Feature-Engineering

Most Kaggle Titanic tutorials open with import pandas as pd and immediately start running .describe(). I did something different: I read about the actual disaster first.

The result was better feature engineering, more informed imputation strategies, and a clearer picture of what was noise versus signal. The model barely mattered. The features did all the work.

Why Research Before Code

The Titanic dataset has 891 rows and 11 features. You could get ~77% accuracy by predicting “all women survive, all men die.” Getting past 77% requires understanding why people survived beyond gender. That understanding comes from the history, not the data.