End-to-end machine learning project for house price prediction using advanced EDA, feature engineering, and regularization techniques. Compared Ridge and Lasso regression with hyperparameter tuning to handle multicollinearity and improve accuracy. Includes business insights for real estate investment decisions.
This project builds an end-to-end machine learning pipeline to predict house prices using advanced data analysis, feature engineering, and regularization techniques. The goal is to help real estate companies identify undervalued properties and maximize profit through data-driven decisions.
A US-based company (Surprise Housing) aims to enter the Australian housing market. The objective is to:
- Predict the actual value of houses
- Identify undervalued properties
- Optimize buy β renovate β sell strategy
-
Housing dataset with multiple features such as:
- Property size
- Quality
- Basement area
- Garage capacity
- Year built, etc.
- Python π
- Pandas, NumPy
- Matplotlib, Seaborn
- Scikit-learn
- Distribution analysis (SalePrice)
- Correlation heatmaps
- Outlier detection (Z-score, IQR)
- Feature relationships (scatter, box plots)
-
Missing value treatment
-
Feature engineering:
- Age of house
- Has basement
- Large house indicator
-
Encoding categorical variables
-
Log transformation of target variable
- Feature selection
- Sparse model
- Handles multicollinearity
- Stable predictions
- GridSearchCV used to find optimal alpha (Ξ»)
- Cross-validation (5-fold)
- Mean Absolute Error (MAE)
- RΒ² Score
- Train vs Test comparison
| Model | MAE | Performance |
|---|---|---|
| Ridge | β Lower | Best |
| Lasso | β Higher | Feature selection |
π Ridge Regression outperformed Lasso due to high feature correlation.
- OverallQual is the most important feature
- GrLivArea strongly impacts price
- Garage & Basement add significant value
- Quality improvements β highest ROI
- Buy medium-quality houses
- Improve quality & functionality
- Sell at premium pricing
βValue in real estate comes from improving perceived quality, not just size.β
- Importance of feature engineering
- Handling multicollinearity
- Ridge vs Lasso trade-offs
- Real-world ML pipeline design
- Add location-based features
- Use advanced models (XGBoost, LightGBM)
- Deploy as web app (Streamlit)
Feel free to fork, improve, and contribute!
This project is inspired by real-world business problems in real estate analytics and machine learning.
Chetan Sonigara AI Research Engineer | Autonomous Systems | ML Enthusiast