Canadian Crash Accidents Prediction

Explore the docs »
View Demo · Report Bug · Request Feature

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
3. Roadmap
4. Contributing
5. Contact
6. Acknowledgments

About The Project

This is the second part of the final project for our Machine Learning course at CUNEF, Madrid. The goal was to productize and deploy (or at least simulate it) the model developed at the first part. There are two main parts in this project.

• Back-End: Dockerizing the model, and serving the model via Flask
• Front-End: Building the user interface that interacts with the above APIs

User Interface Demo

Built With

• Docker
• VSCode
• Python 3.8.12
• Flask

(back to top)

Getting Started

Prerequisites

• Python 3.8.12
• Wanting to learn something new (just like we did! and still do)

Roadmap

• Docker
  • Train the Model
  • Generate Predictions
• Flask
  • Build the Flask app
  • Creatnig the UI with HTML and CSS
• Deploy model to Cloud

See the open issues for a full list of proposed features (and known issues).

(back to top)

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

(back to top)

Contact

Andrés Mahía Morado - [email protected]

Project Link: https://github.com/AMM53/accident_prediction

(back to top)

Acknowledgments

• Antonio Tello Gómez (@destyo) for working as hard on this project as I did

(back to top)