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Study Of Uber-Related Data Using Machine Learning

A.Pallavi Jyothi, G. Rohini, G. Gayathri, B. Sai Reddy


This paper describes the operation of the machine learning algorithm used in the Uber database, which contains data generated by the Uber Movement for a few locations in Hyderabad and the big apple City. Uber is known as a peer-to-peer program. This program connects you to the nearest drivers available to take you to your destination. This database includes Uber capture data with information such as time, ride date additional information such as location, using this data, this paper describes the use of the k-algorithm for combining specific data and activity in different parts of Hyderabad and big apple City. As the industry grows beyond expectations, an effective cable deployment will help each passenger and driver reduce the waiting time to hunt. This model is used to predict the need for cables in a city identified by the uber organization and can predict data-based demand. In this project, our primary data source is uber traffic and user data.This paper shows how the Uber movement data set works, which includes data from Uber from a few locations in Hyderabad and New York. Uber is known as a forum for peer-to-peer. The website connects customers and local drivers who can drive customers to the desired location. The database contains basic data about Uber capture, including time, date, and ride time, as well as location information]. This paper describes how to use the k-integration algorithm to collect data and operate other parts of Hyderabad and New York using this information.


Machine learning, k-means clustering, matplotlib, NumPy, Uber database

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