In this notebook, we'll build a neural network from scratch and use it to predict daily bike rental ridership from a dataset that contains the hourly count of rental bikes from January 1, 2011, to December 31, 2012, belonging to Capital Bikeshare system with the corresponding weather and seasonal information.
You can also read this notebook in Español.
If you have Conda installed on your local machine, download the entire folder of the project to your computer and then open your terminal and navigate to its location.
Then run
conda env create -f environment.yaml
This will create a new environment with the same name listed in environment.yaml.
Once you have the environment created, use source activate predicting-bike-sharing-data to enter it on OSX/Linux. On Windows, use activate predicting-bike-sharing-data.
Now run
jupyter notebook
Finally, open your browser and visit localhost:8888 (or the port indicated in your terminal), and you should see all of the contents of the project. You can now open the notebook.
If you don't have Conda, a requirements.txt file is provided to install all of the necessary packages using pip.
This project is licensed under MIT License - see LICENSE for more details.