JKISeason4-3

<p><strong>Challenge 3: Nutritional Composition of Groceries</strong></p><p><strong>Level:</strong> Medium<br><br><strong>Description:&nbsp;</strong>You are a data scientist working for a grocery store that focuses on wellness and health. One of your first tasks in your new job is to go over the grocery's inventory and find patterns in the items they sell, based on nutritional composition. This will help them assess if they need to tweak their offerings, and where, to match their ethos of wellness and health.<br><br><em>Beginner-friendly objectives:</em> 1. Load and normalize the grocery data. 2. Cluster the data based on its numeric values using an unsupervised learning algorithm such as <strong>k-Means</strong>. 3. Denormalize the data after clustering it.<br><br><em>Intermediate-friendly objectives:</em> 1. Visualize the clustering results using scatter plots and analyze the distribution of clusters. Use flow variables to dynamically control the scatterplot and enhance interactivity. 2. Perform dimensionality reduction using PCA to simplify the dataset while retaining essential information. 3. Visualize the results with scatterplots as well.<br><br>What patterns can you find? What recommendations and insights can you come up with based on these patterns?<br><br><strong>Author:</strong> <strong>Aline Bessa</strong><br><br><strong>Dataset:</strong> <strong>Groceries Dataset in KNIME Community Hub</strong></p>