Evaluating Sparkle and Beyond the Four Cs: Unveiling a New Frontier in Diamond Valuation with Advanced Machine Learning Algorithms

The gemological and high-end retail sectors evaluate diamonds using the “Four Cs”: carat weight, color, clarity, and cut. This study evaluates machine learning diamond valuation accuracy using these parameters. Machine learning might revolutionize diamond assessment by improving accuracy and objectivity. This research compares machine learning diamond price prediction techniques. This study attempts to improve gemological data-driven valuation by creating models that best represent a diamond's features and market value. This study uses a vast diamond price dataset. Following data cleaning, normalization, and encoding, we trained and assessed numerous models like Linear, Ridge, Lasso, ElasticNet, Polynomial Regression, Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, XGBoost Regressor Cat Boost Regressor, K-Nearest Neighbors Regressor, Bayesian Ridge Regressor, SGD Regressor. We used MSE, RMSE, and MSE to evaluate the models' efficacy. Statistical investigations supported our findings. Prediction ensemble and boosting algorithms Cat Boost, XGBoost, Gradient Boosting, and Polynomial Regression performed best. The high R2 values of these models indicate the highest agreement with observed diamond prices. Carat weight was the best predictor, according to industry experts, and feature significance analysis verified this. Combining traditional expertise with machine learning analytics might change diamond assessment, according to the study. Superior models may be used in valuation procedures to improve efficiency and accuracy. This research thoroughly evaluates multiple machine learning diamond price prediction approaches, adding something unique to gemology. It provides the framework for future innovation by showing how machine learning might change valuation methods. Since the study covers gemology and predictive analytics, the findings are groundbreaking.