Abstract

The case study discusses the importance and challenges of forecasting at Uber. The company used its own Time Series data for forecasting. Time Series is a dataset containing values over a period of time. The time period could be seconds, minutes, hours, days, months, years, etc. Time Series data was analyzed using time series basics to forecast the demand for Uber cabs. Regression analysis was also used to forecast demand. The addition of different variables obtained from varied sources also lends credence to the analysis. The choice of variables for this analysis is important. Combining data from different sources to make the analysis actionable is a key insight.

Learning Objectives

The case is structured to achieve the following Learning Objectives:

• To have a basic understanding of the platform economy and business model of Uber in the context of the platform economy
• To understand how to accomplish data wrangling before using data for analysis
• To understand the importance of forecasting
• To understand time series forecasting as a technique
• To understand the use of different data sources to enrich the analysis

Keywords

Python; Multiple Regression; Forecasting; Platform Economy; Data Wrangling; Time Series forecasting