Data storytelling is an important component of Machine Learning through which companies are able to understand the background of various operations. With the help of visualization, companies can avail the benefit of understanding the complex data and gain insights that would help them to craft decisions. Modern Ride-Hailing Apps, such as Uber, depend heavily upon Machine Learning to enhance the customer experience by providing the user with complete details about the driver and arrival time, informing the user in advance of the price of the ride, and giving the user an opportunity to evaluate their experience. This Uber Data Analysis uses two datasets-  Taxi and Limousine Commission (TLC) and NYC Uber Pickups with Weather and Holidays- to explore how time, location, and weather might affect the demand of rides in Washington D.C. and New York City, and to formulate recommendations of how these trends could be implemented in Uber’s business strategies and application development. 

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          Hundreds of millions of customers per year means even more data points to keep track of, which is why it is  imperative that Uber knows the answers to questions like where are the epicenters of travel, which times of the day have the most traffic, what factors would have the greatest effect on the pricing of the ride, or when should prices be increased or decreased to maximize profits? With the provided data visualizations and recommendations, Uber and its customers can feel in control of their decisions.