Lab-grown gem-quality diamonds have shown fast market-share growth since the mid-2010s. Several attempts have been made to predict natural diamond prices based on their characteristics, namely, using Machine Learning techniques. There are no similar studies referring to lab-grown diamonds. In the present study, we aim at predicting lab-grown diamond unit prices. Data used were collected from https://www.1215diamonds.com and https://www.miadonna.com , on 2022, including 44 443 and 18 283 observations (lab-grown diamonds), respectively. These data include the diamond prices and also attributes to be used as predictors: Carat (measured in ct, 0.2 g), Color (7 levels, where the first is the colorless diamond); Clarity (8 levels where the first has no visible inclusions or internal flaws); Cut, including 10 different shapes, the predominant being the “Round”. For the task at hand – predicting lab-grown diamonds prices based on physical characteristics- we propose using Propositional Rules (RULES). K-Nearest Neighbors (KNN) will be used as a baseline. We resort to the R packages “FNN” (for KNN) and “Cubist” (for RULES). Metrics estimated on the test set (30% of the data) including R-Squared, MAE-Mean Absolute Error, and MAPE- Mean Absolute Percentage Error, enable evaluation of the predictive capacity of the proposed approach. We conclude that RULES generally produce better predictions than KNN, and also provide easy-tointerpret outputs and useful insights regarding specific observations. Future analysis may include additional predictors such as Cut quality and Certificate

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This work was supported by FCT,Grant UIDB/50021/2020.