Uber Data Analysis

Abstract

The Uber supply situating model involves anticipating demand patterns and placing driver partners over hubs with the intention of entering the demand, reducing ETAs, and increasing overall potency. Moving from a passive supply-positioning model to acting through specific network recommendations is one of the main focus areas. Since supply optimization is one of Uber's most important priorities, the challenge is to quickly improve supply in high-demand areas (or will be). Search surge, a real-time method in which supply comes from the region with the highest demand, is one of the methods. For instance, if you observe a search surge of two or three times, it indicates how much demand is in that particular space and what kind of service would you like. Another example is when Uber launched its Uber Movement service at the beginning of 2017. It provides access to the outline of travel times between completely different regions of the chosen town and contains billions of items of trip information. It's made tremendous energy, all things considered stirred doubt at steady time among analysts, quality trained professionals, and town organizers. So, is Uber making access to its extensive database free and democratizing information? Maybe not most, but it's actually made a tremendous work to blend and imagines that amount of data for different urban communities round the world. so that local drivers can obtain such travel time predictions information.