Titanic — Machine Learning from Disaster

This post summarizes and styles concepts from the Kaggle notebook: Titanic — Machine Learning from Disaster.

Problem overview

Predict passenger survival using tabular features such as age, sex, class, and fare.

Workflow

1. Audit and impute missing values
2. Encode categorical variables
3. Scale numerics where needed
4. Train baseline models (LogReg, RandomForest)
5. Evaluate with accuracy/F1 and confusion matrix

Reference

Kaggle notebook: Titanic — Machine Learning from Disaster