Diamond Price Prediction using Machine Learning Techniques

Diamonds, composed of carbon formed through natural processes, stand as one of the most coveted and valuable materials globally, particularly among women. The intricate nature of diamond pricing, impacted by diverse factors including weight, cut grade, and dimensions, poses a challenge for accurate valuation. When choosing a technique for a research study, It is important to take into the nature of the data, including its type, distribution, and any missing values or outliers. The technique should align with the study’s objectives, balancing model complexity with the need for interpretability, especially if the results must be understood by stakeholders. In this study, multiple machine learning techniques, including Linear Regression, Random Forest, Decision Tree, KNeighbors, XGB Regressor, Extra tree regressor, Gradient boosting regressor, and multi-layer perceptron were employed to forecast diamond prices. Through a comparative analysis of supervised machine learning models, focusing on accuracy and performance metrics such as R2 score, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), insights were gained into the efficacy of each model. Experimental findings revealed Extra tree as the most proficient model, exhibiting high R2 scores and low RMSE and MAE values, indicative of its superior predictive accuracy. This research contributes to the ongoing efforts to optimize diamond price projection, offering valuable insights for the diamond industry and beyond.