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STUDENT PERFORMANCE ANALYSIS USING DECISION TREE

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Akshay Jain
Akshay Jain

This document describes a student performance analysis project that uses decision trees. It introduces decision trees and their use for classification problems. The project aims to use decision tree methodology to analyze student performance data, including attendance, test scores, seminar marks, and assignment marks to predict exam performance. It discusses the existing manual system and proposes a computerized system using decision tree induction. The key modules described are the calling class for data insertion, binary nodes to represent attribute values, and the decision tree module to build the tree from training data and classify new data.

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STUDENT PERFORMANCE ANALYSIS USING DECISION TREE MAJOR PROJECT MADE BY: ABHISHEK SHARMA(991110341 F7)
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.
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
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.
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.
MODULES  Calling Class  Binary Node  Decision Tree
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.
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.
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
HARDWARE REQUIREMENTS:  SYSTEM : Pentium IV 2.4 GHz  HARD DISK : 40 GB  MONITOR : 15 VGA color  MOUSE : Logitech.  RAM : 256 MB
SOFTWARE REQUIREMENTS  Operating system : Windows XP Professional and Above  Front End : JAVA
THANK YOU!!!

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STUDENT PERFORMANCE ANALYSIS USING DECISION TREE

  • 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