The Use of a K-Nearest Neighbour Classifier and Other Machine Learning Techniques for Exploratory Data Analysis of Red Wine Quality

Abstract

This research investigates the use of a K-Nearest Neighbours (KNN) model and further Machine Learning methodologies for doing Exploratory Data Analysis (EDA) on the quality of red wine. The dataset consists of a wide range of chemical and sensory characteristics that are linked to red wines. The study utilises rigorous data preparation techniques, such as addressing missing values and outliers, to prepare the dataset for analysis. The K-nearest neighbours (KNN) model, known for its simplicity and ability to capture local patterns, is used to reveal complex linkages within the dataset. This research assesses the efficacy of the KNN model and many other Machine Learning approaches by using appropriate measures, therefore elucidating their predictive capacities and ability to generalise to unfamiliar data. The purpose of exploratory investigations is to investigate and access the potential benefits of combining feature engineering and dimensionality reduction techniques in order to improve model interpretability and overall performance. The findings of this study provide significant contributions to our understanding of the intricate connections between chemical and sensory characteristics in red wines. These insights have practical consequences for professionals in viticulture, winemaking, and research within this domain. Additionally, this work highlights and employs the adaptability of Machine Learning in deciphering patterns within complex datasets, hence offering prospects for further investigation in the field of wine quality analysis.