Comparative Analysis of Supervised Models for Diamond Price Prediction

Diamond is one of the expensive gemstones on the planet that occurs naturally in the form of minerals made of carbons. Precious stones like diamonds are always high in demand due to their monetary rewards. The price of such stones varies according to their features. Given this, we carried out a comparative analysis and implementation of various supervised models in predicting the price of the diamond. In our work, we evaluated eight different supervised models like linear regression, lasso regression, ridge regression, decision tree, random forest, ElasticNet, AdaBoost Regressor, and Gradient-Boosting Regressor and showcases the best suitable model with the more accurate result of all. This paper aims from data preprocessing, finding a correlation between the dataset attributes, training the above-given models, testing their accuracy, analyzing their outcomes, and in turn finding the best of them is the Random Forest Regression Model.