Machine Learning -Project Data Analysis: Linear, Multiple, and Logistic Regression Diamond Price Prediction Diamond Price Category Prediction
Session
Management Business and Economics
Description
The research presented here focuses into the use of machine learning techniques to predict diamond price and diamond price category prediction, based on key properties like: carat, cut, color, clarity, and physical dimensions. The study uses a structured dataset with both numerical and categorical characteristics to train and assess regression and classification algorithms. The project's goal is to create an accurate forecasting system that will benefit diamond market stakeholders through data preprocessing, visualization, and model comparison. The expected outcomes include obtaining a predicted accuracy of at least 80%, identifying the most influential features influencing diamond pricing, and determining the best-performing model using performance indicators. By addressing the complexity of diamond valuation, this project helps traders and consumers make better data-driven decisions by providing a transparent, reproducible methodology backed up with Python implementations and visual analytics. The findings not only improve market understanding, but also highlight machine learning's potential for tackling real-world pricing challenges in the luxury goods sector.
Keywords:
international financial markets, exchange rate, financial crises, political economy
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-41-2
Location
UBT Lipjan, Kosovo
Start Date
25-10-2025 9:00 AM
End Date
26-10-2025 6:00 PM
DOI
10.33107/ubt-ic.2025.389
Recommended Citation
Uka, Ardian and Shala, Teuta, "Machine Learning -Project Data Analysis: Linear, Multiple, and Logistic Regression Diamond Price Prediction Diamond Price Category Prediction" (2025). UBT International Conference. 4.
https://knowledgecenter.ubt-uni.net/conference/2025UBTIC/MBE/4
Machine Learning -Project Data Analysis: Linear, Multiple, and Logistic Regression Diamond Price Prediction Diamond Price Category Prediction
UBT Lipjan, Kosovo
The research presented here focuses into the use of machine learning techniques to predict diamond price and diamond price category prediction, based on key properties like: carat, cut, color, clarity, and physical dimensions. The study uses a structured dataset with both numerical and categorical characteristics to train and assess regression and classification algorithms. The project's goal is to create an accurate forecasting system that will benefit diamond market stakeholders through data preprocessing, visualization, and model comparison. The expected outcomes include obtaining a predicted accuracy of at least 80%, identifying the most influential features influencing diamond pricing, and determining the best-performing model using performance indicators. By addressing the complexity of diamond valuation, this project helps traders and consumers make better data-driven decisions by providing a transparent, reproducible methodology backed up with Python implementations and visual analytics. The findings not only improve market understanding, but also highlight machine learning's potential for tackling real-world pricing challenges in the luxury goods sector.