https://github.com/datenhahn/munich-bicycle-prediction
This project uses opendata of the city of Munich to predict the expected number of cyclists for a given day using the weather forecast and historic data.
https://github.com/datenhahn/munich-bicycle-prediction
Last synced: 4 days ago
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This project uses opendata of the city of Munich to predict the expected number of cyclists for a given day using the weather forecast and historic data.
- Host: GitHub
- URL: https://github.com/datenhahn/munich-bicycle-prediction
- Owner: datenhahn
- Created: 2023-06-20T06:16:53.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-19T16:42:49.000Z (almost 3 years ago)
- Last Synced: 2025-02-23T22:28:16.692Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 5.93 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Science Project - Predicting cyclists in Munich
This project uses opendata of the city of Munich to predict the expected number of cyclists for a given day using the weather forecast and historic data.
Blogpost:
https://medium.com/@jonas.hahn/open-data-predicting-cyclist-traffic-in-munich-4acf1c11c0ed
## Getting Started
To run the webapp via docker execute the following commands:
```
docker-compose up
```
The webapp is then available at http://localhost:8080
## Project Structure
The project is structured as follows:
* `blogpost` : Contains the blogpost as markdown file and the images used in the blogpost on medium.com
* `datasources` : Contains the scripts to download and clean the data.
* `explorations` : Contains the jupyter notebooks used for data exploration and model experimentation.
* `models` : Contains the finalized version of the model training pipeline and the trained model.
* `webapp` : Contains the webapp to predict the number of cyclists for a given day.
## Train the model
To train the model, execute the following command:
```
python3 train_model.py --inputdata ../datasources/munich-bicycle-counting-stations/cleaned/bicycle-counting-station-daily.json
```
## Datasources
Every datasource has its own folder in the `datasources` folder, which contains detailed documentation and scripts or jupyter notebooks to download and clean the data.
* **Munich Bicycle Counting Stations** : "Bicycle counting stations" were established in Munich in 2008 for continuous monitoring of increasing bicycle traffic. This is the main dataset used, you find details in the datasets README
### Munich Bicycle Counting Stations
See the readme file `datasources/munich-bicycle-counting-stations/README.md`