An Efficient Machine Learning Approach: Analysis of Supervised Machine Learning Methods to Forecast the Diamond Price

Diamond, a found natural process compound of carbon, is one of the hardest and most immensely expensive material known to men, especially more to women. Investments in expensive gems like diamonds are in significant demand. The rate of a diamond, nevertheless, is not as easily calculated as the value of either gold or platinum since so many factors must be taken into account. Because there is such a broad range of diamond dimensions and qualities; as a result, being able to make reliable price predictions is crucial for the diamond industry. Although, making accurate predictions is challenging. In this study, we implemented multiple machine learning techniques employed to the challenge of diamond price forecasting’s such as Linear Regression, Random Forest, Decision Tree Random Forest, Cat-Boost Regressor and XGB Regressor. This article’s goal is to develop an accurate model for estimating diamond prices based on its characteristics such as weighting factor, cut grade, and dimensions. We compared the sum of estimated values and test values of predicted values with overestimated, underestimated and exact estimations. We applied cross-validation to calculate how much the model deviates from the actual when faced with a difference between the training set and the test set. We predicted values side by side. We performed a comparative analysis of supervised machine learning models with other models to evaluate the model accuracy and performance metrics. The Study’s experimental findings show that out of all the supervised machine learning models, Random Forest performs well with R2score and Low RMSE and MAE values and CV Score.