Research First: Beating Kaggle's Titanic with History Books
Most Kaggle Titanic tutorials open with
import pandas as pdand 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.
Building an AI-Assisted Kaggle Toolkit
I got tired of the standard Kaggle workflow. Open a notebook, import pandas, start typing
.describe(), throw some models at the wall. It works, but you don’t learn much, and three months later you can’t remember why you chose XGBoost over Random Forest.So I built a toolkit that forces a better process: research first, then model. Document everything. Produce educational content as a natural byproduct of doing the work.