A nearest-neighbor classifier stores training observations and votes among the closest ones when a new row arrives. The n_neighbors setting controls how many observations take part in the vote. A small neighborhood can follow local noise; a larger one smooths decisions across more rows. Neither choice is best for every problem.
Distance depends on feature scale. An input measured in large units can dominate even when it carries little useful signal. Fit StandardScaler on training rows inside the same pipeline as the classifier. Reuse that transformation when predicting the holdout. These tasks vary the requested neighbor count and use fresh fictional customer records. Odd counts reduce some ties in binary voting, but repeated points and equal distances can still require attention in a real dataset.