Machine Learning
House Price Prediction
Regression on housing features.
Project overview
House Price Prediction is a mentor-guided ML project designed for university submission. Predict house prices using location, size, and amenity features with error metrics and residual plots. The build uses Python, scikit-learn, Matplotlib and is structured so you can explain architecture, modules, and test cases during viva. Typical delivery timeline is 4–6 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 “House Price Prediction” aligned to academic evaluation criteria.
- Demonstrate core features such as EDA, Feature scaling, Model compare.
- 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: EDA
- Module: Feature scaling
- Module: Model compare
- Documentation, deployment notes, and viva Q&A
Key features
- EDA
- Feature scaling
- Model compare
- Prediction form
Deliverables
- Notebook
- Report
- PPT
Learning outcomes
- Runnable source code with setup guide
- University-style report draft
- Presentation deck (PPT)
- Mentor review checkpoints
