It’s a known fact that older the wine, better the taste. However, there are several factors other than age that go into wine quality certification which include physcicochemical tests like alcohol quantity, fixed acidity, volatile acidity, determination of density, pH and more. The main goal of this project is to build a machine learning model to predict the quality of wines by exploring their various chemical properties. Wine quality dataset consists of 4898 observations with 11 independent and 1 dependent variable.

Data Set Information:

The dataset was downloaded from the UCI Machine Learning Repository.

These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.