Simple Nearest Neighbors Regression and Classification

Offered By
Coursera Project Network
In this Guided Project, you will:

Formulate small examples of KNN classification by hand

Implement a KNN Classification algorithm in Python

Implement a KNN Regression algorithm in Python

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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. We’re currently working on providing the same experience in other regions.

Skills you will develop

Statistical AnalysisMachine LearningPython Programmingregressionclassification

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understanding the Basic Structure of a KNN model

  2. Computing a simple KNN by hand

  3. Looking at an example of a KNN in action in Python

  4. Implementing an example KNN Regression in Python

  5. Implementing an example KNN Classification in Python

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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