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# Simple Nearest Neighbors Regression and Classification

### Overview

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In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python. A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems is the k-nearest neighbors (KNN) algorithm. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which class corresponds to that unknown data point. The unknown is characterized by a straightforward neighborly vote, where the "winner" class is the class of near neighbors. It is most commonly used for predictive decision-making. For instance,: Is a consumer going to default on a loan or not? Will the company make a profit? Should we extend into a certain sector of the market? Note: This course works best for learners who are based in the North America region. Weâ€™re currently working on providing the same experience in other regions.

### Syllabus

• Simple Nearest Neighbors Regression and Classification
• In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making. A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems, the k-nearest neighbors (KNN) algorithm can be used for a variety of prediction problems. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which class corresponds to that unknown data point. The unknown is characterized by a straightforward neighborly vote, where the "winner" class is the class of near neighbors. It is most commonly used for predictive decision-making.

### Taught by

Charles Ivan Niswander II

## Reviews

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