Just participated in this month's Kaggle Tabular Playground challenge focused on Stellar Classification
The goal was to classify astronomical objects into:
A few interesting findings from the exploration:
Redshift turned out to be one of the strongest predictive signals.
Color indices such as u-g, g-r, and r-i were often more informative than raw photometric measurements.
A simple ensemble of LightGBM, XGBoost, and CatBoost performed surprisingly well.
Despite a moderately imbalanced dataset (~65% Galaxy, ~20% QSO, ~14% Star), explicit class weighting actually reduced validation performance in my experiments.
Hyperparameter tuning with Optuna did not outperform a carefully engineered baseline model, reinforcing the importance of feature engineering and domain understanding.
To document the entire process, I created a beginner-friendly guided notebook covering:
• Dataset understanding
• Exploratory analysis
• Feature engineering
• Categorical encoding
• Stratified K-Fold validation
• LightGBM, XGBoost, and CatBoost
• Model ensembling
• Experimentation with class imbalance and hyperparameter tuning
If you're learning machine learning, working on tabular data, or interested in astronomy-related datasets, feel free to take a look.
I'm also happy to discuss alternative approaches, feature engineering ideas, or leaderboard strategies. Constructive feedback and questions are always welcome.
Notebook Link: Click Here