The world of wine is intricate and fascinating, with numerous factors influencing the quality of wines produced. This project aims to delve into the Wine Quality Dataset to uncover patterns, relationships, and insights that shed light on what makes a wine exceptional.
The Wine Quality Dataset contains information about various attributes related to the chemical composition of wines, such as acidity, pH levels, residual sugar, alcohol content, and more. Additionally, it provides quality ratings assigned by human experts. By analyzing this dataset, we aim to understand which factors have the most significant impact on wine quality.
Through data visualization techniques, we will uncover hidden insights. Visual representations such as histograms, scatter plots, and heatmaps will help us identify trends, outliers, and potential relationships among attributes.
Below is the Correlation Heatmap that illustrates the relationships between different attributes in the Wine Quality Dataset:
