- Visualize Uber's ridership growth in NYC in 2014 and 2018.
- Visualize pickup coordinates in a real-time map.
- Characterize the demand based on the identified data from the dataset.
- Estimate the predicted fare using the extracted features from the dataset using two ML Models.
- Compare UBER and LYFT on the basis of the number of service cars available and the number of customers in each service.
REQUIREMENTS:
The code is written in a Jupyter Notebook with a Python 3.9 kernel and in addition, it requires the following packages:
SNIPPETS FROM THE NOTEBOOK:
PICKUP LOCATIONS of UBER CUSTOMERS
Heat Map of UBER RIDES