KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn

What’s in it for you?
Why do we need KNN?
What is KNN?
How do we choose the factor ‘K’?
When do we use KNN?
How does KNN Algorithm work?
Use Case: Predict whether a person will have diabetes or
not

Why KNN?
By now, we all know
Machine learning models
makes predictions by
learning from the past
data available
Input value
Machine Learning Model
Predicted Output

No dear, you can
differentiate
between a cat
and a dog based
on their
characteristics

No dear, you can
differentiate
between a cat
and a dog based
on their
characteristics
DOGSCATS
Sharp Claws, uses to climb
Smaller length of ears
Meows and purrs
Doesn’t love to play around
Dull Claws
Bigger length of ears
Barks
Loves to run around

No dear, you can
differentiate
between a cat
and a dog based
on their
characteristics
DOGSCATS
Length of ears 
Sharpnessofclaws

Now tell me if it
is a cat or a dog?

Now tell me if
it’s a cat or a
dog?
DOGSCATS
Length of ears 
Sharpnessofclaws

It’s features are
more like cats, it
must be a cat!

DOGSCATS
Length of ears 
Sharpofclaws

Why KNN?
Because KNN is based on
feature similarity, we can
do classification using KNN
Classifier!
Input value
KNN
Predicted Output

What is KNN Algorithm?
KNN – K Nearest Neighbors, is one of the simplest Supervised Machine Learning algorithm
mostly used for
Classification
It classifies a data point based on how its
neighbors are classified

KNN stores all available cases and
classifies new cases based on a
similarity measure
Chloride Level 
SulphurDioxideLevel
RED or WHITE?

But, what is K?
Chloride Level 
RED or WHITE?

RED or WHITE?
k in KNN is a parameter that refers
to the number of nearest neighbors
to include in the majority voting
process
K=5
Chloride Level 

RED or WHITE?
A data point is classified by majority
votes from its 5 nearest neighbors
K=5
Chloride Level 

Chloride Level 
SulphurDioxideLevel RED
Here, the unknown point would be
classified as red, since 4 out of 5
neighbors are red
K=5

How do we choose the factor ‘k’?
?
K=3
New
Variable
KNN Algorithm is based on feature similarity: Choosing the right value of k is a process
called parameter tuning, and is important for better accuracy

?
k=3
New
Variable
So at k=3, we can classify ‘?’ as

?
k=3
k=7
New
Variable
But at k=7, we classify ‘?’ as

?
K=1
K=3
New
Variable
So at k=3, we can classify ‘?’ as
The class of unknown data
point was at k=3 but
changed at k=7, so which k
should we choose?

Odd value of K is selected to avoid confusion between two classes of data
Sqrt(n), where n is the total number of data points
To choose a value of k:

Odd value of K is selected to avoid confusion between two classes of data
Sqrt(n), where n is the total number of data points
To choose a value of k:
Higher value of k has lesser
chance of error

When do we use KNN Algorithm?
We can use KNN when
Dog
Data is labeled

We can use KNN when
Data is noise free
Data is labeled
Dog
Noise
Weight(x2) Height(y2) Class
51 167Underweight
62 182 one-fourty
69 176 23
64 173 hello kitty
65 172 Normal

We can use KNN when
Data is noise freeDataset is small
Data is labeled
Dog
Because KNN is a ‘lazy learner’ i.e.
doesn’t learn a discriminative function
from the training set
Noise
51 167Underweight
62 182 one-fourty
69 176 23
64 173 hello kitty
65 172 Normal

Consider a dataset having two variables: height (cm) & weight (kg) and each point is
classified as Normal or Underweight
51 167 Underweight
62 182 Normal
69 176 Normal
64 173 Normal
65 172 Normal
56 174 Underweight
58 169 Normal
57 173 Normal
55 170 Normal

On the basis of the given data we have to classify the below set as Normal or
Underweight using KNN
Assuming, we
don’t know how to
calculate BMI!
57 kg 170 cm ?

To find the nearest neighbors, we will calculate Euclidean
distance
But, what is
Euclidean distance?

According to the Euclidean distance formula, the distance between two points in the plane with
coordinates (x, y) and (a, b) is given by:
dist(d)= √(x - a)² + (y - b)²

Let’s calculate it to understand clearly:
Similarly, we will calculate Euclidean distance of unknown data point from
all the points in the dataset
dist(d1)= √(170-167)² + (57-51)² ~= 6.7
d1
dist(d2)= √(170-182)² + (57-62)² ~= 13
d2
dist(d3)= √(170-176)² + (57-69)² ~= 13.4
d3
Unknown data point

Hence, we have calculated the Euclidean distance of unknown data point from all
the points as shown:
Where (x1, y1) = (57, 170) whose
class we have to classify
Weight(x2) Height(y2) Class Euclidean Distance
51 167 Underweight 6.7
62 182 Normal 13
69 176 Normal 13.4
64 173 Normal 7.6
65 172 Normal 8.2
58 169 Normal 1.4
57 173 Normal 3
55 170 Normal 2

Now, lets calculate the nearest neighbor at k=3
k = 3
57 kg 170 cm ?
62 182 Normal 13
69 176 Normal 13.4
64 173 Normal 7.6
65 172 Normal 8.2
58 169 Normal 1.4
57 173 Normal 3
55 170 Normal 2

62 182 Normal 13
69 176 Normal 13.4
64 173 Normal 7.6
65 172 Normal 8.2
58 169 Normal 1.4
57 173 Normal 3
55 170 Normal 2
Now, lets calculate the nearest neighbor at k=3
k = 3
57 kg 170 cm ?
We have n=10,
And sqrt(10)=3.1
Hence, we have taken k=3

So, majority neighbors are pointing towards ‘Normal’
Hence, as per KNN algorithm the class of (57, 170) should be ‘Normal’
k = 3
Class Euclidean Distance
Underweight 6.7
Normal 13
Normal 13.4
Normal 7.6
Normal 8.2
Underweight 4.1
Normal 1.4
Normal 3
Normal 2

Recap of KNN
Recap of KNN
• A positive integer k is specified, along with
a new sample
• We select the k entries in our database
which are closest to the new sample
• We find the most common classification of
these entries
• This is the classification we give to the
new sample

KNN - Predict diabetes
Objective: Predict whether a person will be diagnosed with diabetes or not
We have a dataset of 768 people who were or were not
diagnosed with diabetes

Import the required Scikit-learn libraries as shown:

Load the dataset and have a look:

Values of columns like ‘Glucose’, BloodPressure’ cannot be accepted as zeroes because it will affect the outcome
We can replace such values with the mean of the respective column:

Before proceeding further, let’s split the dataset into train and test:

Feature Scaling:
Rule of thumb: Any algorithm that
computes distance or assumes
normality, scale your features!

Then define the model using KNeighborsClassifier and fit the train data in
the model
N_neighbors here is ‘K’
p is the power parameter to define
the metric used, which is ‘Euclidean’
in our case

There are other metrics
also to evaluate the
distance like Manhattan
distance , Minkowski
distance etc

Let’s predict the test results:

It’s important to evaluate the model, let’s use confusion matrix to do that:

Calculate accuracy of the model:

So, we have created a model using
KNN which can predict whether a
person will have diabetes or not

And accuracy of 80% tells us that it is
a pretty fair fit in the model!

Summary
Why we need knn? Eucledian distance Choosing the value of k
Knn classifier for diabetes predictionHow KNN works?

KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn

KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Beginners | Simplilearn

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