Introduction

Initial Question

1. What are the characteristics of students who achieve high/low grades?

2. Given a new student, how can we predict the mark of the final grade based on the specific characteristics?

Our product is a shiny app that is powered by regression model and capable for predicting student’s performance in secondary school. The regression model is trained on a data set that predicts the student’s final grade.

Data source

https://archive.ics.uci.edu/ml/datasets/Student+Performance

P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.

Data Preprocessing

• Removed attributes due to alot of missing values
• MJob (Mother Job) (40% Missing Value)
• FJob (Father Job) (35% Missing Value)

Feature Selection according to heatmap

Machine Learning -Linear Regression Model

Performance of Training Set

MSE: 2.505773 MAE: 0.9221254 RMSE: 1.582963 R2 0.8320791

Performance of Testing Set

MSE: 2.211841 MAE: 0.9043607 RMSE: 1.487226 R2 0.8320791

Scatter plot of Training and Testing set

Shiny App UI

Link to Shiny App https://jasmeen.shinyapps.io/StudentPerformancePrediction/

Link to Github https://github.com/yousefhosny1/student-performance-shiny-app

Our Experience