The Use of a DenseNet201 CNN and Other Deep Learning Techniques for Exploratory Data Analysis of Red Wine Quality

This study delves into the exploration of red wine quality using a DenseNet201 CNN model and various other machine learning methodologies for Exploratory Data Analysis (EDA). The dataset encompasses a diverse array of chemical and sensory attributes associated with red wines. Rigorous data preparation steps, including addressing missing values and outliers, are undertaken to ready the dataset for analysis. The simplicity and local pattern-capturing capability of the DenseNet201 CNN model are leveraged to unveil intricate connections within the dataset. The research evaluates the effectiveness of the DenseNet201 CNN model and several other machine learning approaches through appropriate metrics, thereby assessing their predictive capabilities and generalization to unfamiliar data. The objective of exploratory investigations is to explore and evaluate the potential benefits of combining feature engineering and dimensionality reduction techniques to enhance model interpretability and overall performance. The study's findings make significant contributions to understanding the complex relationships between chemical and sensory characteristics in red wines. These insights carry practical implications for professionals in viticulture, winemaking, and research in this field. Furthermore, the research underscores and utilizes the adaptability of machine learning in deciphering patterns within intricate datasets, opening avenues for further exploration in the realm of wine quality analysis.