Predictive modeling and benchmarking for diamond price estimation: integrating classification, regression, hyperparameter tuning and execution time analysis

The objective of this research is to provide a comprehensive analysis of diamond price prediction by evaluating a wide array of 23 machine learning models (ML), including both regression and classification techniques. This study aims to fill a gap in existing literature by applying hyperparameter tuning optimization across various models to enhance prediction accuracy, estimated values and Time execution efficiency, setting a new benchmark in the field. This approach involved a systematic assessment of multiple ML models on their base and tuned performance concerning accuracy, execution time, and predictive value alignment (under, accurate, over). The study utilized advanced hyperparameter tuning techniques to optimize each model’s performance, offering a comparative analysis that highlights the effectiveness of different models in predicting diamond prices. This research makes a distinct contribution through its extensive benchmarking of numerous ML models in the context of diamond price prediction, which is unprecedented in the literature. By applying hyperparameter tuning extensively to enhance model performance, its originality is derived from its comprehensive application of hyperparameter tuning to improve model performance by essentially tuning the model, this paper provides a novel contribution to the growing area of predictive analytics. By benchmarking an unprecedented amount of ML models for diamond price prediction and employing hyperparameter tuning, this paper moves the state of the art by noting the remarkable scope for accuracy improvements in tailored ML applications and demonstrates the extreme importance of model selection and optimization. The findings encompass that CatBoost Regressor, XGBoost Regressor still, kept high accuracy scores after tuning process and Random Forest Regressor accelerated much after tuning. Lastly, CatBoost Classifier, LightGBM Classifier existent achieving accuracies and efficiencies on the problem of diamond price classification tasks. Given its holistic nature, this study acknowledges the potential of overfitting in highly tuned models and their reliance on the specific dataset used for training. Future research might explore the generalisability of these techniques to other datasets and further investigate the trade-offs between model complexity and interpretability. The practical implications of this research are significant for stakeholders in the diamond industry such as retailers, appraisers, and investors. By identifying the most effective models for price prediction, we offer actionable insights that can improve decision-making processes, optimize inventory management, and enhance pricing policies.