Harnessing Data-Driven Insights - Predictive Modeling for Diamond Price Forecasting using Regression and Classification Techniques

At a Glance

Metadata	Details
Publication Date	2023-10-27
Journal	International Journal on Recent and Innovation Trends in Computing and Communication
Authors	Md Shaik Amzad Basha, Peerzadah Mohammad Oveis
Institutions	GITAM University
Analysis	Full AI Review Included

Technical Documentation & Analysis: Predictive Modeling for Engineered Diamond Performance

This document analyzes the application of advanced machine learning (ML) techniques, as demonstrated in the research paper, and pivots the findings to highlight the precision, control, and customization capabilities offered by 6CCVD’s engineered MPCVD diamond materials. While the source paper focuses on gemological diamond price forecasting, the methodologies employed are directly relevant to predicting and guaranteeing the performance of technical SCD, PCD, and BDD materials.

Executive Summary

Validation of Predictive Modeling: The research successfully validated the use of sophisticated ML models (Random Forest, Gradient Boosting, SVC) for predicting diamond attributes based on intrinsic material characteristics.
Exceptional Regression Accuracy: The Random Forest Regressor achieved an R² value of 0.9749, demonstrating near-perfect correlation between material attributes (Carat, Cut, Color, Clarity) and final valuation.
High Classification Reliability: Classification models (Logistic Regression, SVC) achieved 95.32% accuracy in categorizing diamonds into predefined price tiers, confirming the strong influence of material quality on tier placement.
6CCVD Relevance (The Pivot): These high-precision ML methodologies are directly applicable to predicting and guaranteeing the performance metrics (e.g., thermal conductivity, optical transmission, electronic mobility) of 6CCVD’s engineered MPCVD diamond.
Material Attribute Control: The study reinforces that precise control over material attributes (analogous to 6CCVD’s SCD purity, PCD grain size, and BDD doping levels) is paramount for achieving predictable, high-value outcomes.
Conclusion: 6CCVD provides the necessary high-purity, custom-engineered diamond substrates required for applications demanding the predictable, high-performance characteristics modeled by these advanced algorithms.

Technical Specifications

The following data points summarize the performance metrics achieved by the predictive models analyzed in the research paper. These metrics establish a benchmark for the precision achievable when correlating material attributes with final performance/value.

Parameter	Value	Unit	Context
Best Regression R² Score	0.9749	N/A	Random Forest Regressor performance
Lowest Regression RMSE	631.66	Monetary Value	Random Forest Regressor performance
Highest Classification Accuracy	95.32	%	Logistic Regression & Support Vector Classifier
Dataset Size (Total Entries)	53,940	Entries	Kaggle Diamond Dataset
Training Data Size	43,152	Entries	80% of total dataset
Testing Data Size	10,788	Entries	20% of total dataset
Average Depth Percentage	61.75	%	Standard Deviation ± 1.43

Key Methodologies

The research employed a structured, multi-stage methodology combining rigorous data engineering with comparative model analysis. This systematic approach is critical for any high-precision engineering application utilizing MPCVD diamond.

Data Acquisition and Preprocessing:
- Sourced a reputable dataset (53,940 entries) detailing diamond attributes (carat, cut, color, clarity, dimensions, price).
- Rigorous data cleaning involved handling anomalies (e.g., zero dimensions) by replacing them with the median value of the respective column.
Feature Engineering and Scaling:
- Categorical features (Cut, Color, Clarity) were converted using one-hot encoding for ML compatibility.
- Numerical features (Carat, Depth, Table, X, Y, Z) were scaled using Standard Scaler to ensure uniformity and prevent magnitude sensitivity in linear models.
Experimental Design Bifurcation:
- Regression Analysis: Aimed at predicting the continuous, exact monetary price. Models tested included Linear Regression, Ridge, Lasso, Random Forest Regressor, and Gradient Boosting Regressor.
- Classification Analysis: Aimed at predicting categorical price tiers (Low, Medium, High). Models tested included Logistic Regression, Support Vector Classifier (SVC), Random Forest Classifier, and Gradient Boosting Classifier.
Model Training and Evaluation:
- The dataset was split 80:20 for training and testing.
- Regression models were evaluated using R² (variance explained) and Root Mean Square Error (RMSE).
- Classification models were evaluated using Accuracy, Precision, Recall, and F1-Score, detailed via Confusion Matrices.

6CCVD Solutions & Capabilities

The research demonstrates that predictable, high-value outcomes depend entirely on the precise control and measurement of intrinsic material attributes. 6CCVD specializes in providing engineered MPCVD diamond materials where these attributes are controlled to parts-per-billion purity levels, ensuring predictable performance far exceeding the variability of gemological grades.

Applicable Materials for Engineered Prediction

To replicate or extend this research into technical applications (e.g., predicting thermal performance or electronic device characteristics), 6CCVD recommends the following materials, which offer the necessary attribute control:

6CCVD Material	Key Attributes Controlled	Relevant Application (ML Prediction Target)
Optical Grade SCD	Nitrogen Purity (< 1 ppm), Surface Roughness (Ra < 1 nm), Thickness (0.1 µm - 500 µm)	Predicting optical transmission (UV to IR), Coherence Time (T₂) for quantum computing.
High Thermal Grade PCD	Grain Size, Thickness (up to 500 µm), Plate Size (up to 125 mm)	Predicting Thermal Conductivity (W/mK) for heat spreaders and high-power electronics.
Heavy Boron Doped BDD	Boron Doping Concentration (ppm), Resistivity (mΩ·cm), Surface Finish	Predicting electrochemical efficiency, electrode lifetime, and electronic device performance.

Customization Potential for Advanced Research

The ML models in the paper rely on precise input features (dimensions, clarity, etc.). 6CCVD provides the engineering control necessary to define these features precisely for technical applications:

Custom Dimensions: Unlike variable gem diamonds, 6CCVD provides SCD and PCD plates/wafers with custom dimensions up to 125 mm (PCD) and substrates up to 10 mm thick, ensuring consistent input geometry for predictive models.
Ultra-Low Surface Roughness: The “Cut” and “Clarity” factors in the paper are analogous to surface finish in technical diamond. 6CCVD guarantees polishing to Ra < 1 nm for SCD and Ra < 5 nm for inch-size PCD, minimizing performance variability.
Integrated Metalization: For electronic or sensor applications, 6CCVD offers in-house metalization services, including Au, Pt, Pd, Ti, W, and Cu layers, providing a critical, controlled feature for ML models predicting contact resistance or device integration success.

Engineering Support

The success of the Random Forest model (R² = 0.9749) highlights the value of data-driven decision-making. 6CCVD’s in-house PhD team specializes in the material science of MPCVD diamond and can assist researchers and engineers in defining the critical material attributes needed for similar Predictive Performance Modeling projects. We help translate desired application outcomes into precise material specifications (e.g., correlating SCD thickness and purity to predicted thermal resistance).

Call to Action: For custom specifications or material consultation, visit 6ccvd.com or contact our engineering team directly. We ship globally (DDU default, DDP available).

View Original Abstract

In the multi-faceted world of gemology, understanding diamond valuations plays a pivotal role for traders, customers, and researchers alike. This study delves deep into predicting diamond prices in terms of exact monetary values and broader price categories. The purpose was to harness advanced machine learning techniques to achieve precise estimations and categorisations, thereby assisting stakeholders in informed decision-making. The research methodology adopted comprised a rigorous data preprocessing phase, ensuring the data’s readiness for model training. A range of sophisticated machine learning models were employed, from traditional linear regression to more advanced ensemble methods like Random Forest and Gradient Boosting. The dataset was also transformed to facilitate classification into predefined price tiers, exploring the viability of models like Logistic Regression and Support Vector Machines in this context. The conceptual model encompasses a systematic flow, beginning with data acquisition, transitioning through preprocessing, regression, and classification analyses, and culminating in a comparative study of the performance metrics. This structured approach underscores the originality and value of our research, offering a holistic view of diamond price prediction from both regression and classification lenses. Findings from the analysis highlighted the superior performance of the Random Forest regressor in predicting exact prices with an R2 value of approximately 0.975. In contrast, for classification into price tiers, both Logistic Regression and Support Vector Machines emerged as frontrunners with an accuracy exceeding 95%. These results provide invaluable insights for stakeholders in the diamond industry, emphasising the potential of machine learning in refining valuation processes.

Tech Support

Original Source

DOI: https://doi.org/10.17762/ijritcc.v11i9.8355