- This Repository provides a Meticulously Detailed and Exhaustive Exploration of HR Attrition Data.
- Leveraging this Project enables the Extraction of Profound Insights and the Formulation of Discerning Decisions.
Human Resources Attrition, the Phenomenon of Employees leaving an Organization, is a Critical Factor influencing Organizational Stability and Growth. This complex issue Reflects not only the Health of the Workplace Environment but also the Efficacy of Talent Management Strategies.
This Project offers a Sophisticated Analytical Framework for Exploring HR Attrition Data, uncovering the Intricacies of Employee Turnover. It Enables Organization to gain Profound Insights into Workforce Dynamics, Enhancing their understanding of the Factors driving Attrition and Refining their Strategies for Improved Employee Retention and Organizational Harmony.
- Project Goal
- Project Motivation
- Dataset
- Dataset Description
- Requirement
- Installation : ETL Tools
- Data Cleaning
- My Project
- Author
- Lessons Learnt
- Acknowledgement
- About Me
- Technical Skills
- Show your Support
The Goal of this Project is to provide an In-depth and Sophisticated Analysis of HR Attrition Data, delivering Actionable Insights to Enhance Organizational understanding and Retention Strategies.
Driven by a Profound Interest in Data-driven Solutions, this Project not only Fulfills my Intellectual Curiosity but also Advances my Career by Deepening my Expertise in HR Analytics and Strategic Decision-making.
The HR Attrition Dataset is a Excel File, featuring Comprehensive and Meticulously Detailed Sheet.
- Kindly Access and Download the Dataset via the Link provided below
- Raw Data Link :- Raw Data.xlsx
When we examine the Data, we observe a number of Different Columns.
- Attrition : The Rate at which Employees leave the Company over a Specified period.
- Business Travel : The Frequency and Extent of Travel required for the Job.
- CF_Age Band : Categories that Group Employees based on their Age Ranges.
- CF_Attrition Label : Classification indicating whether an Employee is likely to Leave or Stay.
- Department : The Specific Division or Function within the Organization where an Employee works.
- Education Field : The Area of Study or Specialization an Employee's Education pertains to.
- Emp no : Unique Identifier assigned to each Employee.
- Employee Number : A Unique Number assigned to Identify employees within the Organization.
- Gender : The Gender classification of the Employee.
- Job Role : The Specific Position or Function an Employee holds within the Company.
- Marital Status : The Current Relationship Status of the Employee.
- Over Time : Whether the Employee works beyond their Regular hours.
- Over18 : Indicates whether the Employee is Above 18 years old.
- Training Times Last Years : The Number of Training Sessions or Programs the Employee attended in the Past Year.
- Age : The Current Age of the Employee.
- CF_current Employee : A Label indicating if an individual is Currently Employed with the Company.
- Daily Rate : The Amount of Money an Employee earns per Day.
- Distance From Home : The Geographical distance between the Employee's Home and Workplace.
- Education : The Highest Level of Schooling or Qualifications the Employee has achieved.
- Employee Count : The Total number of Employees within a Specific Category or Department.
- Environment Satisfaction : The Employee’s Satisfaction with their Work Environment.
- Hourly Rate : The Amount of Money an Employee earns per Hour of Work.
- Job Involvement : The Degree to which Employees are Engaged and Enthusiastic about their Job.
- Job Level : The Hierarchical Position or Rank of the Employee within the Company.
- Job Satisfaction : The Overall contentment an Employee feels about their Job.
- Monthly Income : The Total Earnings of the Employee per Month.
- Monthly Rate : The Compensation Amount an Employee receives on a Monthly basis.
- Num Companies Worked : The Number of Different Companies an Employee has worked for in their Career.
- Percent Salary Hike : The Percentage increase in the Employee's Salary compared to Previous Periods.
- Performance Rating : An Evaluation Score reflecting the Employee’s Job Performance.
- Relationship Satisfaction : The Degree of Satisfaction the Employee feels in their Relationships with Colleagues and Supervisors.
- Standard Hours : The Regular number of hours an Employee is Expected to work in a Standard Workweek.
- Stock Option Level : The Level of Stock options granted to the Employee as part of their Compensation Package.
- Total Working Years : The Cumulative number of Years an Employee has been Employed in their Career.
- Work Life Balance : The Employee's Ability to Manage work Responsibilities alongside Personal Life.
- Years At Company : The Number of Years the Employee has been with the Current Company.
- Years In Current Role : The Number of Years the Employee has been in their Current Job Role.
- Years Since Last Promotion : The Number of Years since the Employee was Last promoted within the Company.
- Years With Curr Manager : The Number of Years the Employee has been working under their Current Manager.
- ✅ Total Employees 🧑🧔🏻👩👩🏻💼
- ✅ Total Employees Attrition Rate
- ✅ Total Active Employees 👨🏻💻👩🏻💻
- ✅ Avg Age of Employees
- ✅ Total Attrition by High Performance 📈
- ✅ Total Attrition by Low Performance 📉
- ✅ Total Attrition by Work Distance
- ✅ Total Attrition by Employees Job Role
- ✅ Total Attrition by Employees Education 👨🏻🎓📚
- ✅ Total Attrition by Employees Business Travel
- ✅ Total Attrition by Employees Gender
- ✅ Total Attrition by Department 📝
- ✅ Total Attrition by Employees Age Group and Gender 👦🏻👩🏻
Using the Raw Data, I crafted an Insightful and Visually Compelling Dashboard in Excel.
- MS Excel Installation Link :- https://www.microsoft.com/en-in/microsoft-365/excel
- Changed the Data Types wherever required.📅
- Removed Duplicates.
- Replaced data with meaningful data etc.📝
- Applied Sorting and Filters📶
Comprehensive Analysis has been conducted on the Dataset, illustrated through a Variety of Engaging Plots📊📈.
Additionally, the Dashboard offers Customizable filters for Enhanced Data Exploration by Different Department🎓📚💡 and Gender👩🏻💻👨🏻💻.
This Illustrates the Analysis with "Female" Employees. 📍
This Illustrates the Analysis with "Male" Employees. 📍
This Illustrates the Analysis with "HR" Department. 📍
This Illustrates the Analysis with "R&D" Department. 📍
This Illustrates the Analysis with "Sales" Department. 📍
- ⭐Data Quality is Crucial
- ⭐Understand Your Audience
- ⭐Effective Data Visualization
- ⭐Dashboard Performance
- ⭐Data Privacy and Security
- ⭐User Interactivity
- ⭐Regular Updates and Maintenance
- ⭐Clear and Actionable Insights
Thank you to Kaggle for providing me this Invaluable Resource, which I leveraged to Enhance my Analysis and Visualization of the Data throughout the Project.
- Kaggle :- https://www.kaggle.com
I am Passionately delving into the realm of Data Analytics, engaging in thorough Learning and Hands-on Projects to refine my skills. As I explore Career Opportunities, I am eager to Transform data into Valuable Insights and contribute to a Dynamic and Innovative Organization.
- 📌 LinkedIn :- https://www.linkedin.com/in/mansi-p-s-9052a0311
- 📌 Tableau :- https://public.tableau.com/app/profile/mansi.ps
- 📌 Github :- https://github.com/MansiPS
Anaconda-Jupyter Notebook 
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Exploratory Data Analysis 📊📈👨🏻💻.
Excel 
, Word 
, Powerpoint 
.
Tableau 
, Power BI 
, Looker Studio 
.
Pandas, Numpy, Matplotlib, Seaborn, Plotly, Scipy.
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If you have any Feedback, please reach out to me at LinkedIn :- https://www.linkedin.com/in/mansi-p-s-9052a0311