https://github.com/ladykerr/msft-build-copilot-demo

Data exploration with GitHub Copilot
https://github.com/ladykerr/msft-build-copilot-demo

flask-api githubcopilot jupyter-notebook logistic-regression

Last synced: about 1 month ago
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Data exploration with GitHub Copilot

• Host: GitHub
• URL: https://github.com/ladykerr/msft-build-copilot-demo
• Owner: LadyKerr
• License: mit
• Created: 2024-05-09T16:09:21.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-21T05:22:45.000Z (7 months ago)
• Last Synced: 2024-05-21T20:19:25.104Z (7 months ago)
• Topics: flask-api, githubcopilot, jupyter-notebook, logistic-regression
• Language: Jupyter Notebook
• Homepage: https://gh.io/didyouknow
• Size: 4.38 MB
• Stars: 0
• Watchers: 1
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Flight Delay Prediction ✈️

This repository contains a flight delay prediction application. The application uses a logistic regression model to predict the likelihood of a flight being delayed based on the day of the week and the arrival airport.

## Repository Structure 📚

- [`client/`](https://github.com/LadyKerr/msft-build-copilot-demo/tree/927cb5d8a02a584d81dba89f6bf7eda1667039a3/client): Contains the frontend code for the application, built with React and Material-UI.

- [`server/`](https://github.com/LadyKerr/msft-build-copilot-demo/tree/927cb5d8a02a584d81dba89f6bf7eda1667039a3/server): Contains the backend code for the application, which serves the prediction API.

- [`data/flights.csv`](https://github.com/LadyKerr/msft-build-copilot-demo/blob/927cb5d8a02a584d81dba89f6bf7eda1667039a3/data/flights.csv): The dataset used for training the model.

- [`manage-flight-data.ipynb`](https://github.com/LadyKerr/msft-build-copilot-demo/blob/927cb5d8a02a584d81dba89f6bf7eda1667039a3/manage-flight-data.ipynb): A Jupyter notebook that contains the data analysis and model training process.

- [`server/model.pkl`](https://github.com/LadyKerr/msft-build-copilot-demo/blob/927cb5d8a02a584d81dba89f6bf7eda1667039a3/server/model.pkl): The trained logistic regression model.

## How to Run ⚒️

1. Start the backend server by running the `server/server.py` script.

2. Start the frontend application by running `npm run dev` in the `client/` directory.

## Features 👀

- The application allows users to select a day of the week and an airport, and then predicts whether a flight will be delayed or not.

- The prediction is made by a logistic regression model trained on the `flights.csv` dataset.

## Contributing 📝

Contributions are welcome! Please feel free to submit a pull request.

## License 🪪

This project is licensed under the terms of the MIT license. See the `LICENSE` file for details.