This project focuses on selecting the best 5 nominees for ride-sharing based on a set of locations with latitude and longitude information. The goal is to optimize ride-sharing efficiency and minimize travel time for passengers.
The dataset used in this project contains a set of locations with their respective latitude and longitude coordinates. These locations represent potential pick-up and drop-off points for ride-sharing services.
Using the location data, we implemented a selection algorithm to identify the best 5 nominees for ride-sharing. The algorithm considers factors such as distance, traffic conditions, and potential demand to make optimal choices.
The code for this project is available in the file RideSharing.py. It contains the implementation of the nominee selection algorithm and any supporting functions required for the analysis.
Feel free to explore the code to understand the logic behind the selection process and make any improvements if necessary.
If you have any questions or feedback, please don't hesitate to reach out.