Machine Learning-Based Academic Performance Forecasting System

To create a predictive analytics system that forecasts students’ future academic performance using historical academic data, behavioral metrics, and study patterns, enabling educators to provide proactive academic support and improve overall student achievement outcomes.

Gather historical academic datasets including semester marks and attendance records.

Clean and preprocess dataset to handle missing values and outliers.

Perform correlation analysis to identify key performance factors.

Implement regression models such as Linear Regression, Ridge Regression, or Neural Networks.

Split dataset into training and testing sets for validation.

Evaluate model performance using RMSE and R-squared metrics.

Develop a web-based interface where users can input student data to get predictions.

Integrate backend ML model with frontend interface.

Visualize predicted vs actual performance using graphical tools.

Optimize model performance through hyperparameter tuning.

Conduct system testing and validation using real-world scenarios.

Document complete workflow, architecture, and evaluation metrics.