An approach to predict taxi-passenger demand using quantitative histogram on Uber data

The precise prediction of the day to day and monthly transactions is of great value for companies. This information can be beneficial for the companies in analyzing their ups and downs and draw other plans. Moreover, a precise prediction method can optimize the performance of a company. The branch of analytics that deals with prediction is known as predictive analytics. This paper presents the use of data analytics in analyzing the transaction dataset provided by Uber to predict the possible outcomes and the changes to be made. The histograms and heat maps drawn provide us a clear visualization of the dataset and we must predict the rest out of it.