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
-
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
-
Install the required packages
pip install -r requirements.txt
You can check out the deployed app here. The api documentation is available here.
-
Run the Streamlit app
streamlit run streamlit/app.py
-
Run the FastAPI app
cd app uvicorn main:app --reload
-
On the Streamlit app, input the property features and get the price prediction!
-
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