Exploratory Data Analysis for Red Wine Quality Prediction Using a Decision Tree Approach and Machine Learning Methods

The present research investigates the significance of the Exploratory Data Processing (EDP) phase as an essential first step in predicting the quality of red wine utilising a Decision Tree technique within the field of Machine Learning. The dataset includes a wide range of chemical traits and sensory aspects that are characteristic of red wines. The process of Exploratory Data Analysis (EDA) entails a thorough examination of data, including rigorous scrutiny, data cleansing, and rigorous statistical analysis, all aimed at ensuring the integrity and quality of the data. Visualisations play a crucial role in facilitating the identification and exploration of patterns and connections inherent in a given information. The use of feature engineering and dimensionality reduction techniques is implemented to improve the prediction capabilities of the model. The selection of a Decision Tree algorithm is motivated by its interpretability and capacity to capture non-linear interactions. The performance of the trained model is assessed using established criteria to verify its robustness and ability to generalise. The EDP-centric methodology used in this study establishes the foundation for a sophisticated and precise prediction framework for assessing the quality of red wine, hence providing significant insights to the wine industry.