33RanClassification

Travel insurance purchase

I explored who bought travel insurance by age, employment type and income, then trained a decision tree on seven columns and scored 81.4% on a 20% test split.

Original tutorial by Aman Kharwal: Insurance Prediction (opens in a new tab)

What it printed in 2022

Numbers read from the saved notebook outputs. Cell numbers count every cell in the notebook, Markdown included, starting at 1.
Output	Value	Cell #
Test accuracy (decision tree)	81.4%	16

Cell # counts every cell from the top of the notebook, Markdown included, starting at 1.

I never printed the share of people who did not buy, so 81.4% has nothing to be compared with.

The data

A 1,987-row travel insurance table from the tutorial author's data repository.

What I'd do differently

Print the majority-class rate first, then precision and recall for buyers.
Include employment type, which the charts showed mattered, and limit the tree depth.