Know the Incredible Use of Big Data Analytics in Uber

Know the Incredible Use of Big Data Analytics in Uber Uber is a taxi booking service connecting users who need to get somewhere with drivers willing to give them a ride via a smartphone app. Regular taxi drivers have claimed that the service is destroying their livelihoods, and there are concerns about the company's drivers' lack of regulation.

This hasn't stopped it from being hugely successful; since its initial launch in 2009 to serve only India, the service has expanded to many major cities on every continent.

The company is firmly rooted in Big Data and leveraging this data more effectively than traditional taxi companies has been critical to its success. The entire Uber business model is based on the Big Data principle of crowdsourcing. Anyone with a car willing to help someone get somewhere can offer to drive them there.

Uber has a large database of drivers in all the cities it serves, so when a passenger requests a ride, they can immediately match you with the best drivers. Fares are calculated automatically using GPS, street data, and the company's own algorithms, which make adjustments based on the length of the journey. This is a significant distinction from traditional taxi services in that customers are charged for the length of the journey rather than the distance traveled.

Price increase These algorithms continuously monitor traffic conditions and journey times, allowing prices to be adjusted as demand for rides changes and traffic conditions cause journeys to take longer. This encourages more drivers to drive when needed and stay at home when demand is low. The company has applied for a patent on this Big Data-informed pricing method known as "surge pricing."

This algorithm-based approach with little human oversight has occasionally caused issues it was reported that traffic conditions in New York on New Year's Eve 2011 increased fares sevenfold, with a one-mile journey increasing in price from $27 to $135 overnight.

This is an example of "dynamic pricing," which hotel chains and airlines use to adjust prices to meet demand; however, rather than simply raising prices on weekends and holidays, it uses predictive modeling to estimate demand in real time. To learn more about predictive modeling techniques, refer to the machine learning course in Mumbai.

Uber Pool However, changing how we book taxis is only one part of the larger plan. According to Uber CEO Travis Kalanick, the service will also reduce the number of private, owner-operated automobiles on the roads of the world's most congested cities.

UberPool allows users to find others in their area who, according to Uber data, frequently make similar journeys at similar times and offer to share a ride with them.

UberChopper, which offers helicopter rides to the wealthy, UberFresh for grocery deliveries, and Uber Rush, a package courier service, are among the other initiatives that have been tested or are set to launch in the near future.

● System of Evaluation The service also relies on a detailed rating system in which users can rate drivers and vice versa to build trust and allow both parties to make informed decisions about who they want to share a vehicle with.

Drivers, in particular, must be mindful of maintaining high standards; a leaked internal document revealed that those whose scores fall below a certain threshold risk being "fired" and not offered additional work. They also have to be concerned about their "acceptance rate." This is the ratio of jobs they accept to jobs they decline. Drivers were told to aim to keep this above 80% to provide passengers with a consistent level of service.

Uber's response to traditional taxi drivers' protests about its service has been to try to co-opt them by adding a new category to its fleet. UberTaxi, which means you'll be picked up by a licensed taxi driver in a registered private hire vehicle, has joined UberX (standard cars for standard journeys), UberSUV (large cars for up to 6 passengers), and UberLux (high-end vehicles) as a standard option.

● Controversies and Regulatory Pressure It will still have to overcome legal obstacles; the service is currently prohibited in a few jurisdictions, including Brussels and parts of India, and is under intense scrutiny in many other parts of the world. Several court cases in the United States are pending concerning the company's adherence to regulatory procedures.

Another criticism is that, because credit cards are the only payment method available, the service is inaccessible to a large proportion of the population in less developed countries, where the company has focused its expansion.

However, given its global popularity, the company has a significant financial incentive to continue with its plans to revolutionize private travel. If regulatory pressures do not kill it, it can revolutionize how we travel around our congested cities for environmental and economic reasons.

Uber is not alone; competitors such as Lyft and Ola provide similar services on a (so far) smaller scale. If, as a result of Uber's innovation, a deregulated private hire market emerges, it will be extremely valuable, and competition among these upstarts will be fierce. We can anticipate that the winners will be those who make the best use of the data at their disposal to improve the service they provide to their customers. The most successful is the one that best uses the data at its disposal to improve the service it provides to customers.

Ultimately, as you can see, data scientists contribute greatly in the advancement of uber services. If you’re interested in making a career in data science, sign up for a data science course in Mumbai, and land a high-paying job in MAANG companies.