Vancouver hourly and daily bike share forecasts

Build GBM and Random Forest trees to predict hourly and daily bike share in downtown Vancouver

Install

This project requires Python 3.0+ and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• seaborn
• scikit-learn
• XGBoost

You will also need to have software installed to run and execute an iPython Notebook

Code

Code is provided in the notebook bike_share_hourly_all_stations.ipynb notebook file.

Run

In a terminal or command window, navigate to the top-level project directory Vancouver-bike-share-forecast/ and run one of the following commands:

ipython notebook bike_share_hourly_all_stations.ipynb jupyter notebook bike_share_hourly_all_stations.ipynb

This will open the iPython Notebook software and project file in your browser.

Data

Data collected from:

• Historical bike rental data from Mobi website
• Statutory holidays: OfficeHolidays
• Weather information from Air Data Archive by BC Government

Project workflow