Comparative-analysis-of-machine-learning-algorithms-for-wine-quality-prediction

Most companies have been marketing their products in recent years, based on the quality certification they received for the products. The conventional method of measuring the quality of the product is time-consuming, but the methods have become more efficient and consumed less time than before with the advent of machine learning techniques. We have explored some of the machine learning techniques in this paper to determine the quality of the wine based on wine characteristics that depend on the data for this research work. To verify the classification results, we used various feature selection techniques. Different performance measure like accuracy, sensitivity, specificity, positive predictive value, negative predictive value was used for the comparison between various machine learning techniques. Nonlinear, linear and probabilistic classifiers have been used in this paper for analyzing that selecting best features from the data are capable of providing better result than taking into account all features for the classification.