The document analyzes employee attrition, highlighting key factors such as marital status, income, and job involvement that influence turnover. It discusses trends among employees aged 20-35, satisfaction levels, and uses predictive models like Naive Bayes and linear regression for classification and income forecasting. The conclusion emphasizes the need for improved employee engagement and equitable compensation, particularly for lab technicians and research scientists.

1. EMPLOYEE
ATTRITION ANALYSIS
Kris Ghimire

2. LEVERAGE DATA SCIENCE
IDENTIFY FACTORS
INTERESTING
TREND
TURNOVER
CLASSIFICATION PREDICTION
MODEL

3. Over Time
Stock Option
Marital Status
Income
Job Involvement
ATTRITION FACTORS
1
2
3
4
5

4. ATTRITION
4
MARITAL STATUS
High attrition among single employee

5. ATTRITION
5
INCOME ANALYSIS
Income issue always challenging for company
Job Hopper – Low wage employee

6. ATTRITION
YES NO
Reduction in performance
Health and Stress
Turnover
OVER TIME
6
Over Time Vs AttritionOver Time Vs Attrition

7. ATTRITION
7
STOCK OPTION Way of reward, attract and retain employees
Growth opportunity

8. Makes employee valued
Improves decision capability
Create bond among coworkers
JOB INVOLVEMENT
ATTRITION
Motivation
Lower attrition among engaged employees
8

9. ATTRITION
9
Income Analysis

10. SATISFACTION
Happy with the
company
Sales Executive- Most
Satisfied also higher in
number
10

11. INTERESTING TREND
Majority of Employee Between 20-35 Years
Just One Employee around 60
Fairly New to the Company
Sale Executive the dominating number
11

12. 12
naïve Bayes Attrition Classification
Attrition No Attrition Yes
Sensitivity : 91%
Specificity : 76%
Accuracy = 83%

13. 13
Linear Regression Income Prediction
RMSE = 1340
Adjusted R-squared: 0.9184

14. Focus on employee
engagement
Make stock option more
available.
Reduce over time
CONCLUSION
Investigate why Lab
Technician and
Research Scientist
aren’t paid enough

15. THANK YOU!