House Price Prediction using TensorFlow Decision Forests

This project predicts house prices using the House Prices: Advanced Regression Techniques dataset from Kaggle. It leverages TensorFlow Decision Forests (TF-DF) to build a Random Forest regression model, providing accurate predictions and insights into the factors influencing house prices.

---

📂 Project Structure

• data/
  Contains the training and test datasets used for model development.
• house-prices-prediction-using-tfdf.ipynb
  Jupyter Notebook with data preprocessing, model training, evaluation, and predictions.
• README.md
  This file with an overview of the project.
• submission.csv
  File containing the final predictions for Kaggle submission.

---

🚀 Features

• Data Preprocessing:
  • Handle mixed data types (numerical and categorical).
  • Train-test split (70-30).
• Model Development:
  • Built using TensorFlow Decision Forests (Random Forest).
  • Out-of-Bag (OOB) evaluation for performance monitoring.
• Evaluation Metrics:
  • RMSE (Root Mean Squared Error).
  • Feature importance analysis.
• Visualization:
  • Plots for feature distributions and evaluation metrics.

---

📊 Dataset

• Source: Kaggle - House Prices: Advanced Regression Techniques
• Training Data: 1460 houses with 79 features.
• Test Data: 1459 houses with missing sale prices.
• Target Variable: SalePrice (house sale price).

Example Features:

• Numerical: LotArea, GrLivArea, YearBuilt.
• Categorical: MSZoning, Neighborhood, HouseStyle.

---

🛠️ Tools and Technologies

• Framework: TensorFlow Decision Forests (TF-DF)
• Language: Python
• Libraries:
  • Pandas for data manipulation.
  • Matplotlib for visualizations.
  • Numpy for numerical computations.

---

⚙️ How to Run the Project

1. Clone the repository:

   git clone https://github.com/BandaAkshith/House-Prices-Prediction-using-TFDF.git

2. Navigate to the project directory:

   cd House-Price-Prediction-using-TFDF

3. Install the required libraries:

   pip install -r requirements.txt

4. Open the Jupyter Notebook:

   jupyter notebook house-prices-prediction-using-tfdf.ipynb

5. Follow the steps in the notebook to load data, train the model, and make predictions.

---

📝 Results

• Achieved RMSE of <add your result> on the validation set.
• Feature importance analysis highlighted OverallQual, GrLivArea, and GarageCars as top contributors to house prices.

---

📌 Future Improvements

• Experiment with additional models (e.g., Gradient Boosting, XGBoost).
• Optimize hyperparameters for improved accuracy.
• Explore additional feature engineering techniques.

---

🤝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request for enhancements or bug fixes.

---

🏆 Acknowledgments

• Kaggle Dataset for the data.
• TensorFlow Decision Forests team for their fantastic library.