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Coding & Algorithms questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master coding & algorithms interviews.

What difficulty level is this interview question?

This is a medium difficulty Coding & Algorithms question, commonly asked during Technical Screen rounds at J.P. Morgan.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at J.P. Morgan during technical interviews.

Implement KNN from Scratch | J.P. Morgan Interview Question

Implement KNN from Scratch

Last updated: Apr 19, 2026

Quick Overview

This question evaluates a candidate's understanding and practical implementation skills for instance-based supervised learning, specifically the k-nearest neighbors algorithm, covering distance metrics, neighbor selection, majority voting and tie-breaking, feature scaling effects, time complexity, and model selection considerations like k and weighting. It is commonly asked to assess both conceptual grasp of non-parametric classification and practical coding ability to implement and analyze algorithms, and it falls under coding & algorithms and machine learning domains with a mix of conceptual understanding and practical application.

J.P. Morgan

Apr 7, 2026, 12:00 AM

Data Scientist

Technical Screen

Coding & Algorithms

Coding Console

Implement a k-nearest neighbors (KNN) classifier from scratch in Python without using scikit-learn's KNN implementation. Given training features X_train with shape (n_samples, n_features), training labels y_train, a test set X_test, and an integer k, return the predicted class for each test example.

In your explanation, address:

how distances are computed, using Euclidean distance by default
how the k nearest neighbors are selected
how majority voting works and how you would break ties deterministically
why feature scaling matters for KNN
the training-time and inference-time complexity
how you would choose k in practice and when weighted KNN may be preferable

Solution

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