Machine Learning in Python: k-Nearest Neighbors Classification

3 tutorials

In the Machine Learning - Regression in Python module, we started our exploration into the world of machine learning through exploratory data analysis (EDA) and analyzing a dataset from start to finish. In this module, we will explore the famous k-Nearest Neighbors (kNN) algorithm.

Specifically, we will explore using kNN algorithm to solve classification and regression problems. With these two types of problems, you would be able to add kNN algorithm into your machine learning toolbox, which is an absolute must-have. Once again, our machine learning tool of choice will be scikit-learn.

Note that this module assumes you have some background in Python, EDA and basic machine learning, particularly regarding regression in Python. If these are completely new to you, please go through the following StackUp campaigns/modules first before starting this module:

Helpful prior knowledge


Learning Outcomes

By the end of this module, you will be able to:

  • Gain a reinforced understanding of machine learning fundamentals
  • Understand the concepts behind the kNN algorithm
  • Use scikit-learn to solve regression and classification problems using the kNN algorithm
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