Effective Feature Selection Using Ensemble Techniques and Genetic Algorithm

Ghorpade-Aher, Jayshree; Sonkamble, Balwant A.

doi:10.1007/978-981-16-2380-6_32

Cited by 2 publications

(1 citation statement)

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“…While several studies have touched upon the domain of diamond price prediction, the majority have either focused on traditional statistical methods, rudimentary machine learning techniques. However, the true potential of advanced machine learning models [1], encompassing both regression and classification paradigms [2]- [5], remains largely unexplored in this context. The study is poised at this juncture, aiming to fill the gap in the literature.…”

Section: Introductionmentioning

confidence: 99%

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

Basha,

Oveis

2023

IJRITCC

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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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Section: Introductionmentioning

confidence: 99%