WE ARE IMPLEMENTING DECISION TREE IN JAVA FOR STUDENT PERFORMANCE ANALYSIS

1. STUDENT PERFORMANCE ANALYSIS
USING DECISION TREE
MAJOR PROJECT
MADE BY:
ABHISHEK SHARMA(991110341
F7)

2. What is Decision Tree?
A schematic tree-shaped diagram used to clarify
and find an answer to a complex problem. The
structure allows users to take a problem with
multiple possible solutions and display it in a
simple, easy-to-understand format that shows the
relationship between different events or
decisions. The furthest branches on the tree
represent possible end results.

3. INTRODUCTION
 Classification is a classical problem in machine learning
and data mining. Given a set of training data tuples, each
having a class label and being represented by a feature
vector, the task is to algorithmically build a model that
predicts the class label of an unseen test tuple based on
the tuple’s feature vector. One of the most popular
classification models is the decision tree model.
 The main objective of this project is to use data mining
methodologies to study student’s performance in the
courses. Data mining provides many tasks that could be
used to study the student performance. The classification
task is used to evaluate student’s performance and as
there are many approaches that are used for data
classification, the decision tree method is used here.
Information’s like Attendance, Class test, Seminar and
Assignment marks were collected from the student’s
management system, to predict the performance at the

4. EXISTING SYSTEM:
 The current system is maintaining manually
 Manual maintenance of records involves burden
and it is quite tedious task. In general existing
system there is no security.
 If any record missed it is very difficult to retrieve
the classifier tree.
 DRAWBACKS
 Good security is not provided by the existing
system.
 It is very time taking.
 The complexity increases tending to a very high
probability of error.

5. PROPOSED SYSTEM
 The proposed system is computerized to provide
greater easiness to the users of the system.
 In this system we use decision tree induction
method.
 The system is constructed in an object oriented
trend, thinking in an abstract way considering all the
involvement as objects.
ADVANTAGES
 Good security is provided by the existing system.
 Security measures are taken to avoid mishandling of
database.
 It minimizes the man power.

6. MODULES
 Calling Class
 Binary Node
 Decision Tree

7. CALLING CLASS
Data Insertion
In many applications, however, data uncertainty
is common. The value of a feature/attribute is
thus best captured not by a single point value, but
by a range of values giving rise to a probability
distribution. With uncertainty, the value of a data
item is often represented not by one single value,
but by multiple values forming a probability
distribution. This uncertain data is inserted by
user.

8. BINARY NODE
Since, the system is constructed in an object
oriented trend, considering all the involvement as
objects. Hence, representation of each particular
value ,classes of the each attribute in form of
node is necessary in order to create the decision
tree.

9. DECISION TREE
 The Decision tree is built by passing the training
set(sample data) containing record of a certain
no. of students whose performance is to be
analyzed by taking in consideration attributes like
psm(previous semester marks,attendence etc).
 The data is then classified by calculating the
entropy of each particular attribute and further
decision tree is built.
 The Entropy formulae used is:
Entropy = - p j log2 p j

10. HARDWARE REQUIREMENTS:
 SYSTEM :
Pentium IV 2.4 GHz
 HARD DISK : 40 GB
 MONITOR : 15 VGA
color
 MOUSE :
Logitech.
 RAM : 256
MB

11. SOFTWARE
REQUIREMENTS
 Operating system : Windows XP
Professional
and Above
 Front End : JAVA

12. THANK
YOU!!!