How to Use This Visualization

• Set the Value of K: Use the input field labeled “K” to specify the number of neighbors you want to use for classification. The default value is 3.
• Set Data Size: Use the “Data Size” input to control how many random data points are generated when you initialize.
• Initialize: Press the “Initialize” button to start the KNN classification process. This will randomly generate data points.
• Add Data Points:
  • Click the “Add Red Data” button to add a red data point to the canvas.
  • Click the “Add Blue Data” button to add a blue data point to the canvas.
  • You can also click directly on the canvas to add data points. The color of the point will be determined by which “Add Data” button you last clicked.
• Clear: You can clear the canvas by refreshing the page.

About KNN Classification

K-Nearest Neighbors (KNN) is a simple yet powerful algorithm used for classification. The basic idea is to classify a new data point based on the majority class of its K nearest neighbors in the training data.