paulstreli / wine-quality-prediction Goto Github PK
View Code? Open in Web Editor NEWAs part of my third-year module Machine Learning I wrote a four-page paper with the name "Analysis of Regression Predictors for the Wine Quality Dataset". The paper presented my results on finding a suitable hypothesis that is able to predict a quality score (between 0 and 10) based on eleven physicochemical attributes of a specific wine. For this regression problem I implemented several machine learning techniques and algorithms using Matlab and experimented with constant base predictors, support vector machine regression, neural networks, bag of trees and linear regression with and without feature transform and regularisation. For the total dataset of 6497 samples provided by the UCI Machine Learning Repository bag of trees seems to yield the best performance.