Evaluating the Predictive Proficiency of Machine Learning Algorithms: Progressive Developments in Diamond Price Forecasting

Ying Zhang

2024

Abstract

Distinguished for their global recognition as the most resilient mineral and enduring allure as coveted gemstones, diamonds have captivated human fascination for centuries. The popularity of diamonds extends beyond the intrinsic properties, encompassing optical brilliance and unparalleled hardness which is influenced by durability, tradition, fashion, and robust marketing strategies employed by industry producers. Despite inherent qualities, the demand for diamonds is intricately tied to perceived rarity and exclusivity. Forecasting diamond pricing presents a unique set of challenges primarily rooted in nonlinear relationships within crucial attributes like carat, cut, clarity, table, and depth. In response to the complexity, the research conducts a comprehensive comparative analysis, utilizing diverse supervised machine-learning models for precise prediction via classification and regression approaches. Meticulous evaluation of eXtreme Gradient Boosting, Random Forest, Multiple Linear Regression, k-Nearest Neighbors, and Decision Tree Regressor reveals that the eXtreme Gradient Boosting algorithm emerges as the most optimal choice, boasting an impressive R² score of 98.07% through rigorous evaluation. This research encompasses critical phases, including data preprocessing, exploratory data analysis, model training, accuracy assessment, and result interpretation. Not only sheds light on the intricacies of diamond pricing but also contributes valuable insights for leveraging advanced machine learning techniques in the realm of gemstone valuation and prediction.

Paper Citation

in Harvard Style

Zhang Y. (2024). Evaluating the Predictive Proficiency of Machine Learning Algorithms: Progressive Developments in Diamond Price Forecasting. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 448-452. DOI: 10.5220/0012818500004547

in Bibtex Style

@conference{icdse24,
author={Ying Zhang},
title={Evaluating the Predictive Proficiency of Machine Learning Algorithms: Progressive Developments in Diamond Price Forecasting},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={448-452},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012818500004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Evaluating the Predictive Proficiency of Machine Learning Algorithms: Progressive Developments in Diamond Price Forecasting
SN - 978-989-758-690-3
AU - Zhang Y.
PY - 2024
SP - 448
EP - 452
DO - 10.5220/0012818500004547
PB - SciTePress