Lessons from a Kaggle Playground Competition

Question

Just participated in this month's Kaggle Tabular Playground challenge focused on Stellar Classification

The goal was to classify astronomical objects into:

• STAR
• GALAXY
• QSO (Quasar)

A few interesting findings from the exploration:

1. Redshift turned out to be one of the strongest predictive signals.

2. Color indices such as u-g, g-r, and r-i were often more informative than raw photometric measurements.

3. A simple ensemble of LightGBM, XGBoost, and CatBoost performed surprisingly well.

4. Despite a moderately imbalanced dataset (~65% Galaxy, ~20% QSO, ~14% Star), explicit class weighting actually reduced validation performance in my experiments.

5. 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