# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 3 Employee Absenteeism Data  
### Data Sources  
The MojeUre system ([[https://mojeure.si/](https://mojeure.si/)]([https://mojeure.si/](https://mojeure.si/)), accessed on 20 June 2024) was developed by 1A Internet d.o.o., a Slovenian company, to facilitate employee scheduling, working time recording and absence management. This comprehensive system enables the uncomplicated recording of working hours and offers functions such as holiday management, sick leave recording, travel orders and much more.  
Working hours can be recorded on the Internet or through a mobile app. In addition, companies can purchase a time recording machine that allows employees to clock in or out using personalized cards. This system supports different types of breaks, such as lunch breaks and private breaks. Employees can also record their hours at NFC (Near Field Communication) or BLE (Bluetooth Low Energy) enabled clocking points or by scanning a QR code with their mobile phone camera. Manual entry of working hours is also possible, allowing you to record different types of work hours within a single day with just a few clicks.  
The data analyzed in this study comes from the MojeUre electronic system, which is used by more than 200 different companies in Slovenia. This system primarily records employee check-ins and check-outs and categorizes different types of absences, including sick, vacation, paternity, maternity, part-time, study, and student leave.  
For this paper, we analysed the MojeUre data from 2017 to the end of 2020. This period was chosen because of the availability of data in the system's database and was deemed sufficient for our initial proof-of-concept analysis. Future research will focus on more recent data, especially after 2020, to assess the impact of the global COVID-19 pandemic.  
In compliance with the General Data Protection Regulation (GDPR--[[https://eur-lex.europa.eu/eli/reg/2016/679/oj](https://eur-lex.europa.eu/eli/reg/2016/679/oj)]([https://eur-lex.europa.eu/eli/reg/2016/679/oj](https://eur-lex.europa.eu/eli/reg/2016/679/oj)), accessed on 12 April 2024), the data provider signed the necessary agreements with all participating companies. This ensures that individual data is not only accessible for review by the companies themselves but is also legally compliant for use in analytical purposes.  
In Figure 1 we show the distribution of the different company types from our dataset. As we can see, most employees are employed in the company type \"Education, translation, culture and sport\", the second most common company type is \"Agriculture, fishing and forestry\" and the third most common company type is \"Production\". The fewest employees are employed in the \"Insurance\" company type.  
In Figure 2 we show the distribution of employee data between different statistical regions of Slovenia as recorded in our database. The \"Central\" region, in which the Slovenian capital Ljubljana is located, has the most employees. This is followed by the \"South-East Slovenia\" region. The \"Littoral-Inner Carniola\" region has the fewest employees, which indicates that fewer companies are represented in the database. This graph illustrates the regional differences in employment within the data.  
In Figure 2b we visualise the statistical regions of Slovenia. The country is divided into 12 different regions, ranging from the smallest, R12--Central Sava, to the largest, R7--Central Slovenia. This map provides a visual representation of the geographical distribution and relative size of the regions.