Predicting Student Academic Performance Using Machine Learning Technique

Computers do not learn as well as humans do, but many machine-learning algorithms have been found that are effective for some types of learning tasks. They are especially useful in poorly understood domains where humans might not have the knowledge needed to develop effective knowledge engineering a

Project Title

Predicting Student Academic Performance Using Machine Learning Technique

Project Area of Specialization

Artificial Intelligence

Project Summary

Computers do not learn as well as humans do, but many machine-learning algorithms have been found that are effective for some types of learning tasks. They are especially useful in poorly understood domains where humans might not have the knowledge needed to develop effective knowledge engineering algorithms. Generally, Machine Learning (ML) explores algorithms that reason from externally supplied instances (input set) to produce general hypotheses, which will make predictions about future instances. The externally supplied instances are usually referred to as training set. To induce a hypothesis from a given training set, a learning system needs to make assumptions (biases) about the hypothesis to be learned. A learning system without any assumption cannot generate a useful hypothesis since the number of hypotheses that are consistent with the training set is usually huge. Since every inductive learning algorithm uses some biases, it behaves well in some domains where its biases are appropriate while it performs poorly in other domains.

The ability of prediction of a students performance could be useful in a great number of different ways associated with university-level. Students key demographic characteristics and their marks in a few written assignments can constitute the training set for a supervised machine learning algorithm. The learning algorithm could then be able to predict the performance of new students thus becoming a useful tool for identifying predicted poor performers.

Project Objectives

Estimating academic performance by student according to the dataset. Its provide outcome based on datasets given. This can evaluate and forecast the future performance of students on the basis of their academic records and other major factors influencing them, which is extremely important in order to efficiently enforce the required pedagogical strategies and to identify the weaker zones of students.

Project Implementation Method

1.Data pre-processing

2.Applying propose algorithms for training

Naïve Bayes

Support Vector Machine

K-NN 

Logistic regression

Decision Tree (C4.5)

3.Testing proposed algorithm

4.Testing and Training result

Benefits of the Project

Problem Description

Most of the previous work they can be work on the weka tool to predict the student academic performance because weka it can only handle small datasets. Whenever a set is bigger than a few megabytes an Out Of Memory error occurs. The object of this project is to alter weka in such a way that it can handle all datasets, up until a few gigabytes.

They can predict the student academic performance on the same dataset not to predict the data of new student performance.

High authorities predict students academic performance on the base of past records instead of present performance of the students.

Technical Details of Final Deliverable

Python

Jupyter Notebook

Final Deliverable of the Project

Software System

Core Industry

Education

Other Industries

IT

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Quality Education

Required Resources

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