge led to the creation of my side project, altilineverir.com.tr , an AI-driven platform designed to analyze race data and calculate potential payouts in real-time. In this post, I want to share a high-level overview of how I structured the data pipeline and the logic behind the prediction engine. 1. Gathering and Cleaning the Data The first step of any AI project is data collection. I needed historical data spanning several years. The main challenge wasn't just scraping the data, but cleaning it. Racing data is often messy, with inconsistent name formatting and missing track conditions.…