Employee Attrition

Analytics Vidhya Job-A-Thon November 2021

Approach

• The problem is to predict employee attrition, and analogous to customer churn, and a similar approach can be used to model the data as a binary classification problem
• The data is present over a time frame (reporting_dates) for each employee
• The aim is to predict if an employee will churn in the next six months
• The target variable in the train set will be generated for each record as a binary output (0 or 1, with 1 indicating attrition)
• Inspiration to developing the approach has been derived from Korichi et al.

Notebook

Run Notebook:

Data Processing

• Add the last working date to all records as applicable (for attritioned emp_id)
• Evaluate tenure of emp_id for each record {record_date - dateofjoining}
• Evaluate grade_chg_join {designation - joining_designation}
• Evaluate median salary at a particular period and difference from employee salary (salary - median_salary)
• Separate out the test set i.e. records of employees of date '2017-12-01'
• Create target variable attr_risk
• Calculate time before attrition t_attr (in months) for all records (lastworkingdate - record_date)
• Binarize above result and evaluate attrition risk attr_risk as per the formula:
  
• Encode categorical variables

Features provided in set

Features input to model

Final Model

• The train set is modelled using XGBoost
• There are 43 features in the final set
• The model is tuned using RandomizedSearchCV using 15 folds of 60 iterations