Hey Dev.to community!
I just published the second part of my series on creating a Dota 2 Match Outcome Predictor using Python and machine learning. In this post, I take a deeper dive into the dataset by enriching it and engineering new features to boost prediction accuracy.
🔍 What’s Covered in Part 2:
Dataset Enhancement: Techniques I used to add depth to the dataset and capture essential in-game factors.
Feature Engineering: I experimented with various features to find those that most impact prediction accuracy.
Challenges and Lessons Learned: Handling imbalanced data, processing complex game data, and navigating the trial and error of feature selection.
📚 Tech Stack:
Python (Pandas, NumPy, Scikit-Learn) was essential for data processing, feature engineering, and model development. I also used custom scripts to preprocess game data and handle large datasets.
I’d love for you to check it out if you’re interested in gaming analytics, Python, or machine learning, and I’m open to feedback! Especially interested in any tips on dataset enrichment and feature engineering for complex models.
👉 Read it here: https://medium.com/@masterhood13/building-a-dota-2-match-outcome-predictor-part-2-enhancing-the-dataset-and-adding-new-features-3522965de468
Thanks for reading, and let’s connect!
Top comments (3)
Great work on diving deeper into feature engineering and dataset enhancement! 🎮 It's inspiring to see how you tackled the complexity of Dota 2's data and handled challenges like imbalanced data. The techniques you shared could be valuable for anyone working with complex datasets. Looking forward to seeing where you take this next! 👏🚀 #MachineLearning #GamingAnalytics
Thank you !
This is my first article: medium.com/@masterhood13/building-...