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

Executive Summary

This research focuses on developing and comparing advanced machine learning (ML) models for predicting diamond prices, addressing both continuous valuation (regression) and categorical price tier assignment (classification).

Core Value Proposition: Establishment of a robust, data-driven framework for diamond valuation, significantly improving precision and stability compared to traditional expert assessments.
Regression Performance: The Random Forest Regressor demonstrated superior performance in predicting exact monetary values, achieving an R² value of 0.9749.
Classification Performance: Both Logistic Regression and the Support Vector Classifier (SVC) excelled in categorizing diamonds into predefined price tiers (Low, Medium, High), achieving an accuracy of 95.32%.
Methodology: A comprehensive comparative analysis was conducted, juxtaposing ensemble methods (Random Forest, Gradient Boosting) against linear models (Ridge, Lasso, Logistic Regression, SVC).
Data Preparation: The study utilized a rigorous preprocessing pipeline, including one-hot encoding of categorical features (cut, color, clarity) and median imputation for dimensional anomalies (x, y, z).
Engineering Insight: The high R² value confirms that intrinsic attributes (carat, cut, clarity, color) are highly predictive of market price, validating the use of ensemble ML models for complex, non-linear valuation tasks.

Technical Specifications

The following table summarizes the key performance metrics and parameters derived from the comparative analysis of the predictive models.

Parameter	Value	Unit	Context
Dataset Size (N)	53,940	Entries	Diamond transaction records (Kaggle source)
Feature Count	11	Columns	Carat, cut, color, clarity, depth, table, dimensions (x, y, z), price
Data Split Ratio	80:20	% (Train:Test)	Standard split for model training and evaluation
Best Regression Model	Random Forest Regressor	N/A	Highest predictive accuracy
Regression R² (Max)	0.9749	N/A	Variance in price explained by Random Forest
Regression RMSE (Min)	631.66	Monetary	Root Mean Square Error for Random Forest
Best Classification Models	Logistic Regression, SVC	N/A	Categorizing price tiers
Classification Accuracy (Max)	95.32	%	Performance of Logistic Regression and SVC
Price Tier Distribution	~33.00	% per tier	Low, Medium, and High categories
Worst Regression R²	0.9188	N/A	Lasso Regression performance

Key Methodologies

The predictive modeling process followed a structured, systematic flow, ensuring data integrity and model robustness across both regression and classification paradigms.

Data Acquisition: Raw diamond dataset (53,940 entries) sourced from a reputable Kaggle database, including attributes like carat, cut, color, clarity, and physical dimensions (x, y, z).
Anomaly Handling: Anomalous entries in the dimensional columns (y and z) were identified (e.g., values of 58.9 and 31.8 mm) and corrected using median imputation to maintain data integrity and prevent outlier distortion.
Feature Engineering (Categorical): Categorical variables (cut, color, clarity) were transformed into a machine-readable format using one-hot encoding, creating binary columns for each category level.
Feature Scaling (Numerical): Numerical features (carat, depth, table, x, y, z) were scaled using the Standard Scaler. The target variable (‘price’) was explicitly excluded from scaling.
Data Segmentation: The processed dataset was partitioned into training (80%) and testing (20%) subsets, maintaining a consistent random seed for reproducibility.
Regression Model Training: Four models were trained to predict exact price: Linear Regression (Ridge L2, Lasso L1), Random Forest Regressor (ensemble), and Gradient Boosting Regressor (boosting).
Classification Target Transformation: The continuous ‘price’ variable was binned into three approximately equal categories (Low, Medium, High) to facilitate classification analysis.
Classification Model Training: Four models were trained to predict price tier: Logistic Regression, Support Vector Classifier (SVC), Random Forest Classifier, and Gradient Boosting Classifier.
Performance Evaluation: Regression models were evaluated using R² and RMSE. Classification models were evaluated using accuracy, precision, recall, and F1-score, detailed via confusion matrices.

Commercial Applications

The high-accuracy predictive models developed in this study offer significant commercial utility across several sectors of the diamond and luxury goods industries.

Automated Valuation Systems (AVS): Implementation of the Random Forest Regressor (R² = 0.9749) into trading platforms for real-time, objective, and precise diamond pricing, reducing reliance on manual appraisal.
Inventory Management and Trading: Use of the classification models (Logistic Regression/SVC) to quickly stratify large inventories into standardized price tiers, optimizing wholesale and retail stocking decisions.
Financial Risk Assessment: Utilizing the predictive framework for collateral valuation in diamond-backed financing, providing banks and insurers with reliable, data-driven estimates of asset worth.
Consumer Protection Tools: Deployment of the classification model to provide consumers with an immediate, objective price range check (Low, Medium, High) for any diamond based on its GIA specifications, enhancing market transparency.
Quality Control and Anomaly Detection: The models serve as a benchmark to flag diamonds whose actual price deviates significantly from the predicted price, indicating potential misgrading or market anomalies.

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.

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Original Source

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