Choose Number of Neighbors (k)

• Use the slider labeled "Number of neighbors (k):"
• Valid range: 1 to 15
• The selected value is shown below the slider.

Types of Points

• Well Separated: Two distinct clusters.
• Overlapping: Some overlap between classes.
• Concentric: Class 0 inside a ring of Class 1.

Choose Feature transformations:

• None: Raw features
• Standardization: Zero mean, unit variance
• Min-Max: Normalize between 0 and 1

Generate Dataset

• Click "Generate Data"
• Points will be generated based on selected distribution and scaling.

Classify a Single Point

• Click "Classify New Point"
• A random point is generated and classified
• You’ll see a triangle on the plot indicating the prediction

Simulate Multiple Classifications

• Click "Simulate Multiple Tests"
• 100 random test points are generated and classified
• Displays:
  • Accuracy (%)
  • Number of correct predictions (e.g., 82/100)