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I International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456nternational Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 | IF: 4.101

C3: Learning Behavior Average. 3. K. Prasada Rao, M. V. P. P. Chandra Sekhara Rao,
B. Ramesh, Predicting Learning Behavior of Predicting Learning Behavior of
C
S
using sing
T
Here we need to predict whether X belongs to which Here we need to predict whether X belongs to which Students tudents u Classification lassification Techniques, echniques,
I
class. International Journal of Computer Applications nternational Journal of Computer Applications
P(X/C1)=0.33*0.33*0.33*0.33*0.66*0.33*1*0.33*0.P(X/C1)=0.33*0.33*0.33*0.33*0.66*0.33*1*0.33*0. (0975 – 8887) Volume 139 8887) Volume 139 – No.7, April 2016.
66*0.66*0.33*0.33*0.66=2.66
4. Swati a
and Rajinder Kaur, nd Rajinder Kaur, Using Factor
P(X/C2)=0.66*0.33*0.66*0.33*0.66*0.33* P(X/C2)=0.66*0.33*0.66*0.33*0.66*0.33* Classification for the Slow Learner Prediction the Slow Learner Prediction
0.33*0.33* 0.33*1*0.33*0.33*0.33=1.330.33*0.33* 0.33*1*0.33*0.33*0.33=1.33 o
over Various Class of Student Dataset,ver Various Class of Student Dataset, Indian
P(X/C3)=0.75*0.75*0.25*0.5*0.25*0.25*0.25*0.5*0.P(X/C3)=0.75*0.75*0.25*0.5*0.25*0.25*0.25*0.5*0.
J
Journal of Science and Technology, ournal of Science and Technology, Vol 9(48),
25*0.25*0.5*0.75*0.25=3.21
DOI: 10.17485/ijst/2016/v9i48/103651, December OI: 10.17485/ijst/2016/v9i48/103651, December
D
P(X/C1)*P(C1)=2.66*0.3=0.798
2016, ISSN (Online): 09740974-564.
P(X/C2)*P(C2)=1.33*0.3=0.399
P(X/C3)*P(C3)=3.21*0.4=1.284 5. Swati and Rajinder Kaur, Multifactor Naïve Bayes , Multifactor Naïve Bayes
Classification For The Slow Learner Prediction lassification For The Slow Learner Prediction
P(X/C3)*P(C3) gives highest probability so X P(X/C3)*P(C3) gives highest probability so X C
O
belongs to class C3. Over Multicass Student Dataset, ver Multicass Student Dataset, International
Journal on Computational Science & Applications ournal on Computational Science & Applications
J
(
According to Naïve Bayes theorem it is predicted that According to Naïve Bayes theorem it is predicted that (IJCSA) Vol.6, No. 4, August 2016IJCSA) Vol.6, No. 4, August 2016
given tuple X belongs to class C3. Which means that given tuple X belongs to class C3. Which means that 6. Shiwani Rana*, Roopali Garg, Shiwani Rana*, Roopali Garg, Slow Learner
there is highest probability that student is Fast Lerner.there is highest probability that student is Fast Lerner.
Prediction using Multi--Variate Naïve Bayes
Classification lassification A Department of
C
Algorithm, lgorithm,
X. Finding: I Information nformation T UIET, IET, P
U
Technology, echnology,
Panjab anjab
Implementation of Naïve Bayes theorem using C# we Implementation of Naïve Bayes theorem using C# we U
University, Chandigarh, India. 02 December niversity, Chandigarh, India. 02 December
can find out Fast, Slow and Average learners. , Slow and Average learners. 2016.

7. R. Kohavi, “Scaling up the accuracy of Naïve Scaling up the accuracy of Naïve
Bayes classifiers: a decisionayes classifiers: a decision-tree hybrid," Proc.
Conclusion: B
Conference onference
on n
Knowledge nowledge
Naïve bays theorem is implemented using C# to Naïve bays theorem is implemented using C# to International rnational C o K
Discovery and Data Mining (KDD 96), ACM, iscovery and Data Mining (KDD 96), ACM,
determine Slow Learner, Average Lerner and Fast determine Slow Learner, Average Lerner and Fast D
Learner. This application is useful in education Learner. This application is useful in education Aug. 1996, pp. 202-207.
system to categories student according to their system to categories student according to their 8. C. G. Nespereira, E. Elhariri, N. ElC. G. Nespereira, E. Elhariri, N. El-Bendary, A. F.
learning behavior. Proposed application is very user oposed application is very user Vilas, and R. P. D. Redondo, ilas, and R. P. D. Redondo, “Machine learning
V
friendly and applicable for any higher education friendly and applicable for any higher education based classification approach for predicting ased classification approach for predicting
b
sector. It helps teachers to implement different sector. It helps teachers to implement different student’s performance in blended learning,rformance in blended learning,” Proc.
teaching and learning techniques for providing quality teaching and learning techniques for providing quality International Conference on Advanced Intelligent nternational Conference on Advanced Intelligent
I
education to the students. Successful implementation education to the students. Successful implementation S
System and Informatics (AISI 15), Springer, Nov. ystem and Informatics (AISI 15), Springer, Nov.
of this model will improve overall result and learning improve overall result and learning 2015, pp. 47-56.
interest among students.
9. Sudha M, Kumaravel A. Performance comparison Performance comparison

based on attribute selection tools for data mining. ased on attribute selection tools for data mining.
REFERENCES: b
1. Jiawei Han and Micheline Kamber, Jiawei Han and Micheline Kamber, Data Mining Indian Journal of Science and Technology. 2014 Journal of Science and Technology. 2014
Nov; 7(S7):1–5.
Concepts and Techniques ISBNISBN-978-81-312-
0535-8. 10. Weka, University of Waikato, New Zealand, Weka, University of Waikato, New Zealand,
http://www.cs.waikato.ac.nz/ml/weka/ttp://www.cs.waikato.ac.nz/ml/weka/
h
2. Hongbo Du, Data Mining Technique,, Data Mining Technique, ISBN-978-
81-315-1955-4.

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