packages = [ "numpy", ] [[fetch]] files = ["./main.py", "./starbucks_kmeans/utils.py", "./starbucks_kmeans/kmeans.py","./assets/data/starbucks_drinks.csv"]


Hot Coffees
Hot Teas
Hot Drinks
Frappuccino
Cold Coffees
Iced Teas
Cold Drinks
Recommendations

Top Recommended Details
Name:
Category:
Similar Drinks:




How to use this app
When you load or reload the page, you'll see a list of unchecked boxes. Just find your favorite beverages and select them.

As you make your selections, you'll notice the right-hand side updating to provide feedback on your top recommended drinks.

When you're ready to start over, simply click the 'Clear Drinks' button!

What is a K-Means?
K-Means is a widely used clustering algorithm in artificial intelligence. K-Means operates by adjusting centroid nodes along a graph based on the Pythagorean theorem to identify similar data nodes.

Although powerful and relatively easy to implement, using K-Means effectively does require a data scientist to carefully review the results. This is because issues such as over-fitting or under-fitting a cluster, selecting an incorrect number of centroids, or not having enough centroids can occur.