Evaluating the Predictive Proficiency of Machine Learning
Algorithms: Progressive Developments in Diamond Price Forecasting
Ying Zhang
University of Leeds, Leeds, LS27FD, U.K.
Keywords: Python, Price, Prediction, Diamond.
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.
1 INTRODUCTION
The Gemological Institute of America (GIA)
introduced Cut, Carat, Color, and Clarity. They were
providing a standardized framework for assessing and
grading diamonds based on their distinct attributes in
the 1940s.
The burgeoning global appetite for diamonds has
precipitated an imperative for pricing paradigms
characterized by both accuracy and transparency.
Conventional methodologies, tethered to venerable
compendia like the Rapaport Price List, grapple with
the intricate challenge of assimilating and mirroring
the multifarious dynamism inherent in the diamond
market. The idiosyncratic attributes of diamonds
manifest in diverse morphologies, dimensions, and
gradations of clarity, which introduce a compounding
layer of complexity in discerning their intrinsic
market value.
The realm of diamond price prognostication,
delving into the realm of machine learning,
orchestrates a symphony of analytical prowess. This
entails the meticulous training of models, leveraging
historical datasets and meticulously considering
variables such as carat weight, cut quality, color
gamut, and clarity. These trained models, having
imbibed historical intricacies, extrapolate
overarching patterns to venture predictions into
uncharted territories of new diamond valuations. A
methodological bastion grounded in data-driven
acuity, this approach finds an organic alignment with
the evolving contours of the gemstone market,
cherishing the imperatives of transparency,
efficiency, and razor-sharp precision.
In summation, the rubric of diamond price
prognostication not only dovetails with age-old
valuation paradigms but also interfaces seamlessly
with the kaleidoscopic shifts characterizing the
contemporary market milieu. In catering to the
discerning exigencies of a modern consumer cohort,
this predictive discipline emerges as a linchpin,
bestowing sagacious insights unto stakeholders,
investors, and consumers alike. This predictive
accuracy serves as a potent instrument, galvanizing
investors with informed decision-making
capabilities, charting the course for sagacious