Unlocking Students Potential: A Predictive Model to Enhance Educational Performance Prediction

Authors- Assistant Professor Dr.Pandya Vishal Kishorchandra, Dr. Pandya Rajnikant

Abstract-This study highlights important developments in the use of machine learning in educational technology by presenting the creation and implementation of a predictive model for student exam scores. With the help of the Random Forest method, the model was able to handle complex, nonlinear interactions with efficacy and resilience against overfitting. It also demonstrated great accuracy and reliability. The model’s performance was largely attributed to extensive data preparation and feature engineering, highlighting the significance of using domain expertise to convert unstructured data into useful features. With its user-friendly interface and dynamic form generation, the web application created for this project prioritizes ease of use and maintenance. We believe that usability testing, user feedback, multilingual support, and accessibility features will all be beneficial for future improvements. To optimize the model’s impact, a number of important topics were identified for further research. These include broadening the dataset to include a range of demographics and extracurricular activities, stress levels, and other variables, and refining the application’s usability in response to feedback from teachers and students. Other machine learning algorithms that are being investigated include support vector machines and deep learning models. To ensure continued accuracy and usefulness, real-world validation via partnerships with academic institutions will be essential for assessing the application’s efficacy in classroom environments. By going in these directions, the project will become a comprehensive instrument that will advance educational technology and improve educational outcomes dramatically.

DOI: /10.61463/ijset.vol.13.issue1.115