Predicting Categories with K-Nearest Neighbors

The k-Nearest Neighbors (k-NN) algorithm is a form of supervised machine learning that is used to predict categories. In this chapter, you will learn about the following:

• Preparing a dataset for machine learning with scikit-learn
• How the k-NN algorithm works under the hood
• Implementing your first k-NN algorithm to predict a fraudulent transaction
• Fine-tuning the parameters of the k-NN algorithm
• Scaling your data for optimized performance

The k-NN algorithm has a wide range of applications in the field of classification and supervised machine learning. Some of the real-world applications for this algorithm include predicting loan defaults and credit-based fraud in the financial industry and predicting whether a patient has cancer in the healthcare industry.

This book's design facilitates the implementation of a robust machine learning pipeline for each and every algorithm mentioned in the book, and a Jupyter Notebook will be required. 

The Jupyter Notebook can be installed on your local machine by following the instructions provided at: https://jupyter.org/install.html.

Alternatively, you can also work with the Jupyter Notebook in the browser by using: https://jupyter.org/try.