Downloaded 49 times
More Related Content
Similar to Employee Attrition Analysis / Churn Prediction
![IBM-HR-Analytics-Employee-Attrition-and-Performance[1].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/ibm-hr-analytics-employee-attrition-and-performance1-251018181740-7e1d1f2b-thumbnail.jpg?width=640&height=640&fit=bounds)
Employee Attrition Analysis / Churn Prediction
- 1.
- 2.
- 3. BUSINESS PROBLEM A leadingorganisation's business had been increasing quite well over the past. But, there has been slowdown in terms of growth. To predict, why best and most experienced employees are leaving based on the given profile. HRM Planning is very important for the companies to make sure the continued retention of the high performers with the best talent.
- 4.
- 5. Attributes On to theLeft Numerical Attributes On to the Right Categorical Attributes Target Variable Left
- 6. Attribute – Range/Level ATTRIBUTERANGE/LEVEL Satisfaction_level 0.09 to 1.00 Last_Evaluation 0.36 to 1.00 Number_Project 2 To 7 Average_monthly_hours 96 To 310 Time_spend_company 2 To 10 Department 10 Levels Salary 3 Levels Left, Work accident, Promotion_last_5years 0 , 1
- 7. Data Understanding ATTRIBUTE 0’s1’s Left 11428 3571 Left (Distinct) 10000 1991 Total number of observations is 14999 with 9 independent attributes. The given data has mix of Numerical & Categorical Attributes. Target variable is left & it is discrete, which has high class imbalance.
- 8. Binary Classification (Approach) Given, Targetvariable is left & it is discrete which has two classes. Where as ‘1’ is considered as left & that of ‘0’ is considered as not left . This is supervised learning classification model building approach. Error Metric to be worked on is F1Score .
- 9.
- 10. Correlation Plot Positive Correlation Number_project, last_evaluation, Average_monthly_hours, Time_spend_company Negative Correlation Satisfaction_level, Number_project , Time_spend_company Neutral /No Correlation Average_monthly_hours, satisfaction_level
- 11.
- 12.
- 13. Department v/s Left (Salary) Attritionrate for the low salary is high. Sales, Support, Technical departments has high attrition.
- 14. Avg. Satisfaction Level v/s Avg.Monthly Hours Monthly hours does have effect on satisfaction level More monthly hours have less satisfaction level Reason : Human capacity per day
- 15.
- 16. INFERENCE Common traits ofGood people leaving Experienced Very low satisfaction level Spend more time at work Possible Reasons for people leaving Experienced people may not be finding any challenges in work. Hence they leave. Work to Pay ratio may be high (because we find clear correlation only in low and medium salary ranges)
- 17.
- 18. Data Preprocessing Task DuplicateRecords No Missing Values Subsetting & Categorical conversion Standardization Handling Class Imbalance Implementation Using Distinct Function -- Using as.factor() function Using Range, Z-Score methods Using SMOTE - 60:40, 70:30, 80:20
- 19. MODEL BUILDING AsTarget Variable (left) is discrete, hence Various Classification Techniques has to be applied. Started with Logistic regression and then with Decision Trees, RandomForest and XGBOOST obtained predicted values. MODEL APPLIED / F1 Score TRAIN VALIDATION TEST LOGISTIC 0.5932136 0.5982906 0.5939249 DECISION TREES 0.9383260 0.9302899 0.9315540 RANDOMFOREST 0.9673754 0.9988067 0.9643624 XGBOOST 0.9818643 0.9953924 0.9693356
- 20. MODEL BUILDING Using ROCcurve prob > 0.35 is taken as 1’s & rest as 0’s Logistic Regression Hyper paremeters : ntrees = 200 to 500 Mtry = 4 (3 to 6) Random Forest
- 21. MODEL BUILDING Tree Depth: Reduced from 32 to 6 Decision Trees Enabled Cross Validation Handling Missing Values Tree pruning XGBOOST
- 22. FUTURE ENHANCEMENTS Withthe use of functional/domain knowledge, Feature engineering will be done by generating a new columns or attributes. Other Classification models will be applied with the respective hyper parameter tuning.
- 23.