Welcome to the repository of Immo-Eliza-Deployment, a project that showcases the deployment of a real estate price prediction model!
- About The Project 📘
- Built With 🛠️
- Getting Started 🏁
- Usage 🚀
- Contributing 🤝
- License 📜
- Contact 📧
- Acknowledgements 🎉
👋 Hi, I'm a passionate data scientist from BeCode, an intensive data science bootcamp. This project is a part of my learning journey, where I focus on doing rather than just theorizing.
The goal was to deploy a Random Forest Regression model, crafted with scikit-learn, into a working API using FastAPI and integrate it with a Streamlit web application. This allows both technical and non-technical users to interact with the model and get real estate price predictions.
After 5 days of hard work, the result is a seamless connection between the frontend and backend, opening up API access to peers and providing a user-friendly web app for clients.
The model predictions are not perfect and should not be taken as professional appraisals.
- FastAPI
- Streamlit
- scikit-learn
To get a local copy up and running follow these simple steps.
- Python 3.8+
- pip
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Clone the repo and go to the project directory
git clone -b local-deployment --single-branch git@github.com:Danil-Zhuravlov/immo-eliza-deployment.git cd immo-eliza-deployment -
Create a virtual environment
python -m venv venv
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Install the required packages
pip install -r requirements.txt
You can check out the deployed app here. The api documentation is available here.
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Run the Streamlit app
streamlit run streamlit/app.py
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Run the FastAPI app
cd app uvicorn main:app --reload -
On the Streamlit app, input the property features and get the price prediction!
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On the FastAPI app, you can access the API documentation HERE
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
Distributed under the MIT License. See License for more information.
Danil Zhuravlov - LinkedIn