Employee Attrition
To determine which factors increases or decreases the probability of attrition
To identify which employees across different jobs are under high risk of attrition
Data Sources
To be able to provide any data analytical solution, it is essential to gather and inspect available sources including independent HR databases and possible outputs from tools and information systems
- Employee profile (experience and skillset)
- Satisfaction evaluation (company environment and coworkers)
- Performance evaluation
- Project planning and utilization
- Absence and time-sheets
- Communications and interaction schemas
Approach
Develop suite of ML models to identify potential employees at risk
Derive insights about drivers causing employees to leave
Devise scoring algorithm to quantify level of risk basis business rules
Feature Engineering & Selection
Advanced modelling techniques like neural networks or logistic regression are able to identify “drivers” that influence target variable risk of attrition in this case
In opposite to traditional and trivial methods such as simple correlation, advanced methods are able to get further information and uncover more complex patterns
Employees in a danger of the attrition, prediction model was developed & applied. For evaluating purposes, one-third of the dataset was separated to test the model accuracy
The rest was used to train the model & perform previous analysis. Developed model is able to predict 88.9% of employees with left-the-company flags
The Result
Feel more pressure to deliver
Required to work long hours
Frustrated due to inactivity
Suffer Brunout
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