- This Repository offers an Intricate and Comprehensive Analysis of HR Analytics Data.
- By Harnessing this Project, we can reveal Profound Insights and make Discerning Decisions.
In Today’s Data-driven World, HR Analytics has Emerged as a Transformative Tool for Optimizing Human Resource Management. By Harnessing the power of Data, HR Professionals can gain Profound Insights into Workforce Dynamics, Uncover Trends and make Strategic Decisions that drive Organizational Success.
This Analytical approach not only Enhances Operational Efficiency but also Fosters a more Inclusive and Equitable workplace by Identifying and Addressing key HR Metrics and Issues.
- 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 Develop a Cutting-Edge HR Analytics Dashboard that Seamlessly integrates Advanced Data Visualizations to provide Actionable Insights into Workforce Metrics, thereby Enabling Strategic Decision-Making and Enhancing Organizational Efficiency.
Driven by an Unwavering Passion for Data Analytics and a Fervent Commitment to Advancing my Career, I am Motivated by the Opportunity to Leverage my skills to create Impactful Solutions that Drive Meaningful Improvements in HR Management and Contribute to my Professional Growth.
The HR Analytics 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 a Company, either Voluntarily or Involuntarily.
- Business Travel : The Extent or Frequency of Travel required for an Employee’s job.
- CF Age Band : A Categorized Age Range for Employees.
- CF_Attrition label : A Label Indicating whether an Employee has left the Company (Attrition) or is still Employed.
- Department : The specific Department or Division within the Organization where an Employee works.
- Education Field : The Academic Discipline or Field of Study in which the Employee holds a Degree.
- Emp No : Employee Number, a Unique Identifier assigned to each Employee.
- Employee Number : The same as 'Emp No', a Unique Identifier for an Employee.
- Gender : The Gender of the Employee.
- Job Role : The Position or Title held by the Employee within the Company.
- Marital Status : The Employee’s Marital Status.
- Over Time : Indicates whether the Employee works Overtime.
- Over18 : Indicates whether the Employee is over 18 Years of Age.
- Training Times Last Year : The Number of Training Sessions or Courses an Employee Completed in the Past Year.
- Age : The Age of the Employee.
- CF_Current Employee : A Label indicating whether the Employee is Currently Employed by the Company.
- Daily Rate : The Employee’s Daily pay Rate.
- Distance From Home : The Distance between the Employee’s Home and the Workplace.
- Education : The Highest level of Education attained by the Employee.
- Employee Count : The Total number of Employees in the Dataset or Organization.
- Environment Satisfaction : A Measure of how satisfied Employees are with their work Environment.
- Hourly Rate : The Employee’s pay rate per hour.
- Job Involvement : The Degree to which an Employee is involved and engaged with their Job.
- Job Level : The level of the job within the organization’s hierarchy (e.g., Entry-level, Manager, Senior Executive).
- Job Satisfaction : A Measure of how satisfied the Employee is with their job.
- Monthly Income : The Total Income an Employee earns per Month.
- Monthly Rate : The Employee’s pay rate per Month.
- No Companies Worked : The Number of Different Companies the Employee has worked for in their Career.
- Percent Salary Hike : The Percentage increase in Salary an Employee Receives, usually during a Performance Review.
- Performance Rating : The Rating given to the Employee’s Performance often used in Performance Evaluations.
- Relationship Satisfaction : The Level of Satisfaction an Employee has with their Work Relationships.
- Standard Hours : The Standard number of Working Hours expected per Week.
- Stock Option Level : The Level of Stock options granted to the Employee.
- Total Working Years: The total number of years the employee has worked in their career.
- Work Life Balance : A Measure of how well the Employee can balance work demands with Personal Life.
- Years At Company: The Number of Years the Employee has worked at 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’s Last Promotion.
- Years With Curr Manager : The Number of Years the Employee has worked with their Current Manager.
- ✅ Employees Count🧑🧔🏻👩👩🏻💼
- ✅ Attrition Count
- ✅ Attrition Rate
- ✅ Active Eployees
- ✅ Avg Age
- ✅ Attrition by Gender 👨🏻💻👩🏻💻
- ✅ Department-wise Attrition
- ✅ No of Employees by Age Group
- ✅ Job Satisfaction Ratings
- ✅ Education Field wise Attrition 👨🏻💻
- ✅ Attrition Rate by Gender for Different Age Group
Using the Raw Data, I crafted an Insightful and Visually Compelling Dashboard in Tableau Public.
- Tableau Public Installation Link :- https://www.tableau.com/tableau-login-hub
- 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 Education🎓📚💡 Category and Age Bin⏳.
This Illustrates the Analysis of Education with "Bachelor's Degree". 📍
This Illustrates the Analysis of Education with "Master's Degree". 📍
This Illustrates the Analysis of Education with "Associates Degree". 📍
This Illustrates the Analysis of Education with only "High School". 📍
This Illustrates the Analysis of "Research and Development" Department. 📍
This Illustrates the Analysis of "Sales" Department. 📍
This Illustrates the Analysis of Attrition with Education Field as "Life Sciences". 📍
This Illustrates the Analysis of Attrition with Education Field as "Marketing". 📍
This Illustrates the Analysis of Attrition of "Female" Employees. 📍
This Illustrates the Analysis of Attrition of "Male" Employees. 📍
This Illustrates the Analysis with Age Bin as "5" in No of Employees by Age Group. 📍
This Illustrates the Analysis with Age Bin as "10" in No of Employees by Age Group. 📍
- ⭐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 
.
Exploratory Data Analysis 📊📈👨🏻💻.
Excel 
, Word 
, Powerpoint 
.
Tableau 
, Power BI 
, Looker Studio 
.
Pandas, Numpy, Matplotlib, Seaborn, Plotly, Scipy.
If you appreciate this Project, please consider awarding it a ⭐
If you have any Feedback, please reach out to me at LinkedIn :- https://www.linkedin.com/in/mansi-p-s-9052a0311