Students Marks Prediction Using Linear Regression

Abstract:

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

Problem statement:

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

Objective:

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

Existing system:

  • Researches has done work on Grading systems which final examination marks are used for giving grades for 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 which are based on after completing data.
  • Early stage evaluation is not possible in these methods.

Proposed system:

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

Advantages:

  • Before final marks of all subjects are evaluated prediction can be performed.
  • Using 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 marks dataset
  • IDE :           Jupiter notebook

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