After exploring predictive modeling, I achieved a 53% accuracy in forecasting game outcomes—a promising step toward understanding competitive dynamics. This was made possible using a Decision Trees classification model, a method that aligns precision with simplicity to deliver meaningful insights.

To train the model, I utilized data from over 10,000 matches, carefully selecting players from Silver, Gold, and Platinum tiers to strike a balance between accessibility and competitiveness. This effort was backed by an entire year of rigorous data collection, ensuring the predictions are both meaningful and impactful.