House Price Prediction and Recommender System 🏠📈

This project tackles the task of predicting house prices using machine learning and also includes a basic recommender system for houses based on features. The workflow consists of structured preprocessing, exploratory data analysis (EDA), feature engineering, ensemble modeling, and a content-based recommendation approach.

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📂 Project Structure

House Price Prediction/
├── Datasets/
│   ├── train.csv                # Training dataset
│   ├── test.csv                 # Test dataset for prediction
│   └── sample_submission.csv    # Sample submission format
├── Models/
│   └── best_gbr_model.pkl       # Trained Gradient Boosting Regressor model
├── Notebooks/
│   ├── 01_data_loading_and_eda.ipynb          # Data loading & EDA
│   ├── 02_feature_engineering.ipynb           # Feature engineering pipeline
│   ├── 03_modeling_and_prediction_annotated.ipynb  # Modeling and predictions
│   ├── 04_recommender_system.ipynb            # Recommender system notebook
│   └── submission_*.csv                       # Generated submission files
├── Processed/
│   ├── X_train.csv              # Processed training features
│   └── X_test.csv               # Processed test features
├── submission_ensemble.csv      # Final ensemble predictions
└── submission_gbr.csv           # GBR model predictions

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📊 Models Used

• Gradient Boosting Regressor (GBR) – Trained and saved in best_gbr_model.pkl
• Ensemble Predictions – Combined multiple models for improved accuracy

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🤖 Recommender System

Implemented a content-based recommender system in 04_recommender_system.ipynb, which suggests similar houses based on numerical and categorical features. It uses:

• Feature similarity (cosine distance or Euclidean distance)
• Normalization and vectorization of features
• Nearest neighbor retrieval

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🧰 How to Run

1. Clone the Repository

git clone https://github.com/your-username/house-price-prediction.git
cd house-price-prediction

2. Set Up Environment

pip install -r requirements.txt

3. Run Notebooks

Launch Jupyter or VSCode and open the notebooks in the Notebooks/ directory.

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📦 Requirements

Install dependencies from requirements.txt:

pip install -r requirements.txt

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📜 License

MIT License – free to use with attribution.

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✨ Author

Developed by [Your Name]. Contributions welcome!