Scenario
“As a Marketing Manager at a diamond retail company, you’ve just been handed a dataset of 53,940 diamonds and a single question from your CEO: ‘Can we stop guessing what to charge?’ This chapter walks you through how you’d answer that — step by step, using real data and machine learning.”
The Business Problem
You manage pricing strategy for a diamond retailer. Every day your team faces questions like:
- A customer brought in a 1.2-carat Ideal-cut, E color, VS1 clarity diamond — what should we offer?
- Are we underpricing our Premium-cut stones compared to the market?
- Which features actually drive price the most — the carat weight, the cut, or the clarity?
Right now, your team answers these with gut feeling, spreadsheets, and years of experience. That works — until you’re dealing with 54,000 diamonds. Machine learning lets you turn data into a pricing engine that can answer these questions instantly, consistently, and at scale.
The dataset we’ll use is the classic seaborn diamonds dataset: 53,940 round-cut diamonds with 10 attributes including carat, cut, color, clarity, and price.
Diamond Database
Prices of over 50K round cut diamonds database are available here.