This repository contains the code behind my submission to Kaggle’s iconic Titanic: Machine Learning from Disaster competition. The goal is to predict who survived the sinking of the RMS Titanic, using only the features available at boarding time.
The solution leans on thorough feature engineering, paired with a LightGBM model, Bayesian hyperparameter tuning, and threshold optimization. Model interpretation is provided by a set of SHAP visualizations.
Alongside the full notebook, you’ll find a streamlined, well-commented Python script for reproducibility.
For the full story, including the modeling process, design decisions, and the human context behind the dataset, check out the accompanying writeup: : From Data to Deck — How I Hit Above 0.8 on the Titanic Challenge, and How You Can Too.
🛠️ Tools used:
- Python & pandas
- LightGBM
- KMeans (sklearn)
- Bayesian optimization (skopt)
- SHAP
- Stubbornnes