Learn all about the k-Nearest Neighbors (kNN) algorithm in Python, including how to implement kNN from scratch, kNN hyperparameter tuning, and improving kNN performance using bagging.

Learn all about the k-Nearest Neighbors (kNN) algorithm in Python, including how to implement kNN from scratch, kNN hyperparameter tuning, and improving kNN performance using bagging.

In this tutorial, you’ll get a thorough introduction to the k-Nearest Neighbors (kNN) algorithm in Python. The kNN algorithm is one of the most famous machine learning algorithms and an absolute must-have in your machine learning toolbox. Python is the go-to programming language for machine learning, so what better way to discover kNN than with Python’s famous packages NumPy and scikit-learn!

Below, you’ll explore the kNN algorithm both in theory and in practice. While many tutorials skip the theoretical part and focus only on the use of libraries, you don’t want to be dependent on automated packages for your machine learning. It’s important to learn about the mechanics of machine learning algorithms to understand their potential and limitations.

At the same time, it’s essential to understand how to use an algorithm in practice. With that in mind, in the second part of this tutorial, you’ll focus on the use of kNN in the Python library scikit-learn, with advanced tips for pushing performance to the max.

**In this tutorial, you’ll learn how to:**

- Explain the
**kNN algorithm**both intuitively and mathematically - Implement kNN in Python
**from scratch**using**NumPy** - Use kNN in Python with
**scikit-learn** - Tune
**hyperparameters**of kNN using`GridSearchCV`

- Add
**bagging**to kNN for better performance

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