Abstract : Our project introduces a comprehensive Performance Evaluation System designed to anticipate a student's academic success and learning style based on a set of input features. Leveraging advanced data analysis techniques and machine learning algorithms, this system considers factors such as previous test scores, parental education levels, and additional relevant attributes to predict a student's future academic performance. Furthermore, our system goes beyond conventional performance prediction by also identifying if a student falls into the category of "slow learner." By discerning learning characteristics, we can provide tailored support and guidance, ultimately contributing to improved educational outcomes. This project is poised to empower educators and institutions with data-driven insights, helping them better understand and support students on their unique academic journeys
- AKOLAWALA MUSTAFA HUSEIN [ Team Leader ]
- HASAN SHADAAB NAJMUL
- MASTIM AYAN AHMED QASID
- KHAN HAMZA GULREIZ
- PROF. MOHD ASHFAQUE SHAIKH [ Primary Guide ]
Please follow the below steps to run this project.
- clone the repository
git clone https://github.com/MustafaAkolawala/Performance-evaluation-system.git - install the required packages
pip install -r requirements.txt - run the
app.pypython script - once the server is running, go on your browser on this link
http://localhost:5000 - follow the steps displayed and run the program accordingly
- Class : TE (COMP) Div A - 2023-2024
- Subject : Mini Project : 2A (MP2A(P)(2019))
- Project Type : Mini Project
- Kaggle - Students Performance in Exams. It contains information about students' demographics, parental education, lunch type, test preparation course, and their corresponding math scores.
- https://kaggle.com/dataset1
- IEEE standard journal paper
Mehil Shah, Yogesh Gupta , "Student Performance Assessment and Prediction System" using Machine Learning