Employee-Absenteeism-R-Python

The task is to build a machine learning regression model will predict the number of absent hours. As Employee absenteeism is a major problem faced by every employer which eventually lead to the backlogs, piling of the work, delay in deploying the project and can have a major effect on company finances. The aim of this project is to find an issue which eventually leads toward the absence of an employee and provide a proper solution to reduce the absenteeism.

Dataset Details:

Dataset Characteristics: Timeseries Multivariant

Number of Attributes: 21

Attribute Information:

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1. Individual identification (ID)
2. Reason for absence (ICD).
3. Month of absence
4. Day of the week (2. MONDAY 3. TUESDAY 4. WEDNESDAY 5. THURSDAY 6. FRIDAY)
5. Seasons (1. SUMMER 2. AUTUMN 3. WINTER 4. SPRING)
6. Transportation expense
7. Distance from Residence to Work (kilometres)
8. Service time
9. Age
10. Workload Average/day
11. Hit target
12. Disciplinary failure (yes=1; no=0)
13. Education (HIGH SCHOOL (1), GRADUATE (2), POSTGRADUATE (3), MASTER AND DOCTOR (4))
14. Son (number of children)
15. Social drinker (yes=1; no=0)
16. Social smoker (yes=1; no=0)
17. Pet (number of pet)
18. Weight
19. Height
20. Body mass index
21. Absenteeism time in hours

Programming Language: Python 3 and R Language

Note:

Please refer the project report