Prediction and Feature Importance Analysis for Diamond Price Based on Machine Learning Models

Hairong Zhang

doi:10.54254/2754-1169/46/20230347

Research Article

Open access

Published on 1 December 2023

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Zhang,H. (2023). Prediction and Feature Importance Analysis for Diamond Price Based on Machine Learning Models. Advances in Economics, Management and Political Sciences,46,254-259.

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Prediction and Feature Importance Analysis for Diamond Price Based on Machine Learning Models

Hairong Zhang *^,1,

¹ South China Normal University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/46/20230347

Abstract

The advent of Artificial Intelligence (AI) has facilitated the prediction of diamond prices through data analysis techniques. By incorporating relevant data, various models were constructed to examine the interrelationships between different factors and subsequently forecast diamond prices, which were then subjected to rigorous verification. The findings revealed that the XGBoost model demonstrated superior performance, exhibiting a high coefficient of determination (R square) and a low Root Mean Squared Error (RMSE). Furthermore, employing the feature importance method elucidated the significance of specific factors in determining diamond prices. Notably, carat weight emerged as the most influential factor, followed by width, clarity, and color. Conversely, other factors exhibited a lesser impact on price determination. These findings provide valuable insights for stakeholders in the diamond industry, enabling them to prioritize the most influential factors when assessing and forecasting diamond prices. Future research endeavors could explore additional data sources and advanced AI techniques to further enhance the accuracy and comprehensiveness of diamond price predictions.

Keywords

diamond price prediction, machine learning, feature importance

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Cite this article

Zhang,H. (2023). Prediction and Feature Importance Analysis for Diamond Price Based on Machine Learning Models. Advances in Economics, Management and Political Sciences,46,254-259.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Conference website: https://www.icftba.org/

ISBN：978-1-83558-139-1(Print) / 978-1-83558-140-7(Online)

Conference date: 8 November 2023

Editor：Javier Cifuentes-Faura

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.46

ISSN：2754-1169(Print) / 2754-1177(Online)

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