An end-to-end machine learning project for predicting wine quality

In this project, the usecase I picked is develop a supervised learning model which is useful to support the oenologist wine tasting evaluations and improve wine production. The goal is to model and predict wine quality based on physicochemical test observations. Results can be used to improve winemaking by identifying the most influential factors and to stratify wines such as premium brands. The performance will be measured by the accuency(precision,recall,etc.) and MSE

The data is Wine Quality Data Set I found from UCI Machine Learning Repository. The dataset I choose is White vinho verde wine samples, from the north of Portugal. Which has 4898 records and 12 columns

NN(Neural network) model achieved the higest performance based on cross-validation