The document presents a classification model that predicts students' knowledge level in a subject based on variables like study time, exam performance, and repetition. It uses a k-nearest neighbor algorithm and data from PhD students studying electrical machines. The model was 70% trained and 30% tested. It achieved 89% accuracy in predicting students as having high or low knowledge. The model can help recruitment and colleges assess student knowledge levels.

1. CLASSIFICATION MODEL FOR PREDICTING
STUDENT’S KNOWLEDGE LEVEL ON A SUBJECT
Presented By : Ashish Ranjan
Vaibhav Jain

2. AGENDA
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Introduction & Objective
Variables
Data Set
Rattle Implementation
Decision Tree Overview
GINI INDEX
Model Evaluation : Receiver Operating Characteristic
Business Conclusion

3. CASE STUDY – OBJECTIVE
The data used for the classification model has been implemented at PH.D
level study for determining the knowledge level of student’s on subject
DC Electrical Machine.
Based on study time spend by students, repeated studies for the subject,
study time spend on related topics to the subject, exam performance for
predicting the knowledge level of the student : HIGH OR LOW.
Note: We have used intuitive knowledge classifier (a hybrid ML technique of k-NN
and meta-heuristic exploring methods), k-nearest neighbour algorithm.
Source : Faculty of Technology, Department of Software Engineering ,Turkey
www.UCI.edu

4. VARIABLES
STG (The degree of study time for goal object materials), (IV)
SCG (The degree of repetition number of user for goal object
materials) (Input Variable)
STR (The degree of study time of user for related objects with
goal object) (IV)
LPR (The exam performance of user for related objects with
goal object) (IV)
PEG (The exam performance of user for goal objects) (IV)
UNS (The knowledge level of user) (Target Variable)

5. DATA SET

6. RATTLE IMPLEMENTATION
70% Training Data
tested on 30% Test Data

7. DECISION TREE

8. MODEL EVALUATION & ACCURACY
CONFUSION MATRIX(Test data)
PREDICTED
high
ACTUAL
low
TOTAL
High
(Tp)41
(Fn)3
44
low
(Fp)5
(Tn)29
34
TOTAL
46
32
78
ACCURACY(TP+TN/P+
N)
ERROR
RATE(FP+FN/P+N)
0.897435897
0.102564103

9. GINI INDEX
GINI INDEX
CALCULATION
ROOT Node
0.4838
Internal PEG node
0
0.4838
Diff b/w ROOT and Internal PEG node
Internal LPR node
0.1638
0.32
Diff b/w Root and Internal LPR Node

10. Model Evaluation : Receiver Operating Characteristic (ROC)

11. BUSINESS CONCLUSION
∗ Based on the training set model , we can predict a
student who has scored low in PEG & LPR, has low
knowledge as compared to a student who has scored
higher marks.
Implementation in Real Life Situations :∗ This model can be used by recruitment companies to
access the knowledge level of students before
offering appointment.
∗ College can categorize student’s basis the model
outputs and select the student’s for specialized
training programs.