Enhancing Predictive Modeling of Diamond Prices using Machine Learning and Meta-Ensemble Techniques

Accurate prediction of diamond prices is vital for decision-making in the diamond industry, benefiting all stakeholders. In this study, we employ a comprehensive dataset encompassing various diamond attributes, including cut, color, clarity, carat weight, and engineered features. We explore the predictive capabilities of traditional machine learning (ML) algorithms such as Linear Regression, Ridge Regression, Lasso Regression, Decision Tree Regression, Gradient Boosting Regression, Random Forest Regression, and AdaBoost Regression. Subsequently, we investigate the efficacy of ensemble techniques, including Bagging, Voting, and Stacking, aiming to enhance predictive accuracy and robustness. This research rigorously evaluates and compares these models' performance using established metrics, including Root Mean Squared Error(RMSE), R-squared, and Mean Absolute Error(MAE). Additionally, we suggest a novel strategy that uses ensemble model predictions as features to train a Meta-Model, increasing prediction accuracy even further. This study's results highlight how well machine learning and ensemble methods can predict diamond prices. We clarify the benefits and drawbacks of different modeling techniques through comparison analysis, offering insights into the best techniques for estimating diamond prices. This study gives stakeholders in the diamond business useful information and a data-driven method for predicting prices and making decisions.