The image presents Python code that brings the k-Nearest Neighbors algorithm to life. With clearly defined methods for model initialization, fitting, and predicting, it also provides flexibility in calculating distance metrics—whether it’s Euclidean, Manhattan, or cosine. A practical example of training and predicting with the algorithm adds depth, making this a compelling, well-organized code snapshot that’s perfect for hands-on machine learning enthusiasts.
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