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Machine Learning · Mini Project
House Price Prediction
Real-estate price regression with a web form for new listings
1-2 weeks build
Overview
Predicts property prices using the public Bengaluru House Price dataset on Kaggle. The kit cleans messy square-footage values, groups rare locations, removes price-per-square-foot outliers and compares linear, Lasso, random forest and XGBoost regressors. A Flask form takes location, area, bedrooms and bathrooms and returns an estimated price.
What makes it stand up
- 01Cleaning of ranged and mixed-unit area values
- 02Location grouping and price-per-square-foot outlier removal
- 03Four regressors compared with cross-validation
- 04Residual analysis by location in the report
- 05Flask form with a location dropdown for estimates
Module breakdown
- 01
Cleaning
Parses area ranges, units and missing fields.
- 02
Feature work
Location grouping and derived ratios.
- 03
Models
Linear, Lasso, random forest and XGBoost.
- 04
Evaluation
R-squared, MAE and residual plots.
- 05
Web form
Flask page returning an estimated price.
After this, you will be able to
- Explain how outliers distort a regression model
- Defend your location-grouping strategy
- Demo an estimate for a listing the examiner describes
Technology stack
- Python
- Pandas
- scikit-learn
- XGBoost
- Flask
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support