There are many prediction systems available for problems like stock exchange, medical diagnosis, insurance calculation, etc. Wine Quality is one area where there is a big opportunity to recommend a good quality of wine to users based on their preferences as well as in historical data. This paper describes the work to learn and assess whether a given wine sample is of good quality or not. The use of machine learning techniques specifically the linear regression with stochastic gradient descent were explored, and the features that perform well on this classification were engineered. The main aim is to develop a cost-effective system to acquire knowledge using data analysis through machine learning algorithms to predict the quality of wine in a better way.

There are many prediction systems available for problems like stock exchange, medical diagnosis, insurance calculation, etc. Wine Quality is one area where there is a big opportunity to recommend a good quality of wine to users based on their preferences as well as in historical data. This paper describes the work to learn and assess whether a given wine sample is of good quality or not. The use of machine learning techniques specifically the linear regression with stochastic gradient descent were explored, and the features that perform well on this classification were engineered. The main aim is to develop a cost-effective system to acquire knowledge using data analysis through machine learning algorithms to predict the quality of wine in a better way.