HR-Attrition-Analytics-Dashboard

Dashboard:

Project Objective:

To create a dashboard that can help HR to build strategies in order to reduce the attrition of employees from the company.

About Dataset:

The dataset consist 1500 records spread across 38 columns which include likes of 'EmpID','Age','Attrition','Business Travel','Daily Rate','Department','Distance From Home','Education','Education Field','Employee Count','Employee Num','Enviroment Satisfaction','Gender','Hourly Rate','Job Involvement','Job Level','Job Role','Job Satisfaction','Marital Status','Monthly Income','Salary Slab','Monthly Rate','NumOfCompaniesWorked',''Monthly Income','No. of Companies Worked','Year Since Promotion' etc

Insights Gathered:

• 1] Employees in age group 26-35 have high attrition count.
• 2] Employees having education background in Life Sciences are leaving more.
• 3] Top 5 designation that has high attrition count are Lab technicion, Sale executive, Research Scientist, Sales Represenative, Human Resource.
• 4] Employee with satisfactory score 1 have high attrition count.
• 5] Employee having salary less than upto 5k are leaving more.
• 6] Employye with less than 1 year in company are leaving more.
• 7] Employye with job level 1 have high attrition count.
• 8] Single employees have high attrition count followed by married.
• 9] Employees having hourly rate 70+ are leaving more.
• 10] Human Resource and Sales department have highest attrition rate.

Remember, data analysis is an ongoing adventure. So grab your coffee ☕, investigate further, and keep drawing those insights! 🚨 Feel free to expand upon this conclusion or add any additional findings you discover. Happy analyzing! 😊🐱‍👓