This project focuses on building an end-to-end machine learning solution to predict residential property prices using structured real estate data. The objective is to develop a statistically sound, business-relevant regression model and prepare it for production deployment via an API (Flask/FastAPI).
The current implementation achieves:
- R² score ~ 0.87 on test data
- RMSE ~ 28% (relative to average property price)
These metrics indicate strong explanatory power and reasonable prediction error for a real-world real estate pricing use case, where noise and location-driven variance are inherently high.
Accurately estimating house prices is critical for:
- Buyers and sellers making informed decisions
- Real estate platforms providing price recommendations
- Financial institutions performing risk assessment
The goal is to predict the market price of a house based on a combination of structural, geographical, and categorical features.
The dataset consists of residential property records with both numerical and categorical attributes.
- Bedrooms
- Bathrooms
- House size / built-up area
- City
- State (highly influential geographical feature)
- Additional engineered features derived during preprocessing
- Price of the property
Significant effort was invested in preparing the data to meet linear regression assumptions and improve model stability.
- Handling missing values
- Outlier detection and treatment using IQR-based methods
- Log transformation of skewed target values (price)
- Encoding categorical variables (city, state)
- Feature scaling using StandardScaler (fit on training data only)
- Train-test split to ensure unbiased evaluation
Note: Feature engineering and EDA were intentionally separated from model serialization to maintain clean production artifacts.
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Linear Regression was selected as the primary model due to:
- Interpretability
- Business explainability
- Strong baseline performance
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Multicollinearity was analyzed using Variance Inflation Factor (VIF)
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Regularized models (e.g., Lasso) were evaluated conceptually to address:
- Feature selection
- Overfitting risk
| Metric | Value | Interpretation |
|---|---|---|
| R² (Train) | ~0.876 | Good learning of underlying patterns |
| R² (Test) | ~0.87 | Strong generalization, minimal overfitting |
| RMSE | ~28% | Acceptable error range for real estate pricing |
- No significant overfitting (train and test R² closely aligned)
- Low bias, controlled variance
- Performance is consistent across major geographies
The trained model and preprocessing objects (scaler, encoders) are serialized using pickle to ensure:
- Reproducibility
- Consistency between training and inference
- Seamless deployment
Artifacts saved:
- Trained regression model
- Feature scaler
- Encoders / feature mappings
- FastAPI / Flask for serving predictions
- REST endpoint accepting JSON input features
- Input validation and preprocessing pipeline
- Client sends property features
- API applies same preprocessing steps
- Pickle-loaded model generates prediction
- Price prediction returned as JSON response
- Local development server
- Cloud deployment (Docker-ready)
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## Business Interpretation
* Location (state and city) plays a dominant role in pricing
* Structural features (size, bedrooms, bathrooms) drive incremental value
* The model provides **explainable predictions**, suitable for stakeholder-facing applications
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## Conclusion
This project demonstrates a complete machine learning lifecycle—from raw data to deployable model—applied to a realistic real estate pricing problem. With an R² of ~0.87 and controlled error levels, the model is production-viable and provides meaningful business value while maintaining interpretability.
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## Author
**Aasim**
Machine Learning & Data Science Practitioner