K-Nearest Neighbors Regression Algorithm Implementation

Introduction

In this project, you will learn how to implement the K-Nearest Neighbors (KNN) regression algorithm using Python. KNN is a widely used machine learning method, commonly used for classification problems. However, it can also be applied to regression tasks, where the goal is to predict a continuous target value.

🎯 Tasks

In this project, you will learn:

• How to understand the KNN regression algorithm and its working principle
• How to implement the KNN regression algorithm in Python
• How to calculate the Euclidean distances between the test data and training data
• How to identify the k nearest neighbors and retrieve their target values
• How to compute the average of the k nearest neighbors' target values to predict the output for the test data

🏆 Achievements

After completing this project, you will be able to:

• Implement the KNN regression algorithm from scratch using Python
• Use the Euclidean distance as a distance measure in the KNN algorithm
• Apply the KNN regression algorithm to predict continuous target values
• Demonstrate practical skills in machine learning algorithm implementation