Students Marks Prediction Using Linear Regression Project

Abstract:

Analyzing and predicting academic performance is important for any educational institution. Predicting student performance can help teachers to take steps in developing strategies for improving performance at early stages. With the advancement of machine learning and supervised and unsupervised techniques developing these kinds of applications are helping teachers to analyze students in a better way compared to existing methods. In this student marks prediction using Linear regression project students’ academic performance is predicted considering input as previous students’ marks and predicting next subject marks and the accuracy of the model is calculated.

Problem statement:

Analyzing and prediction of marks for students was done based on guesses and students’ personal marks details are not considered for academic evaluation.

Objective:

Machine learning-based data mining techniques are used to automate the process of student performance prediction using linear regression techniques.

Existing system:

• Researchers have done work on Grading systems in which final examination marks are used for giving grades to students and evaluation of each student is done.
• Association rule mining and apriori algorithms are used for classifying students based on their marks

Disadvantages:

• Most of these methods work on data mining techniques that are based on complete data.
• Early-stage evaluation is not possible in these methods.

Proposed system:

• Students’ marks in other subjects are taken as input for the evaluation of students’ performance. The data set is pre-processed and features and labels are extracted from the dataset then the dataset is split into test and train sets then linear regression is applied to the dataset for prediction.

Advantages:

• Before the final marks of all subjects are evaluated prediction can be performed.
• Using a machine learning process automation of marks prediction can be done.

SOFTWARE REQUIREMENTS:

• Operating system: Windows XP/7/10
• Coding Language:            python 
• Development environment: anaconda, Jupiter
• Dataset: students mark the dataset
• IDE :           Jupiter notebook