# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 3 Employee Absenteeism Data
### Data Attributes and the Structure of the Data Instances
This level of detail helps to identify weekly patterns that are crucial to understanding regular absences, such as the tendency to take long weekends. The **seasonal lagged attributes** shown in Table 4 provide aggregates based on the occurrence of public holidays and the effects of seasonal variations. These attributes are calculated for future data projections and are important to improve the predictive accuracy \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Attribute Name** & **Type** & **Description** \\ \hline CompanyType & nominal & Company type by specific categories. We have define 33 different company types. \\ \hline Region & nominal & The region in which the employee’s company is located. We have define 12 different regions. \\ \hline EmploymentYears & numeric & Describes how many years the person has been employed by the current company. \\ \hline WorkHour & numeric & Data indicating how many hours per day an employee is employed by contract. \\ \hline JobType & nominal & Describes type of job (e.g., permanent, part-time). We have define 6 different job types. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The structure of demographic profile attributes. of the models during periods when employee absence patterns are affected by seasonal factors. These attributes help the models account for the increased likelihood of absences during public holidays and more specific holiday periods such as winter or spring holidays, which can vary greatly by region and culture. The attributes also capture absences during different seasons and reflect the seasonal impact on employee health and behavior. For example, absenteeism could increase in winter due to the flu season. On the other hand, the target attributes are used as prediction outputs. In the single-target prediction (see Table 1a), only one attribute is used as an output variable (e.g., the number of days of absence in the next week). With multi-target prediction (see Table 1b), two or more attributes are predicted simultaneously (e.g., the number of days of absence for the next four weeks). In Table 5 you will find the attributes that are used as targets in the case of vacation leave prediction and sick leave prediction. \begin{table}
\begin{tabular}{l l l} \hline
**Attribute Name** & **Type** & **Description** \\ \hline WeekPublicHolidayK+X & numeric & Count of absence days of public holidays. \\ \hline WeekWinterHolidayK+X & numeric & Count of absence days in winter holidays. \\ \hline WeekSpringHolidayK+X & numeric & Count of absence days in spring holidays. \\ \hline WeekSummerHolidayK+X & numeric & Count of absence days in summer holidays. \\ \hline WeekAutumnHolidayK+X & numeric & Count of absence days in autumn holidays. \\ \hline WeekWinterSeasonK+X & numeric & Count of absence days in winter season. \\ \hline WeekSpringSeasonK+X & numeric & Count of absence days in in spring season. \\ \hline WeekSummerSeasonK+X & numeric & Count of absence days in in summer season. \\ \hline WeekAutumnSeasonK+X & numeric & Count of absence days in in autumn season. \\ \hline \end{tabular}
\end{table} 
Table 4: The structure of the seasonal lagged attributes where X present week number.