Predicting the Red Wine Quality using Exploratory Data Analysis and VGG19 Convolutional Neural Network in Deep Learning

The quality of red wine is a multifaceted characteristic influenced by various chemical components. This study adopts a dual approach, integrating Exploratory Data Analysis (EDA) and the VGG19 CNN Model from Deep Learning to anticipate red wine quality. The dataset encompasses diverse chemical features such as acidity, alcohol concentration, volatile acidity, and quality ratings. Utilizing histograms, scatter plots, and correlation matrices, the distribution and interplay of variables are clarified. Subsequently, the robust VGG19 CNN Model is employed to categorize red wines into quality groups. Trained on a subset of the dataset, the model estimates the likelihood of wines falling into distinct quality categories using a binary classification approach. The dataset is divided into training and testing sets for model training and evaluation. Model performance is assessed through metrics like accuracy, precision, recall, and F1-score. Feature selection techniques are applied to identify key chemical properties influencing wine quality. The acquired VGG19 CNN model serves as a valuable interpretive tool, allowing an examination of the relative importance of each factor through coefficient analysis. A comprehensive understanding of chemical constituents is pivotal for enhancing wine production processes, empowering producers to optimize quality and meet customer expectations.