Predicting students performance in exams using machine learning classifiers : Logistic regression, KNN and SVM. Extraction of factors impacting students' performances.

This project understands how the student's performance (test scores) is affected by other variables such as Gender, Ethnicity, Parental level of education, Lunch and Test preparation course

Predict Students Dropout and Academic Success Using Machine Learning Algorithms

TDT4173 project

Analyse performance of students on the basis of their personal life style, studying style, family related, educational environment satisfaction, students grades using ML model

🎓 Leveraging for containerization and CI/CD pipelines. It includes code and configuration files for seamless development and deployment processes.

To understand the how the student's performance (test scores) is affected by the other variables (Gender, Ethnicity, Parental level of education, Lunch, Test preparation course).

Application for university teachers, students or administration, which will help them to check and monitor the academic performance of students, student groups etc. with dashboard of reports and infographics.