Students opting engineering as their disciple is increasing rapidly. But due to various factors and inappropriate primary education in India dropout rates are high.With the help of data mining techniques we can predict the performance of students in terms of grades and dropout for a subject can be predicted. In the proposed system, Naïve Bayes algorithm is used. Based on the rules obtained from the developed technique, the system can derive the key factors influencing student performance.

1. ANALYSIS OF STUDENTS ACADEMIC
BEHAVIOUR BY DATA MINING’S
CLASSIFICATION METHOD
Shiwani Suryavanshi
Under the Guidance of,
Mr.Sushant S. Bahekar
S.S.B.T.COET.Jalgaon
June 10, 2017

2. Abstract Proposed System Solution To The Problem Methodology Advantages Conclusion
Abstract
Students opting engineering as their disciple is increasing
rapidly. But due to various factors and inappropriate primary
education in India dropout rates are high.
With the help of data mining techniques we can predict
the performance of students in terms of grades and
dropout for a subject.
There are various data mining methods such as classification.
Classification is supervised learning method that builds a
model to classify a data item into a particular class
label.The aim of classification is to predict the future
outcome based on the current available data.
Shiwani Suryavanshi Under the Guidance of, Mr.Sushant S. Bahekar S.S.B.T.COET.Jalgaon
ANALYSIS OF STUDENTS ACADEMIC BEHAVIOUR BY DATA MINING’S CLASSIFICATION METHOD

3. Abstract Proposed System Solution To The Problem Methodology Advantages Conclusion
We have the following four goals concerned with Educational
Data Mining (EDM):
Studying the factors affecting Engineering Education
System.
Providing the Scientific knowledge parameters to the
Educational Institutes
Shiwani Suryavanshi Under the Guidance of, Mr.Sushant S. Bahekar S.S.B.T.COET.Jalgaon
ANALYSIS OF STUDENTS ACADEMIC BEHAVIOUR BY DATA MINING’S CLASSIFICATION METHOD

4. Abstract Proposed System Solution To The Problem Methodology Advantages Conclusion
Problem In Existing Environment
Probablistically Prediction of student Whether he will
become an engineer or not during his academic course by
using several attributes is found difficult.
Increasing drop rates in Engineering indicates Education
System failure which is found to be crucial problem.
Shiwani Suryavanshi Under the Guidance of, Mr.Sushant S. Bahekar S.S.B.T.COET.Jalgaon
ANALYSIS OF STUDENTS ACADEMIC BEHAVIOUR BY DATA MINING’S CLASSIFICATION METHOD

5. Abstract Proposed System Solution To The Problem Methodology Advantages Conclusion
Solution Of The Problem
Developement of an data mining tool which could be helpful
in finding the Probablistic Analysis of student whether he will
become an engineer or not using various Classification
algorithms(Naive Bayes).
Using Probablity theory it will be analogically helpful to
calculate of several factors.
This help us to get insight of statistical data and reach to
meaningful conclusion.
Shiwani Suryavanshi Under the Guidance of, Mr.Sushant S. Bahekar S.S.B.T.COET.Jalgaon
ANALYSIS OF STUDENTS ACADEMIC BEHAVIOUR BY DATA MINING’S CLASSIFICATION METHOD

6. Abstract Proposed System Solution To The Problem Methodology Advantages Conclusion
Methodology
Classification Techniques such as Naive-Bayes Classification
Algorithm is used.
Shiwani Suryavanshi Under the Guidance of, Mr.Sushant S. Bahekar S.S.B.T.COET.Jalgaon
ANALYSIS OF STUDENTS ACADEMIC BEHAVIOUR BY DATA MINING’S CLASSIFICATION METHOD

7. Abstract Proposed System Solution To The Problem Methodology Advantages Conclusion
Advantages
Institute will be able to know Student academic
performance from time to time by this implementation.
It will also be helpful for teachers as well as parents To
improve the weak point of the student.
It will help to Improve the Result of student and Colleges.
Shiwani Suryavanshi Under the Guidance of, Mr.Sushant S. Bahekar S.S.B.T.COET.Jalgaon
ANALYSIS OF STUDENTS ACADEMIC BEHAVIOUR BY DATA MINING’S CLASSIFICATION METHOD

8. Abstract Proposed System Solution To The Problem Methodology Advantages Conclusion
Conclusion
Student dropout rates in India are high.
Data mining techniques can be able to estimate the
performance of students using Classification Techniques.
We Implemented almost semi-accurate executable which leads
to meaningful conclusion regarding the context of student
whether he/she will become an engineer or not.
Naive-Bayes Classification Algorithm is found to be the
Accurate Classification methods among which we have chosen.
Shiwani Suryavanshi Under the Guidance of, Mr.Sushant S. Bahekar S.S.B.T.COET.Jalgaon
ANALYSIS OF STUDENTS ACADEMIC BEHAVIOUR BY DATA MINING’S CLASSIFICATION METHOD