Revolving drivers: data mining and discovering the causes of driver turnover
| dc.contributor.author | Pazdor, Adam | |
| dc.contributor.examiningcommittee | Wang, Yang (Computer Science) Wang, Liqun (Statistics) | en_US |
| dc.contributor.supervisor | Leung, Carson K. (Computer Science) | en_US |
| dc.date.accessioned | 2019-12-16T17:16:27Z | |
| dc.date.available | 2019-12-16T17:16:27Z | |
| dc.date.issued | 2019-12 | en_US |
| dc.date.submitted | 2019-12-12T00:02:35Z | en |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Turnover (or Churn) is a great concern to every industry. Employees who leave represent hours of training wasted and the expense of hiring a replacement, something undesirable for any business. Few industries experience the problem as acutely as the trucking industry, where turnover rates have been as high as 90%. Uncovering the underlying reasons that are behind why so many drivers leave their jobs is a point of priority for many trucking companies. A solution, or even an explanation, could mean hundreds of training hours and thousands of dollars saved. In this M.Sc. thesis, I examine real-life data from a trucking company and use a random forest model to understand the driver turnover situation. | en_US |
| dc.description.note | February 2020 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34409 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Data mining | en_US |
| dc.subject | Transportation data mining | en_US |
| dc.subject | Data science | en_US |
| dc.subject | Business analytics | en_US |
| dc.subject | Churn rate | en_US |
| dc.subject | Turnover rate | en_US |
| dc.subject | Data analytics | en_US |
| dc.title | Revolving drivers: data mining and discovering the causes of driver turnover | en_US |
| dc.type | master thesis | en_US |