This project focuses on the interactive visualization and analysis of the Wine dataset, utilizing Bokeh, a powerful Python library designed for creating interactive plots and dashboards. The dataset includes chemical composition data for wines derived from three different cultivars grown in the same region of Italy. These measurements span multiple features such as alcohol content, malic acid, ash, and flavanoids.
Using Bokeh, the project constructs interactive scatter plots, pairwise feature comparisons, and Principal Component Analysis (PCA) projections to explore and interpret the relationships among the variables. The interactivity allows users to zoom, pan, and hover over data points, providing dynamic insights into how different chemical properties relate to each other and how cultivars cluster in multi-dimensional feature space.
Through PCA, the dimensionality of the dataset is reduced, enabling clearer visualization of clustering patterns between wine types. Bokeh’s intuitive tools further enhance the user experience by enabling real-time filtering and selection of specific cultivars or chemical features.
The outcome of this project demonstrates how interactive visualizations can significantly enhance the interpretability of complex datasets. It also highlights Bokeh’s effectiveness as a data exploration and communication tool in the data science workflow. This approach provides both technical users and stakeholders with an accessible, engaging method to analyze multidimensional data for pattern recognition and decision-making.