Data Analysis Using R
Budget: $2 – $8 USD
A company would like to identify the employees that are likely to leave the company.
The company has demographic, educational, and other information about the
employees. The data set is split into two parts: HRRetention_train.csv and
HRRetention_test.csv.
Build predictive models (use at least 3 different algorithms) that would help the
company to identify the employees that are likely to leave.
Task1: Check the missingness in data. Use appropriate charts and tables to explain
the missing data issues.
Task2: Use appropriate data imputation techniques to impute data.
Task 3: Run multiple algorithms to predict which employees are more likely to leave
the company.
Task 4: What are the most important predictors of whether an employee will leave
the company? Clearly state your conclusions and recommendations for the company’s HR department.
The company has demographic, educational, and other information about the
employees. The data set is split into two parts: HRRetention_train.csv and
HRRetention_test.csv.
Build predictive models (use at least 3 different algorithms) that would help the
company to identify the employees that are likely to leave.
Task1: Check the missingness in data. Use appropriate charts and tables to explain
the missing data issues.
Task2: Use appropriate data imputation techniques to impute data.
Task 3: Run multiple algorithms to predict which employees are more likely to leave
the company.
Task 4: What are the most important predictors of whether an employee will leave
the company? Clearly state your conclusions and recommendations for the company’s HR department.