Diamond pricing is notoriously opaque — the same stone can be quoted at wildly different prices depending on who's selling it, because pricing leans on subjective grading and dealer intuition rather than a consistent, data-driven model. I wanted to see whether the same regression techniques used in asset valuation and financial risk-scoring could bring transparency to a market that badly needed it, while also giving myself a complete, deployable ML project — not just a notebook. The app takes a diamond's physical and quality attributes — carat, cut, color, clarity, depth, table, and dimensions (length/width/height) — and returns a predicted price in real time through a simple web interface. Instead of relying on subjective grading judgment, the underlying regression model, trained on historical diamond sales data, estimates fair market value directly from measurable attributes.
Diamond Price Predictor: ML Valuation Engine
An end-to-end ML pipeline that predicts diamond prices from cut, clarity, and carat — tracked with MLflow/DVC and deployed as a real-time Flask app.
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