Wine Quality Prediction Using Machine Learning

Nowadays, people desire to live opulently. They have a propensity to show off and use things frequently. The usage of red wine has become widespread in modern societies. The industry uses product quality certification to sell its items as well. The use of techniques like Random Forest, Decision Tree Classifier and Logistic Regression is done using datasets that have been obtained from a source. Based on the outcomes of the training set, computes several performance measures between training and testing sets, compares them, and predicts which of the two strategies will perform the best. The dataset for red wine is used in all tests. This study demonstrates that using a subset of data as opposed to all features can lead to more precise prediction. This project aims to develop a machine learning model for predicting the wine quality based on its chemical characteristics. The dataset used in this project consists of attributes such as fixed acidity, density, residual sugar, citric acid, total sulphur dioxide, pH, sulphates, free sulphur dioxide, volatile acidity, alcohol, and the quality rating of the wine. Data pre-processing, data analysis, model selection, feature selection, and evaluation are all aspects of the project. The implementation and comparison of several machine learning methods, including logistic regression, decision trees, and random forests, will be done in terms of performance metrics like mean squared error, accuracy, and precision. The project's findings will help improve the wine-making process and shed light on the most crucial elements that affect wine quality. Winemakers can use the algorithm to forecast wine quality and adjust their production methods accordingly [1]