1. PROJECT PRESENTATION
INSTRUCTOR : SIR OMER
SUBMITTED BY:
NAME ROLLNO
MAHAM SAJID 2
SAMIA AKHTER 27
MEHAK GHAIS KHAN 52
MCS MORNING 3RD

2. PROBLEM STATEMENT
 The objective of project was to classify the students
into two categories ‘Male’ and ‘Female’.
 This classification is to be done on the basis of their
educational record.
 We have to approach the classification by applying
unsupervised learning approach i.e kmeans and
any supervised learning approach.

3. SOLUTION APPROACH
Data Collection and Data entrance:
We collected the data of students targeting some
attributes which can be useful In decision making .
Data load in Matlab:
The data files were loaded in to Matlab
Application of Kmeans:
Matlab queries were applied on the data to get the
result
Data Division:
The whole data was divided into 70% of male and
female for Train dataset. Remaining 30% was
divided into Male and Female Test dataset.
Knn Application ; knn was applied on test and train
data

4. DATA SET
Rows 174
Features 8 (Category, sgp1, sp2, cgp, total
marks, database gp , oodm and
graphics gp)
Female data 111 rows
Mal data 63 rows

5. DATA SET

6. CLASSIFICATION ALGORITHMS
 We applied
1) Kmeans
2) Knn
3) Ann (optional to compare its accuracy with knn)

7. RESULT OF KMEANS
Cluster 1
Cluster 2
Centroid
Separation between two classified clusters

8. KMEANS RESULT
 % accuracy =58.7
 % error=41.3

9.  The Blue shaded area left to the 0 of cluster1 along
x-axis is misclassified because it is towards
negative value
 Cluster2 is better classified than cluster1 because it
is more towards positive 1 value.
RESULT OF KMEANS IN THE FORM OF SILHOUTTE
GRAPH

10. KNN ALGORITHM
 Traning data:
 70% training data of females=77 rows
 70% trainning data of males=44
 Testing data:
 30% testing data of females=34
 30% testing data of males=19

11. RESULT OF KNN WITH DIFFERENT K
Value of k %Training accuracy %Testing
accuracy
1 98.3871 50
2 71.7742 56
3 70.1613 52
5 67.7419 50
7 65.3226 52
8 60.4839 58
9 63.7097 54

12. PLOT OF TRAIN AND TEST ACCURACIES WITH
DIFFERENT K
0
20
40
60
80
100
120
k1 k2 k3 k5 k7 k8 k9
train acc
test accu

13. ANOTHER GRAPH OF RESULT OF KNN
0
10
20
30
40
50
60
70
80
90
100
k1 k2 k3 k5 k7 k8 k9
test accu
train accu

14. RESULT OF ANN WITH H=10

15. COMPARISON OF RESULTS OF ALL
ALGORITHMS
Algorithm Trainning
accuracy
Testing
accuracy
accuracy error
kmeans --------- ----------- 58.7 41.3
Knn with
k=2
71.7742 56 ---------- -----------
Ann with
h=10
65.6 73.1 ----------- ------------

16. CONCLUSION
 Accuracy of unsupervised algo (kmeans) was
improved by supervised learning algorithms knn
and ann.
 Training accuracy of knn is better than Ann at value
k=2.
 Testing accuracy of Ann is greater then knn at
hidden layers 10.