Building classification models to predict quality of wines. (Accuracy = 71.33%)

Our aim was to determine variation in white and red wine using the variables alcohol, fixed acidity, volatile acidity, residual sugar, critic acid, chlorides, free sulphur dioxide, total sulphur dioxide, density, pH and sulphates and to facilitate comparison between the two wines taking these variables and the best models into consideration.

This project focuses on analyzing wine quality using a dataset containing various chemical properties of wines. The goal is to explore the dataset, understand its central tendencies, and develop machine learning (ML) models to predict wine quality based on these features.

This repository contains important experiments and notebooks on wine quality analysis, related to the project of real-time wine sensing tool in Software Engineering and Project coursework.