https://github.com/epiphone/junction2018_weatherall

Our entry for the Junction 2018 hackathon: a tool for predicting city bike station resupply demand
https://github.com/epiphone/junction2018_weatherall

hackathon junction2018

Last synced: about 1 month ago
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Our entry for the Junction 2018 hackathon: a tool for predicting city bike station resupply demand

• Host: GitHub
• URL: https://github.com/epiphone/junction2018_weatherall
• Owner: epiphone
• Created: 2018-11-30T09:56:35.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-01-08T10:42:45.000Z (about 6 years ago)
• Last Synced: 2024-11-05T11:09:51.473Z (3 months ago)
• Topics: hackathon, junction2018
• Language: Jupyter Notebook
• Size: 9 MB
• Stars: 1
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Junction 2018 - Weatherall

Our entry for the Junction 2018 hackathon: an app for predicting city bike resupply demand. Consists of

- A prediction model based on city bike usage statistics, FMI weather data and Telia Crowd Insights data

- Integration to a Vaisala sensor device for real-time weather data

- A map-based UI

## Team

- Fraser Barclay

- Mohamed Karim Bouaziz

- Mikaela Hallenberg

- Aleksi Pekkala

- Katri Tegel

![Screenshot](doc/screen2.png)

## Install

### Setup backend

1. Install `pipenv`

2. Run `pipenv install` to install deps

3. Start app with `pipenv run python app.py` (or open `pipenv shell` and run `python app.py`)

### Setup frontend

1. `cd frontend`

2. `npm install`

3. `npm run serve` -> App will run in http://localhost:8080/

### Setup datascience

#### Jupyter notebook

1. `cd datascience`

2. `pip install -r requirements.txt`

3. `jupyter notebook` -> App will run in http://localhost:8888/