This document discusses the K-Nearest Neighbors (KNN) classification algorithm. It provides an example dataset classifying individuals as "adult" or "child" based on their years in school and height. It then demonstrates how KNN would classify a new data point based on different values of K. The document also discusses a regression dataset to predict house prices based on square footage and age. It explains how to evaluate different values of K through hyperparameter tuning and applies KNN to predict the price of a new house. In the end, it notes a drawback of KNN related to scaling of different features.

1. AI Club

2. AI Club
KNN
K-Nearest Neighbours Algorithm

3. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
Adult
Child

4. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
Adult
Child

5. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
Adult
Child
Input to AI
Years in school = 6
Height = 5ft

6. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
If K = 1
Adult
Child

7. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
If K = 3
Adult
Child

8. Regression Dataset
index sq_ft age price
1 895 26 707158
2 827 61 285327
3 873 58 365744
4 847 58 338163
5 801 66 204300
6 522 58 14117
7 784 46 388363
8 450 25 121220
9 657 33 381713
10 425 52 82357
0
70
450
30
Sq_ft
age
900
1
3
2
4
5
6
7
8
9
10

9. Regression Dataset
index sq_ft age price
1 895 26 707158
2 827 61 285327
3 873 58 365744
4 847 58 338163
5 801 66 204300
6 522 58 14117
7 784 46 388363
8 450 25 121220
9 657 33 381713
10 425 52 82357
0
70
450
30
Sq_ft
age
900
1
3
2
4
5
6
7
8
9
10
If K = 3

10. How do you find the best K?
• The process of find the best hyper-parameters
for a algorithm is called hyper-parameter
tuning.
• In case of KNN, we tune a single parameter
called K.

11. What is a good price for
House 1?
(a)10,000 (b) 5,000?
House
Number
Square feet age Price
1 1000 0
2 800 0 10,000$
3 1000 100 5,000$

12. What do you think KNN will do if K=1?
Who is the nearest neighbor of the first
house?
House
Number
Square feet age Price
1 1000 0
2 800 0 10,000$
3 1000 100 5,000$

13. Square feet age Price
1000 0
800 0 10,000$
1000 100 5,000$
Distance(House 1, House 2) = 200
Distance(House 1, House 3) = 100

14. Drawbacks of KNN
• Scaling: Different features may have different
ranges in numbers

15. Lets try it!
• Build an AI to predict
house prices
• Change K and see
how the AI behaves!

16. THANK YOU
https://aiclub.world
info@pyxeda.ai