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Pythonscikit-learnRandom ForestPandas
Kaggle — Titanic Survival Prediction
Compared logistic regression, decision trees, gradient boosting, XGBoost, and random forest on the classic Titanic dataset. Random Forest chosen for best bias-variance tradeoff — ~82% CV accuracy.
Year: 2025Category: Python
Project Overview
Work on the Kaggle Titanic competition with shared feature engineering and stratified 5-fold CV. Logistic regression, single trees, gradient boosting, and XGBoost were explored; Random Forest was kept as the main model for strong accuracy, stable folds, and clear feature importances. This portfolio entry links to the repo with the Random Forest pipeline; the README records the full model comparison and preprocessing.