This is our project for our artificial intelligence lab.The topic of our project is " Finding suitable classifier for different datasets ."

1. Finding suitable classifier
for different datasets

2. Submitted by -
1.Zahidul Islam Razu (ID – 011 132 047)
2.MD.Fazle Rabbi (ID- 011 132 004 )
Submitted to -
Dr. Swakkhar Shatabda ,Assistant Professor
,Department Of C.S.E. , United International University

3. Fitting Contact lenses

4. Outline
 The main concept of this project is to find suitable contact lenses for suitable
people.

5. Dataset
Name : Fitting
contact
lenses.
Features :
Number of
Instances:
24
Number of
Attributes:
4

6. Class Information
1. age of the patient.
2.spectacle prescription.
3. astigmatic.
4. tear production rate.

7. Attribute information
3 Classes
1 : the patient should be fitted with hard contact
lenses
2 : the patient should be fitted with soft contact
lenses
3 : the patient should not be fitted with contact
lenses.

8. Figure/Visualization of Class
Attributes
5
4
15
0
2
4
6
8
10
12
14
16
Soft Hard None

9. Used Algorithms
K – Nearest
Neighbors
Naïve-Bayes
Logistic
Regression
Decision Tree

10. Accuracy /Error Percentage

11. Accuracy /Error Percentage
Name of
the
classifier
Instances Correctly
classified
Accuracy
Percentage
Error Error
Percentage
Naïve Bayes 24 17 70.833% 7 29.167%
K-N-N 24 19 79.166% 5 20.834%
Logistic 24 16 66.667% 8 33.334%
Decision
Tree
24 20 83.333% 4 16.667%

12. Confusion Matrix

13. Result
After classifying the datasets with
all the classifiers ,we find that
decision tree has better accuracy
and better confusion matrix than
other classifiers. So, we should
choose decision tree for this
datasets.

14. Discussion
Source- Cendrowska, J. "PRISM: An algorithm for inducing modular rules",
International Journal of Man-Machine Studies, 1987, 27, 349-370.

15. Glass Identification

16. Outline
 The main concept of this project is to identify suitable glasses from it’s raw
materials.

17. Dataset
Name :Glass
Identification.
Features :
Number of
Instances:
214
Number of
Attributes:
10

18. Attribute Information
10 attributes.
1. Id number: 1 to 214
2. RI: refractive index
3. Na: Sodium
4. Mg: Magnesium
5. Al: Aluminum
6. Si: Silicon
7. K: Potassium
8. Ca: Calcium
9. Ba: Barium
10.Fe: Iron

19. Class Information
7 classes
1 .building windows float processed
2 .building windows non float processed
3. vehicle windows float processed
4 .vehicle windows non float processed
5 .containers
6. tableware
7 .headlamp

20. Figure/Visualization of Class Attributes
70
76
17
0
13
9
29
0
10
20
30
40
50
60
70
80
B.W (float) B.W (Non-float) V.W(float) V.W(Non-float) Containers Tableware Headlamps

21. Used Algorithms
K – Nearest
Neighbors
Naïve-Bayes
Logistic
Regression
Decision Tree

22. Accuracy /Error Percentage

23. Accuracy /Error Percentage
Name of
the
classifier
Instances Correctly
classified
Accuracy
Percentage
Error Error
Percentage
Naïve Bayes 214 104 48.398% 110 51.401%
K-N-N 214 151 70.560% 63 29.440%
Logistic 214 137 64.019% 77 35.981%
Decision
Tree
214 143 66.825% 71 33.175%

24. Confusion Matrix

25. Result
After classifying the datasets with
all the classifiers ,we find that k
nearest neighbors has better
accuracy and better confusion
matrix than other classifiers. So, we
should choose K Nearest neighbors
for this datasets.

26. Discussion
Source-B. German, Central Research Establishment , Home Office Forensic Science
Service,
Aldermaston, Reading, Berkshire RG7 4PN

27. Any Questions ?
Thank you !