Building a Dota 2 Match Outcome Predictor, Part 2: Enhancing the Dataset and Adding New Features

“The journey of continuous improvement is fueled by the desire to be better today than yesterday, and better tomorrow than today.” — Anonymous

In the first part of this series, I covered the foundational steps of building a Dota 2 match outcome predictor, focusing on data collection and basic model setup. This involved initial data preparation, selecting essential features, and creating a baseline model, alongside discussing insights and challenges faced in the process. These steps laid a strong groundwork for prediction, but there’s more we can do to increase the model’s effectiveness and usability. In Part 2, I’ll focus on enhancing the dataset and incorporating new user-facing features to refine prediction accuracy and usability, offering players a more interactive experience and deeper insights into match dynamics in Dota 2.

In Part 1 of this series, I laid the groundwork for predicting match outcomes in Dota 2 by gathering essential metrics, selecting basic features, and building an initial predictive model. While this provided a starting point, I knew there was more we could do to enhance the model’s predictive power by digging deeper into data refinement and feature engineering.

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