Analysis of Machine Learning Algorithm to predict Wine Quality.

Abstract

Product quality certification is used by industries to sell or advertise their products. The quality of wine is assessed by a human specialist, which is a time-consuming process that makes it quite expensive. Several machine learning techniques have already been applied to evaluate wine qualities such as quality and class on wine quality datasets. The quality of wine is determined not only by the amount of alcohol in it, but also by many traits, which change through time and therefore refine the wine's quality. It is critical to establish the wine's quality and categorise it into several categories based on a quality assessment. This study employs a variety of machine learning algorithms to predict wine quality. This research gives a comparison of fundamental and technical analysis based on many characteristics. This research compares and contrasts several prediction algorithms used to predict wine quality. Technical analysis such as time series analysis and machine learning algorithms like Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Ada Boost Classifier, and Gradient Boosting Classifier are examples of these methodologies. With the use of visualisation, several techniques are evaluated based on methodologies, datasets, and efficiency.