{"id":23881706,"url":"https://github.com/eusha425/housing-market-analysis","last_synced_at":"2026-04-09T21:04:12.893Z","repository":{"id":270673600,"uuid":"910790233","full_name":"Eusha425/housing-market-analysis","owner":"Eusha425","description":"Implementation of supervised learning algorithms for real estate price prediction, featuring Ridge Regression optimization, IQR-based outlier detection, and extensive feature engineering. 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The project demonstrates the complete machine learning lifecycle, from data preprocessing and feature engineering to model evaluation and optimization. This project was developed as part of the KIT315 unit assessment, demonstrating practical application of machine learning concepts in real-world scenarios.\n\n\n## 🔍 Key Features\n- Implementation of 7 different regression algorithms\n- Advanced feature engineering and preprocessing pipeline\n- Robust outlier detection and handling using IQR method\n- Comprehensive model evaluation and comparison\n- Detailed data visualization and analysis\n- Hyperparameter optimization using Grid Search CV\n\n## 🛠️ Technologies Used\n- Python 3.7+\n- Jupyter Notebook\n- Key Libraries:\n  - pandas \u0026 numpy: Data manipulation\n  - scikit-learn: Machine learning algorithms\n  - seaborn \u0026 matplotlib: Data visualization\n  - xgboost: Gradient boosting\n\n## 📈 Models Implemented\n1. Linear Regression\n2. Ridge Regression\n3. Decision Tree Regressor\n4. Random Forest Regressor\n5. Gradient Boosting Regressor\n6. Kernel Ridge Regression\n7. K-Nearest Neighbors Regressor\n8. XGBoost Regressor\n\n## 🔄 Project Pipeline\n\n### Data Preprocessing\n- Missing value detection and imputation\n- Outlier removal using IQR method\n- Feature scaling with StandardScaler\n- Categorical variable encoding\n\n### Feature Engineering\n- Created 'area_per_bedroom' to capture house spaciousness\n- Log transformation of 'area' for normal distribution\n- One-hot encoding for categorical variables\n\n### Model Development \u0026 Evaluation\n- Cross-validation with 15 folds\n- Grid Search for hyperparameter optimization\n- Performance metrics:\n  - Mean Absolute Error (MAE)\n  - Mean Squared Error (MSE)\n  - R-squared (R²)\n\n## 📊 Results\nRidge Regression emerged as the optimal model, demonstrating:\n- Superior R-squared score\n- Consistent performance across price ranges\n- Robust handling of feature multicollinearity\n\n## 📁 Project Structure\n```\nhousing-market-analysis/\n│\n├── Housing_Price_Prediction.ipynb   # Main Jupyter notebook\n├── README.md                        # Project documentation\n└── data/                           \n    └── Housing.csv                  # Dataset file\n```\n\n## 🚀 Setup and Usage\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/Eusha425/housing-market-analysis.git\n   ```\n\n2. Install required packages:\n   ```python\n   import pandas as pd\n   import numpy as np\n   import seaborn as sns\n   import matplotlib.pyplot as plt\n   from sklearn.model_selection import train_test_split, GridSearchCV\n   from sklearn.preprocessing import StandardScaler\n   import xgboost as xgb\n   ```\n\n3. Open and run the Jupyter notebook:\n   ```bash\n   jupyter notebook Housing_Price_Prediction.ipynb\n   ```\n\n## 📈 Future Improvements\n1. **Data Processing**\n   - KNN imputation for missing values\n   - Advanced outlier detection methods\n\n2. **Feature Engineering**\n   - Recursive Feature Elimination (RFE)\n   - Domain-specific feature creation\n\n3. **Model Optimization**\n   - Bayesian optimization for hyperparameter tuning\n   - Ensemble methods exploration\n\n4. **Evaluation**\n   - Implementation of MAPE\n   - Addition of adjusted R²\n\n## 📚 References\n1. Pedregosa et al. (2011) - Scikit-learn: Machine Learning in Python\n2. Guyon \u0026 Elisseeff (2003) - Variable and Feature Selection\n3. Bergstra \u0026 Bengio (2012) - Random Search for Hyper-Parameter Optimization\n4. Dietterich (2000) - Ensemble Methods in Machine Learning\n5. Kohavi (1995) - Cross-Validation and Model Selection\n\n## 🤝 Contributing\nFeel free to fork the project and submit pull requests. For major changes, please open an issue first to discuss the proposed changes.\n\n## 📄 License\nThis project is licensed under the MIT License - see the [LICENSE](https://github.com/Eusha425/housing-market-analysis/blob/main/LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feusha425%2Fhousing-market-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feusha425%2Fhousing-market-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feusha425%2Fhousing-market-analysis/lists"}