HR Analytics - Predict Employee Attrition

June 26, 2018
This model is created using the data put up by IBM. This model contains 35 variables with the attrition indicator variable name Attrition
Employee retention plays an important role in the success of any organization and the effectiveness of its HR department. This model is created using the data put up by IBM. This model contains 35 variables along with the attrition indicator named "Attrition". This is a data set contains 1470 rows worth of data with 35 variables making it robust and vivid. This model can be further improvised and is a must use for any data scientist or analytics enthusiast. **Data Set Variables:** Age **Attrition (Label or Outcome Variable)** BusinessTravel DailyRate Department DistanceFromHome Education EducationField EmployeeCount EmployeeNumber EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel JobRole JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager To make this experiment even more interesting, I have used the Permutation Feature Importance widget that can be used to compute the variable importance given the scores based on the trained model and test data. **Information about the creator:** **Sunil Kappal** is a freelance Advance Analytics Consultant. You can view his profile on his website [Statistics For Rookies][1] **Sunil Kappal** is also running his own blog, [access his blog here][2] Tel: +91-9013830386 Email:, [1]: [2]: