A data-driven model for predicting the rental price of residential properties based on historical rental data and property attributes.
- Introduction
- Problem Statement
- Getting Started
- Usage
- Model Training
- Model Deployment
- Results
- Contributing
- License
- Acknowledgements
In the real estate industry, accurate rent predictions play a crucial role in assisting property owners, tenants, and property management companies in making informed decisions. This project aims to develop a predictive model that estimates the rental price of residential properties using a dataset containing historical rental information and property attributes.
The main goal of this project is to build a machine learning model that accurately predicts the rental price of residential properties based on relevant features. The model is trained on the provided training dataset, House_rent_train.csv. The resulting model is saved as rent_predictor.pkl for future use.
- Python 3.7 or higher
- Jupyter Notebook (for running
Pravartak_Datathon.ipynb) - Required Python packages (specified in
requirements.txt)
-
Clone this repository to your local machine:
git clone https://github.com/your-username/rental-price-prediction.git
To use the trained rental price prediction model:
Run the Jupyter Notebook Pravartak_Datathon.ipynb to train and evaluate the model.
The trained model will be saved as rent_predictor.pkl.
The process of training the rental price prediction model is documented in the Jupyter Notebook Pravartak_Datathon.ipynb. The notebook covers the following steps:
- Data loading and exploration
- Feature preprocessing and engineering
- Model selection and training
- Model evaluation and performance metrics
Currently, the model is trained and evaluated within the Jupyter Notebook environment. Future work could involve deploying the model as a web application or API using frameworks like Flask or FastAPI.
The results of the trained model, including performance metrics and predictions on the test dataset, are presented in the Jupyter Notebook Pravartak_Datathon.ipynb.
Contributions to this project are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request.
This project is licensed under the MIT License - see the LICENSE file for details.
This project was completed as part of the Pravartak Datathon organized by Pravartak and GUVI. Special thanks to the organizers, mentors, and contributors.