> ## Documentation Index
> Fetch the complete documentation index at: https://shapely.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# KNN Classifier
> K-Nearest Neighbors (KNN) classifies a data point based on the majority class of its nearest neighbors. It finds the K closest training samples and assigns the most common class among them.
This simple yet effective algorithm requires no training phase. It memorizes the data and makes decisions at prediction time.
* KNN is sensitive to the scale of features. Always use the **Feature Scaler** node to normalize your data before applying KNN. Use the **K-Finder** analysis node to determine the optimal K value.
* Ideal for initial exploration of classification problems before trying more complex models.
### Use Cases
* **Room similarity detection**: Find rooms most similar in shape and area to a reference room.
* **Zone classification**: Assign zones (residential, commercial, industrial) based on spatial properties.
