# k-Nearest Neighbors (k-NN) Classification

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Implement the k-Nearest Neighbors (k-NN) algorithm for classification using the following steps:

1. For each test data point, compute the Euclidean distance to each training data point.

2. Identify the 'k' training data points with the smallest distances.

3. Among these 'k' points, determine the most frequent label.

4. Assign this label to the test data point.

The k-Nearest Neighbors (k-NN) algorithm is a type of instance-based learning where the function is only approximated locally, and all computation is deferred until classification. The algorithm works by finding the 'k' training samples closest in distance to the test point and predicts the output value based on the most common output value among them.$[Euclidean distance = sqrt(sum_{i=1}^{n} (x_i - y_i)^2)]$

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