diff --git a/contrib/machine-learning/K-nearest neighbor (KNN).md b/contrib/machine-learning/knn.md
similarity index 99%
rename from contrib/machine-learning/K-nearest neighbor (KNN).md
rename to contrib/machine-learning/knn.md
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--- a/contrib/machine-learning/K-nearest neighbor (KNN).md	
+++ b/contrib/machine-learning/knn.md
@@ -119,4 +119,4 @@ plt.show()
 - **Feature Scaling:** Since KNN relies on distance calculations, features should be scaled (standardized or normalized) to ensure that all features contribute equally to the distance computation.
 - **Distance Metrics:** The choice of distance metric (Euclidean, Manhattan, etc.) can affect the performance of the algorithm.
 
-In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance.
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+In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance.