Data Scientist, Enke Systems
Topic: Predicting winner of a horse race
We’ll discuss the next points:
1. Basic information about horse racing;
2. Betting strategy. Kelly criterion;
3. ‘Raw features’. Feature engineering from raw features;
4. Features and target variable. Problem statement;
5. Mathematical predictive model. Error function;
6. Methods of solving. Linear log regression, XGBoost, CatBoost;
7. Results (accuracy);
8. Ways to improve (Boosting, FM).
About: Data Scientist & Machine Learning engineer, PhD applied mathematics MIPT. Data scientist in EnkeSystem (video classification), Former Data scientist in ThoroughtBet (Horse race prediction) worked with supervise & unsupervise models, feature engineering, classification , regression and ranking tasks.