dublinBikes

This application provides real-time information about Dublin Bikes stations, including the availability of bikes and stands, and weather forecasts. It uses a K-Nearest Neighbors (KNN) machine learning model to provide predictions for bike availability per station.

Structure

The application is structured as follows:

• app.py: The main Flask application file.
• add_availability.py, add_stations.py, add_weather.py: Scripts to add data to the database.
• get_weather_forecast.py: Script to get weather forecast data.
• data_analytics/: Directory containing data analytics notebooks and scripts.
• models_availability/, models_bike_stands/: Directories containing machine learning models for predicting bike availability and bike stand availability.
• static/, templates/: Directories containing static files and HTML templates for the Flask application.

Setup

1. Clone the repository.
2. Install the required Python packages using pip install -r requirements.txt.
3. Run python add_stations.py, python add_availability.py, and python add_weather.py to populate the database.
4. Run python app.py to start the Flask application.

Usage

• Visit http://localhost:8080 to view the application.
• Use the Journey Planner to plan your bike journey.
• Click on a station to view real-time availability and prediction for the next hour.

Contributing

Contributions are welcome. Please open an issue to discuss your idea or submit a pull request.