The objective of this project is to design and implement a machine learning–based system that predicts student academic performance using historical data. The system aims to assist educational institutions in identifying at-risk students and improving academic planning through data-driven insights.
Collect and analyze historical student data such as attendance, internal marks, assignment scores, and demographic details.
Perform data cleaning by handling missing values, removing duplicates, and normalizing numerical attributes for better model accuracy.
Conduct exploratory data analysis (EDA) using visualizations to identify patterns and correlations affecting student performance.
Select appropriate machine learning algorithms such as Linear Regression, Decision Tree, or Random Forest for prediction tasks.
Split the dataset into training and testing sets to evaluate model performance objectively.
Train machine learning models and tune hyperparameters to improve prediction accuracy.
Evaluate models using performance metrics such as accuracy, precision, recall, and mean squared error.
Design a simple user interface where faculty members can input student data and view predicted performance results.
Integrate the trained model with the front-end interface using Python-based frameworks.
Document the system architecture, data flow, and algorithm selection process clearly.
Test the system with sample data and validate prediction results.
Prepare final project documentation, including problem statement, methodology, results, and future scope.
