In this project, I built a system to predict student academic outcomes by analyzing key parameters such as attendance, marks, and study hours. Using Python for data processing and MySQL as a backend database, the system allows educational institutions to make informed decisions.

• Languages & Tools: Python, MySQL, Pandas, NumPy, Matplotlib, Scikit-learn
• ML Algorithms: Linear Regression, Decision Tree Regression
• Features: Data cleaning, visualization, prediction model, database integration

• Predictive analytics for student grades based on academic indicators
• Visual representation of student performance trends
• Backend integration with MySQL for secure data storage and retrieval
• Insights into student behavior and risk detection for academic support

The system achieved ~85% accuracy in predicting student grades and proved effective in identifying students needing intervention. This project highlights how combining machine learning and relational databases can support education systems.