Problem type: Multiclass Classification

Includes solution and jury presentation of BTK Akademi Datathon 2023. I attended the competition solo and ranked in top 10 by the jury's selection out of 359 competitors and 255 teams.

• A very detailed EDA phase followed by multiple pivot tables
• Feature engineering; extracting new numerical features, trying the experimental "Cluster feature" method and getting statistical features by cluster groups
• Feature selection with Sequential Feature Selection, RFECV, SHAP (not included in this repo)
• Model selection / model re-evaluation
• Detection and analysis of the sample that is being misclassified by each of the Random Forest, XGBoost, CatBoost, LightGBM models
• Hyperparameter tuning with Optuna
• Creating the final submission with decided final feature set and model architecture

• I also included every helper function that I use throughout different sections of the solution

• About the experimental "cluster feature" method: 1, 2
• SHAP implementation for multiclass classification

https://www.kaggle.com/so24def