Machine Learning
Student Performance Predictor
Predict grades from study features.
Project overview
Student Performance Predictor is a mentor-guided ML project designed for university submission. Regression/classification model predicting student outcomes from attendance, study hours, and prior scores. The build uses Python, Pandas, scikit-learn and is structured so you can explain architecture, modules, and test cases during viva. Typical delivery timeline is 3–4 weeks depending on package and college deadlines.
Best suited for: Students working on predictive analytics and applied ML projects
Tech stack
Objectives
- Design and implement a working solution for “Student Performance Predictor” aligned to academic evaluation criteria.
- Demonstrate core features such as Feature analysis, Model training, Score predict.
- Produce documentation (synopsis, report chapters, and PPT) suitable for guide approval.
- Prepare viva-ready explanations for design choices, tech stack, and future enhancements.
Key modules
- Module: Feature analysis
- Module: Model training
- Module: Score predict
- Documentation, deployment notes, and viva Q&A
Key features
- Feature analysis
- Model training
- Score predict
- Charts
Deliverables
- Notebook
- Report
- PPT
Learning outcomes
- Runnable source code with setup guide
- University-style report draft
- Presentation deck (PPT)
- Mentor review checkpoints
