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Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model.

Samuel Njoroge Kigo1, Evans Otieno Omondi2,3, Bernard Oguna Omolo1,4,5

  • 1Institute of Mathematical Sciences, Strathmore University, P.O Box 59857-00200, Nairobi, Kenya.

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The Random Forest (RF) model accurately predicts diamond prices, outperforming other machine learning algorithms. This study highlights RF

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Area of Science:

  • Machine Learning Applications
  • Data Science
  • Econometrics

Background:

  • Diamond pricing is complex due to non-linear relationships between features like carat, cut, clarity, table, and depth.
  • Accurate price prediction is crucial for the diamond industry, impacting pricing strategies and market analysis.
  • Supervised machine learning offers potential solutions for modeling these complex relationships.

Purpose of the Study:

  • To comprehensively analyze and compare multiple supervised machine learning models (regressors and classifiers) for accurate diamond price prediction.
  • To identify the most effective model for predicting diamond prices, considering both regression and classification approaches.
  • To provide insights into the importance of data preprocessing techniques for improving predictive model performance.

Main Methods:

  • Data preprocessing included outlier handling (inter-quartile range), standardization, median imputation for missing values, and multicollinearity resolution.
  • Feature engineering involved equal-width binning for the 'cut' variable and correlation-based feature selection.
  • Evaluated models included Random Forest (RF), Multi-Layer Perceptron (MLP), XGBoost, Boosted Decision Tree, K-Nearest Neighbors (KNN), Linear Regression, and Support Vector Regression.

Main Results:

  • The Random Forest (RF) regressor achieved the lowest Root Mean Squared Error (RMSE) of 523.50 and the highest R-squared score of 0.985.
  • The RF classifier demonstrated perfect performance with an Area Under the Curve (AUC) of 1.00.
  • Other models like MLP, XGBoost, and Boosted Decision Tree showed varying degrees of effectiveness, while KNN, Linear Regression, and Support Vector Regression performed less accurately.

Conclusions:

  • The Random Forest (RF) model is the superior choice for accurately predicting diamond prices, exhibiting exceptional performance in both regression and classification tasks.
  • The study underscores the significance of robust data preprocessing techniques in enhancing the accuracy of machine learning models for price prediction.
  • The findings offer valuable tools and insights for the diamond industry, aiding in pricing strategies, market trend analysis, and informed decision-making.