• E.V.R.J Sastry
• Hema Sandhyana Papolu
• Kusumanchi Jaya Surya
• Makavarapu Divya Sai Ganesh
• Chekuri Likith

This paper introduces Learn Mate, a web-based application developed to help engineering students optimize their study time allocation and predict their academic performance. The application integrates advanced Multi-Criteria Decision Making (MCDM) techniques, which assist students in effectively distributing their study time based on various factors, including the importance of subjects, personal efficiency levels, and individual problem-solving capabilities. These factors are critical in determining how to prioritize study sessions to maximize learning outcomes. In addition to the MCDM framework, Learn Mate incorporates a machine learning (ML) model that forecasts students' academic performance. By analyzing data inputs such as study hours, class attendance, participation in extracurricular activities, and other relevant academic behaviors, the ML model predicts how well students are likely to perform in their courses. This predictive capability serves as an early indicator for students, helping them take proactive steps to improve their learning strategies. The application is built using Streamlit, a popular Python-based framework, for creating an interactive and user-friendly front-end interface. PostgreSQL is employed for efficient database management, ensuring smooth data storage and retrieval. The core functionalities, including MCDM and ML algorithms, are implemented using Python, leveraging its robust ecosystem of libraries for data analysis and machine learning. Results from early-stage user testing indicate that Learn Mate significantly improves students study habits by offering personalized study time recommendations based on their unique learning patterns and performance prediction. The application not only helps in efficient time management but also provides students with insights into how their current academic behaviors impact their future success. In conclusion, Learn Mate serves as a valuable tool for engineering students, providing data-driven recommendations to optimize study efforts and offering early predictions of academic performance. It demonstrates the potential of combining MCDM and machine learning to address common challenges faced by students in managing their academic workloads effectively. Keywords Learn Mate, Study time allocation, Multi-Criteria Decision Making (MCDM), Machine learning, Academic performance prediction, Engineering students, Time management, Academic behaviors, Personalized recommendations, Streamlit, PostgreSQL, Data-driven recommendations, Performance forecasting.

Learn Mate, Study time allocation, Multi-Criteria Decision Making (MCDM), Machine learning, Academic performance prediction, Engineering students, Time management, Academic behaviors, Personalized recommendations, Streamlit, PostgreSQL, Data-driven recommendations, Performance forecasting.

"LEARN MATE: An Adaptive Learning Platform for Personalized Education", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.j174-j185, March-2025, Available :http://www.jetir.org/papers/JETIR2503925.pdf

"LEARN MATE: An Adaptive Learning Platform for Personalized Education", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppj174-j185, March-2025, Available at : http://www.jetir.org/papers/JETIR2503925.pdf