Part 2 of My Dota 2 Match Outcome Predictor

[Project Update] Part 2 of My Dota 2 Match Outcome Predictor – Now Available!

Hey DevTalk community!

I just published the second part of my series on building a Dota 2 Match Outcome Predictor. This project combines machine learning with feature engineering to try and forecast match outcomes, using Python as the primary tool.

What’s New in Part 2:

• Dataset Enhancement: Exploring ways to add valuable context to our data and handle unique in-game events.
• Feature Engineering: Selecting and testing new features that could capture the game dynamics more accurately.
• Challenges and Technical Insights: From balancing data to working with game-specific features, I go over the issues I encountered and the solutions that helped.

Tools & Libraries Used:

Pandas, Scikit-Learn, and NumPy for data manipulation and ML, plus a few custom scripts to preprocess and enrich data.

If you’re into Python, gaming analytics, or machine learning, I’d love to hear your feedback on how I can improve the model. I’m especially interested in any tips on feature engineering and managing data with high dimensionality.

Check out the article here: https://medium.com/@masterhood13/building-a-dota-2-match-outcome-predictor-part-2-enhancing-the-dataset-and-adding-new-features-3522965de468

Looking forward to your thoughts and ideas!

python devtalk #MachineLearning #Dota2 #FeatureEngineering #PredictiveModeling