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Diamond price prediction

Abstract:
One of the most durable and expensive materials made naturally from carbon is diamond. However, unlike gold and silver, figuring out the price of a diamond is extremely difficult because several factors must be taken into account. The goal of this research is to develop the diamond price prediction system that is the most effective. The algorithms used to train the specific machine learning models on the diamond dataset for the prediction of diamond prices based on various attributes include Linear regression, Support Vector regression, Decision trees, Random Forest regression, K-Neighbors regression, CatBoost regression, Huber regression, Extra tree regression, Passive Aggressive regression, Bayesian Regression, and XGBoost Regression. For the purpose of predicting the price of any diamond, a comparison of multiple Machine Learning Regression models is conducted. With an R2 score of 0.9872 and impressive training and testing accuracies of 98.74% and 98.72%, respectively, the CatBoost Regression method was determined to be the best optimal algorithm from the performance parameter values and analysis. Therefore, using the values of characteristics collected from an image of a diamond certificate, the CatBoost algorithm has been developed for the price prediction of a diamond specimen
final year project is based on IEEE Paper.this will be one of the best Final year engineering project for computer science. Components that we will provide are. 1.complete documentation support
2.complete working hardware/software implemented in students environment
3.classes will held accordingly.

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