Smart task logging : Prediction of tasks for timesheets with machine learning

• Main Authors: Bengtsson, Emil, Mattsson, Emil
• Format: Others
• Language: English
• Published: Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) 2018
• Subjects: Computer science; machine learning; multiclass logistic regression; multinomial logistic regression; Scala; JavaScript; web application; training data; Computer Systems; Datorsystem
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-76152

Every day most people are using applications and services that are utilising machine learning, in some way, without even knowing it. Some of these applications and services could, for example, be Google’s search engine, Netflix’s recommendations, or Spotify’s music tips. For machine learning to work it needs data, and often a large amount of it. Roughly 2,5 quintillion bytes of data are created every day in the modern information society. This huge amount of data can be utilised to make applications and systems smarter and automated. Time logging systems today are usually not smart since users of these systems still must enter data manually. This bachelor thesis will explore the possibility of applying machine learning to task logging systems, to make it smarter and automated. The machine learning algorithm that is used to predict the user’s task, is called multiclass logistic regression, which is categorical. When a small amount of training data was used in the machine learning process the predictions of a task had a success rate of about 91%.