Description

This focuses on the development and evaluation of machine learning models to predict students' academic performance. By leveraging the vast amount of educational data available, these models aim to provide insights into the factors influencing student success and identify early warning signs of underperformance.

Various machine learning algorithms, such as Decision Trees, Random Forests, Support Vector Machines, Neural Networks, and Gradient Boosting, are applied to create predictive models. Features like past academic records, socioeconomic background, study habits, and engagement are incorporated to enhance the model's accuracy.

The dataset used for training and testing the models is collected from diverse educational institutions, covering a wide range of disciplines and educational levels. Rigorous cross-validation techniques are employed to ensure the models' robustness and generalizability.

The ultimate goal is to empower educators and institutions with a predictive tool that can identify struggling students proactively. By early intervention and targeted support, it is possible to enhance students' learning outcomes, improve retention rates, and foster a more personalized and effective educational experience.