KNN Algorithm Using R | Edureka

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Agenda
32 41 5
Machine
Learning
What is KNN
Algorithm?
KNN Use Case Hands - On
KNN Algorithm

Introduction To Machine Learning

Machine learning is a subset of artificial intelligence (AI) which
provides machines the ability to learn automatically & improve from
experience without being explicitly programmed.
What Is Machine
Learning? Continuously
feed data

Machine learning is a subset of artificial intelligence (AI) which
provides machines the ability to learn automatically & improve from
experience without being explicitly programmed.
What Is Machine
Learning? Continuously
feed data
Predicted
output

Types Of Machine Learning

Supervised
Machine Learning
Supervised Machine Learning is a learning in which we teach or train
the machine using data which is well labelled.

Unsupervised learning is the training of machine using information
that is unlabelled and allowing the algorithm to act on that
information without guidance
Unsupervised
Machine Learning

Reinforcement
Machine Learning
Reinforcement Learning is a part of Machine learning where an agent is put
in an environment and he learns to behave in this environment by
performing certain actions and observing the rewards which it gets from
those actions.

What Is KNN Algorithm?

What Is KNN Algorithm?
K Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into
the target class, depending on the features of it’s neighbouring data points.
KNN Algorithm

Features Of KNN
Supervised learning
algorithm
Simple
Non-parametric
Lazy algorithm
Classification & regression algorithm
Based on feature similarity
No training step

KNN Use Case

Book Recommendation Using KNN

How KNN Algorithm Works?

How Does KNN Algorithm Work?
Problem Statement: Assign the new data point into one of the two clusters
Class A
Class B

Class A
Class B
Choose the value of ‘k’, here k=3 and find the 3 nearest neighbours

Here k=7, find the 7 nearest neighbours
Class A
Class B

Assign the new data point into Class B since majority of it’s nearest neighbours are of type class B
Class A
Class B

Euclidian Distance
0
1
2
3
4
1 2 3 4 5
P1
P2
Point P1 = (1,4)
Point P2 = (5,1)
Euclidian distance =
Calculations

Demo
YouTube Video Link in the Description

Demo
Problem Statement: Study a bank credit dataset and make a decision about whether to approve the loan of
an applicant based on his profile
KNN algorithm
Approve loan Reject loan

WebDriver vs. IDE vs. RC
➢ Data Warehouse is like a relational database designed for analytical needs.
➢ It functions on the basis of OLAP (Online Analytical Processing).
➢ It is a central location where consolidated data from multiple locations (databases) are stored.

KNN Algorithm Using R | Edureka

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