Learn all about the KNearest Neighbor (KNN) Algorithm in Machine Learning
KNearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, you’ll find KNN to be among the easiest algorithms to pick up.
And despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning.
The KNN algorithm is simple to understand, easy to explain and perfect to demonstrate to a nontechnical audience (that’s why stakeholders love it!). That’s a key reason why it’s widely used in the industry and why you should know how the algorithm works.
Before you sit for Machine Learning interviews, you should know the answers to these KNN questions:
 What is the KNearest Neighbor (KNN) algorithm?
 Why should you learn KNN in machine learning?
 When should you use KNN?
 Can I use KNN for both classification and regression problems?
 What kind of projects can you do using KNN?
 Do I need to learn Python or R (or any other programming language) to use KNN?
 Will machine learning project interviews ask you about KNN?
 What does the”K” in KNN stand for?
 How to calculate the distance between points in KNN?
This free course by Analytics Vidhya will help you understand what KNearest Neighbor (KNN) is, how the KNN algorithm works, and where KNN fits in the machine learning umbrella. We will also showcase how to implement KNN in Python and R. There’s a lot to learn so enroll and get started!
Who is the KNearest Neighbor (KNN) Algorithm in Python and R Course for?
This course is for anyone who:
 Wants to learn about the popular KNN algorithm and how it works
 Wants to get started with machine learning algorithm
 Is curious about the different algorithms in machine learning
What do you need to get started with the KNN Algorithm in Python and R course?
Here’s what you’ll need:
 A working laptop/desktop with 4 GB RAM
 A working Internet connection
 Basic knowledge of Machine Learning
 Basic knowledge of Python / R  check out this Course first, if you are new to Python
Common Questions Beginners Ask About the KNN Algorithm
What is the KNearest Neighbor (KNN) algorithm?
KNearest Neighbor (KNN) algorithm is a distance based supervised learning algorithm that is used for solving classification problems. In this, we will be looking at the classes of the k nearest neighbors to a new point and assign it the class to which the majority of k neighbours belong too.
To identify the nearest neighbors we use various techniques of measuring distance, the most common of them being the ‘Euclidean Distance’.
Why should you learn KNN in machine learning?
KNN is among the most widely used and popular machine learning algorithms in the industry. Every data scientist, amateur or established, is expected to know the ins and outs of KNN. You should be able to implement it in Python or R (or any other programming language) if you want to land a role in the machine learning space.
Additionally, as you’ll see in the course, KNN unlocks a lot of avenues to solving machine learning problems.
When should you use KNN?
KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry.
Can I use KNN for both classification and regression problems?
Yes! We just answered the question above but let’s expand a bit more on that. To evaluate any technique we generally look at 3 important aspects:
 Ease to interpret output
 Calculation time
 Predictive Power
Let us take a few examples to place KNN on the machine learning scale:
KNN algorithm fairs well across all parameters of considerations. It is commonly used for its ease of interpretation and low calculation time.
What kind of projects can you do using KNN?
You can perform both classification and regression projects. From predicting the price of a house (regression) to identifying if a loan will default or not (classification), you can apply KNN across a range of problems.
We suggest heading to the DataHack platform, picking up a problem of your choice, and applying KNN there to solidify your practical understanding.
Do I need to learn Python or R (or any other programming language) to use KNN?
Well  yes and no. While KNN can still be performed on GUI based tools like KNIME, you should still have a working knowledge of how to implement it in Python or R. That’s a key consideration in the machine learning space.
Will machine learning project interviews ask you about KNN?
There’s a high chance you’ll be asked at least a couple of questions on the KNN algorithm. Depending on the level you’re applying for, you could be asked to explain how KNN works in a certain business scenario and how you would explain the results of a KNN model to a nontechnical audience (a very common question!).
What does the ”K” in KNN stand for?
“K” in “KNN” stands for the number of cases that are considered to be "nearest" when you convert the cases as points in a euclidean space.
That means, if we consider k=3, and when a new point has to be assigned a class, it will be on the basis of the classes of the 3 nearest points that surround the new point as per the euclidean distance.
How to calculate the distance between points in KNN?
There are various methods for calculating this distance, of which the most commonly known methods are – Euclidian, Manhattan (for continuous) and Hamming distance (for categorical). While other methods do exist, these are typically the most common ones and you’ll learn more about them in the course.
Course curriculum

1
Introduction
 Welcome to the Course

2
KNEAREST NEIGHBOUR
 What is KNN?
 Applications of KNN

3
Steps to Build a KNEAREST NEIGHBOUR Model
 Steps to build a KNN model
 Determining right value of k
 How to Calculate distance?
 Issues with distance based algorithms

4
Implementation in Python and R
 Implementation of KNN in Pyhton
 Implementation of KNN in R

5
What's Next?
 More resources for you
Instructor(s)

Analytics Vidhya
Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals.
FAQ
Common questions related to the KNearest Neighbor (KNN) Algorithm in Python and R course

Who should take the KNearest Neighbor (KNN) Algorithm in Python and R course?
This course is designed for anyone who wants to understand what the KNN algorithm is and how it works in machine learning. This is an important concept in machine learning that’s widely used in the industry. All the more reason to start learning today!

I have decent programming experience but no background in machine learning. Is this course right for me?
You should ideally have a basic grasp on machine learning algorithms and know the difference between regression and classification. We suggest enrolling in the Introduction to Data Science course for starters.

What is the fee for the course?
This course is free of cost! All you need to do is sign up and get started.

How long would I have access to the “KNearest Neighbor (KNN) Algorithm in Python and R” course?
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.

How much effort do I need to put in for this course?
You can complete the “KNearest Neighbor (KNN) Algorithm in Python and R” course in a few hours.

I’ve completed this course and have a good grasp on the KNN algorithm. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course.
 Can I download the videos in this course?