Utilizing Machine Learning Techniques for Excellent Coffee Prediction

Melewwe Thantrige, Methmi Kaveesha (2023) Utilizing Machine Learning Techniques for Excellent Coffee Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

As universal coffee utilization rises, the coffee industry remains highly valued on sustaining and rising coffee quality. To this is added the requirement to understand the essential components affecting the quality of coffee accurately. By bridging the gap between traditional cupping techniques for coffee quality detection, this study utilizes modern machine learning approaches to explore the sensory as well as non-sensory attributes shaping coffee quality. Three machine learning models namely, Random Forest, Gradient Boosting Machine, and Support Vector Regressor, along with a Hybrid Model which combines all three algorithms, are used for predictive analysis and the models are evaluated using evaluation metrics namely, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R Squared). The outcomes deliver valuable perceptions into the features critical for coffee quality evaluation. Results express that among all four predictive models, Support Vector Regression performance is best in forecasting coffee quality by settling good generalizations on unseen or trained data.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Basilio, Jorge UNSPECIFIED
Uncontrolled Keywords:	Machine Learning Models; Quality Prediction; Non-sensory Attributes; Support Vector Regression; Gradient Boosting Machine; Random Forest; Hybrid Model
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Food Industry > Beverage industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	18 May 2025 13:38
Last Modified:	18 May 2025 13:38
URI:	https://norma.ncirl.ie/id/eprint/7570

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