Dublin Bikes Prediction System

This system is a Flask application that provides a REST API for interacting with data related to Dublin Bikes. It enables users to retrieve real-time and historical data about bike stations and availability. Additionally, it uses trained machine learning models to predict the availability of bikes and bike stands at each station.

Note

Replace your Google Maps api key in the HTML file and your AWS RDS credentials in the flask app.

Features

• Retrieve real-time data about all Dublin Bikes stations
• Access historical data for a given station
• Predict bike and bike stand availability at a specific time and date using machine learning
• Current and forecasted weather data

Installation

To run this application locally, you need Python 3 and the ability to install libraries using pip.

1. Clone this repository to your local machine.
2. Navigate to the cloned directory.
3. Install the necessary libraries by running pip install -r requirements.txt.
4. Run the application by running python app.py.
5. Visit localhost:5000 in your browser to interact with the application.

API Endpoints

Here are the primary API endpoints:

• GET /stations: Returns a list of all bike stations.
• GET /availability: Returns the latest availability data for all stations.
• POST /predict_available_bikes/<station_number>: Returns a prediction of the number of available bikes at the given station number.
• POST /predict_available_bike_stands/<station_number>: Returns a prediction of the number of available bike stands at the given station number.
• GET /current_weather: Returns the current weather conditions.
• GET /history: Returns historical availability data for the past six hours.

Built With

• Python
• Flask
• PyMySQL
• scikit-learn

License

This project is licensed under the MIT License.