Delmy Valle (firstname.lastname@example.org)
Myat M Khant (email@example.com)
Nilajah Rivers (Nilajah.Rivers@baruchmail.cuny.edu)
CIS 4400 PTRA
Uber Dataset Analysis
Uber Technologies, Inc. is an immensely popular American born company based in San Francisco California. Uber offers peer to peer ride-sharing, ride-hailing, micro-mobility (utilizing electric bikes and scooters), and food delivery (Uber Eats). Customers can access Uber through its online platform by using any electronic device with an internet connection. The most popular being through Uber’s mobile app.
Uber has expanded to multinational levels over the years and now operates in over 785 metropolitan areas worldwide. And for many New Yorkers without their own cars on hand, Uber provides a convenient way to get around the city when riding the MTA isn’t ideal. Our data warehouse takes a look at several different metrics. The first is Uber pickups in New York City spanning six months, from January through June 2015. The main analysis focuses on Uber pickup volumes as they relate to weather, time of day, location, and holidays. The derived information could allow Uber drivers to plan ahead, and position themselves near locations where they are more likely to secure a fare during similar times. For our analysis, we will be utilizing the uber_nyc_enriched.csv file from Kaggle.com. This dataset provides us with detailed information such as pick up dates and times, temperature, rain activity, location, holiday information, and several other factors that could have an effect on Uber pickup volume. We also chose to look at fare acquisitions in general by utilizing Uber request data accumulated from various locations. The data gives relevant information on trip statuses such as completion, cancellation, and car availability. This information can be used to highlight the volume of completed Lastly, the database houses customer sentiment information sourced from Consumer Affairs.
Our fact tables are Weather Fact and Uber Pickup Fact. our dimensions are Temperature Dimension, Date Dimension, Precipitation Dimension, and Region Dimension.