Early in 2017, the NYC Taxi and Limousine Commission (TLC) released a dataset about Uber's ridership between September 2014 and August 2015. The data contains features distinct from those in the set previously released and throughly explored by FiveThirtyEight and the Kaggle community. Check the Jupyter Notebook in this repository to see the contents of the data.
This project aims to:
- visualize Uber's ridership growth in NYC during the period
- characterize the demand based on identified patterns in the time series
- estimate the value of the NYC market for Uber, and its revenue growth
- other insights about the usage of the service
- attempt to predict the demand's growth beyond 2015 [IN PROGRESS]
The code is written in a Jupyter Notebook with a Python 2.7 kernel, and in addition it requires the following packages:
- Numpy (version: 1.11.2)
- Pandas (version: 0.19.2)
- Matplotlib (version: 1.5.3)
- Seaborn (version: 0.6.0)