Hardev Ranglani, EXL Service Inc, USA
Accurate pricing and authentication of diamonds are essential for ensuring market transparency and consumer confidence. This study applies machine learning (ML) techniques to predict diamond prices and classify diamonds as lab-grown or natural based on attributes such as cut, color, clarity, and carat weight, etc. A unified framework combining regression for price prediction and classification for origin determination is developed with robust model evaluation. The proposed models achieve a mean absolute error of $ 554.32 in price prediction and an F-1 score of 98.66 % in origin classification. The study also explores the varying linear relationship between the variables in predicting diamond price using local linear regression. These findings provide valuable insights for the gemstone industry, offering a practical and interpretable approach to automated diamond valuation and authentication.
Diamond Price Prediction, Regression and Classification Modeling, Local Linear Regression, Lab-Grown vs Natural Diamonds, R2.
