📌 GitHub link for the complete code is provided below.
https://github.com/AarohiSingla/Knn-Algorithm
In this video, I explain the K-Nearest Neighbors (KNN) algorithm using both theoretical concepts and a practical example to help you understand how KNN works in Machine Learning.
We begin with the intuition behind KNN and distance-based learning, and then move to a hands-on implementation, showing how the algorithm is applied to real data. The GitHub repository link for the complete code is shared in the description for practice and reference.
In this video, you’ll learn:
What KNN is and how it works
Distance metrics used in KNN (Euclidean, Manhattan, etc.)
Choosing the right value of K
Practical example of KNN in Machine Learning
Advantages and limitations of KNN
How KNN is implemented using Python
This video is ideal for:
Beginners learning Machine Learning
Students preparing for ML interviews
Anyone who wants a clear mix of theory and practice
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