Abstract:
Diamonds, known for their durability and high value, pose a unique challenge in determining their price due to multiple influencing factors. This research project aims to develop the most effective diamond price prediction system. Various machine learning algorithms, including 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, are trained on a diamond dataset to predict diamond prices based on different attributes. A comparative analysis of multiple machine learning regression models is performed to predict the price of any diamond. Through performance parameter values and analysis, the CatBoost Regression method demonstrates exceptional results, with an R2 score of 0.9872 and impressive training and testing accuracies of 98.74% and 98.72%, respectively. Thus, the CatBoost algorithm is chosen as the optimal algorithm for diamond price prediction, utilizing characteristic values collected from diamond certificate images.
This final year engineering project, based on an IEEE paper, offers a valuable commercial opportunity. We are pleased to provide the following components:
This project is available for purchase at a low and best price, making it an ideal choice for students looking to undertake a successful and impactful final year engineering project in computer science.