Predicting Student Marks Using Linear Regression

Introduction

Student performance prediction is a crucial task in the education sector. It helps educators and institutions identify students who may need additional support to improve their academic performance. One common method of predicting student marks is through linear regression, a statistical technique that identifies the relationship between independent and dependent variables. In this report, we will discuss the existing system of predicting student marks and propose a new system using linear regression for more accurate results.

Problem Statement

The existing system of predicting student marks may not be accurate enough to provide meaningful insights for educators. This can lead to misallocation of resources and ineffective interventions for underperforming students. Therefore, there is a need to improve the prediction accuracy of student marks to enhance the quality of education.

Existing System

The existing system of predicting student marks may rely on simple statistical methods or subjective evaluations by educators. This can lead to biases and inaccuracies in predicting student performance. Additionally, the existing system may not take into account all relevant variables that could impact student marks, such as study habits, attendance, and extracurricular activities.

Disadvantages

Some disadvantages of the existing system of predicting student marks include:
1. Lack of accuracy in predictions
2. Biases in evaluation
3. Inability to consider all relevant variables
4. Limited scope for improvement

Proposed System

Our proposed system aims to improve the accuracy of predicting student marks using linear regression. This statistical technique will allow us to analyze the relationship between various independent variables, such as study hours, attendance, and quiz scores, and the dependent variable of student marks. By incorporating all relevant variables into the prediction model, we can generate more accurate and reliable predictions for student performance.

Conclusion

In conclusion, predicting student marks using linear regression offers a more sophisticated and accurate approach compared to the existing system. By analyzing the relationship between independent and dependent variables, educators and institutions can make data-driven decisions to enhance student performance. This proposed system has the potential to revolutionize the way student marks are predicted and improve the overall quality of education delivery.