Insights into Red Wine Quality: Utilizing Gradient Boosting for Data Exploration and Prediction Analysis

Many different chemical components affect the complexity and quality of red wine. This research uses a multilingual approach to forecast red wine quality by combining Exploratory Data Analysis (EDA) with the Gradient Boosting method from Machine Learning. Some of the many chemical properties included in the dataset being studied include acidity, alcohol concentration, volatile acidity, and quality ratings, among others. It is usual practice to use statistical tools like histograms, scatter plots, and correlation matrices to display the distribution and correlations between variables. Afterwards, various quality classifications for red wines are applied using Gradient Boosting, a robust classification approach. Using a binary classification strategy, the Gradient Boosting model estimates the likelihood of wines being categorised into different quality categories by training on a portion of the dataset. The dataset is divided into two parts, the training set and the testing set, to make it easier to train and evaluate models. When evaluating the model’s performance in predicting red wine quality, performance metrics including recall, accuracy, precision, and F1-score are chosen. In addition, the chemical qualities that have the greatest influence on the wine’s quality are identified using feature selection procedures. The produced Gradient Boosting model is useful for interpretation since it allows one to examine coefficients to find out how important each element is. Improving wine production processes through a comprehensive knowledge of chemical constituents is crucial for producers to optimise quality and exceed customer expectations.