2024
Titanic, feature engineering and Random Forest
Survival classification with feature engineering (titles, family, deck, ticket frequency) and a GridSearch-tuned Random Forest.
Problem
Predict which passengers survived from their travel data.
Data
Kaggle's Titanic training set: 891 passengers with class, sex, age, family, ticket, fare, cabin and port. Included as `data/titanic.csv`.
Method
Group-wise imputation, title and surname extraction, family and deck features, mutual information, and a 216-combination, 3-fold GridSearch.
Result
Without data leakage, the Random Forest scores 81.6% on the hold-out (84.8% cross-validated), 3.9 points above the "women survive" rule. The original 98.9% came from computing family and ticket survival rates on the whole dataset.
Stack
- Python
- scikit-learn
- pandas
- Seaborn
Context
Course: Machine Learning I · 2024