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📊 HR Analytics – Employee Attrition & Performance Analysis

HR Analytics Project Cover

End-to-End HR Analytics Portfolio Project

SQL Server · Python · Power BI · Tableau

📑 Table of Contents

📌 Project Overview

HR Analytics – Employee Attrition & Performance Analysis is an end-to-end data analytics project focused on understanding employee workforce composition, attrition patterns, compensation, job satisfaction, work-life balance, business travel, and employee retention.

The project follows a complete analytics workflow:

Raw HR Data → SQL Server → Python EDA → Power BI → Tableau → Business Insights

The main objective is to transform raw employee data into meaningful business insights that can help HR teams understand employee attrition and identify factors associated with employee turnover.

🎯 Business Objectives

The project aims to answer important HR business questions such as:

  • How many employees are currently in the organization?

  • What is the overall employee attrition rate?

  • Which departments have higher employee attrition?

  • Which job roles experience higher employee turnover?

  • How does age relate to employee attrition?

  • How does salary relate to employee turnover?

  • Does overtime influence employee attrition?

  • How does job satisfaction relate to attrition?

  • Does work-life balance affect employee retention?

  • Does business travel influence employee turnover?

  • How does employee tenure relate to attrition?

  • Which employee segments may require greater retention attention?

🔄 Project Workflow


Raw HR Dataset

      ↓

SQL Server

Data Cleaning + Validation + Advanced SQL Analysis

      ↓

Python

Exploratory Data Analysis + Visualization

      ↓

Power BI

Interactive Business Intelligence Dashboards

      ↓

Tableau

Interactive HR Analytics Dashboards

      ↓

Business Insights

🗄️ 1. SQL Server – Data Preparation & Analysis

SQL Server was used for data storage, data cleaning, validation, KPI analysis, and advanced analytical queries.

Key Activities

  • Imported the HR dataset into SQL Server

  • Created the HR analytics database

  • Created the HR_Data table

  • Validated data structure and records

  • Removed unnecessary columns such as:

* EmployeeCount

* Over18

* StandardHours

  • Converted the Attrition field from Yes/No into a binary 1/0 flag

  • Calculated employee and attrition KPIs

  • Performed department-level analysis

  • Performed salary ranking analysis

  • Identified high-risk departments

Advanced SQL Concepts Used

  • SELECT

  • WHERE

  • GROUP BY

  • CASE

  • Aggregate Functions

  • Conditional Aggregation

  • CTEs (Common Table Expressions)

  • Window Functions

  • Ranking Functions

🐍 2. Python – Exploratory Data Analysis

Python was used for exploratory data analysis, data validation, visualization, and preparation of the cleaned dataset.

Libraries Used

  • Pandas

  • NumPy

  • Matplotlib

  • Jupyter Notebook

EDA Activities

  • Loaded and inspected the dataset

  • Checked dataset structure

  • Performed data cleaning

  • Analyzed employee attrition

  • Analyzed department-wise attrition

  • Examined salary distributions

  • Analyzed employee age patterns

  • Studied relationships between numerical variables

  • Created correlation analysis

Visualizations Created

  • Attrition Pie Chart

  • Department-wise Attrition Bar Chart

  • Box Plots

  • Correlation Heatmap

Python Files


HR\_EDA.ipynb

cleaned\_data.csv

📊 3. Power BI – Business Intelligence Dashboard

Power BI was used to create interactive HR dashboards using DAX measures and business-oriented visualizations.

Core DAX Measures

  • Total Employees

  • Active Employees

  • Attrition Employees

  • Attrition Rate

  • Average Monthly Income

Power BI Dashboards

Dashboard 1 – Executive Overview

Provides a high-level overview of:

  • Workforce size

  • Employee attrition

  • Attrition rate

  • Workforce demographics

  • Department-level trends

Dashboard 2 – Employee Demographics & Job Factors

Analyzes employee demographics and job-related factors associated with employee attrition.

Dashboard 3 – Salary, Performance & Attrition Insights

Focuses on:

  • Salary

  • Performance

  • Employee retention

  • Attrition-related factors

Additional Features

  • Interactive filters

  • KPI cards

  • Corporate dashboard theme

  • Page navigation

  • Data Dictionary & Metric Documentation

📈 4. Tableau – HR Analytics Dashboards

Tableau was used to recreate and enhance the HR analytics solution using the same business logic and analytical definitions.

The final Tableau workbook contains 2 interactive dashboards.

📊 Dashboard 1 – HR Workforce & Attrition Overview

This dashboard provides an overall view of the organization's workforce and employee attrition.

KPIs

  • Total Employees

  • Active Employees

  • Attrition Employees

  • Attrition Rate

  • Average Age

  • Average Monthly Income

  • Average Years at Company

Charts

| Analysis | Chart Type |

| ----------------------------- | -------------- |

| Attrition by Department | Horizontal Bar |

| Attrition by Job Role | Horizontal Bar |

| Attrition by Age Group | Bar Chart |

| Attrition by Gender | Donut Chart |

| Attrition by Overtime | Bar Chart |

| Attrition by Job Satisfaction | Bar Chart |

| Attrition by Salary Band | Bar Chart |

Filters

  • Department

  • Job Role

  • Gender

  • Overtime

📊 Dashboard 2 – HR Attrition & Performance Analysis

This dashboard focuses on deeper analysis of employee attrition and performance-related factors.

KPIs

  • Total Employees

  • Attrition Employees

  • Attrition Rate

  • Average Monthly Income

  • Average Years at Company

Charts

| Analysis | Chart Type |

| ------------------------------ | -------------- |

| Attrition Rate by Department | Horizontal Bar |

| Attrition Rate by Job Role | Horizontal Bar |

| Attrition by Salary Band | Column Bar |

| Attrition by Years at Company | Line Chart |

| Attrition by Work-Life Balance | Column Bar |

| Attrition by Business Travel | Bar Chart |

Filters

  • Department

  • Job Role

  • Gender

  • Age Group

  • Overtime

🧮 5. Calculated Fields

Several calculated fields were created in Tableau to support employee segmentation and attrition analysis.

Attrition Rate


Attrition Employees / Total Employees

The Attrition Rate measures the percentage of employees who left the organization.

Age Group

Employees were segmented into five age groups:

| Age Group | Age Range |

| --------- | ------------ |

| Under 25 | Below 25 |

| 25–34 | 25 to 34 |

| 35–44 | 35 to 44 |

| 45–54 | 45 to 54 |

| 55+ | 55 and above |

Salary Band

Employees were categorized into four monthly income bands:

| Salary Band | Monthly Income |

| ----------- | ---------------- |

| Below 3K | Below 3,000 |

| 3K–6K | 3,000–6,000 |

| 6K–10K | 6,000–10,000 |

| 10K+ | 10,000 and above |

📌 6. Key Metrics

The final analysis uses the following verified project-level metrics:

Metric Value
Total Employees 1,470
Active Employees 1,233
Attrition Employees 237
Attrition Rate 16.12%
Average Age 36.92
Average Monthly Income 6,502.93
Average Years at Company 7.01
Average Job Satisfaction 2.73
Average Work-Life Balance 2.76
Average Performance Rating 3.15
Metric Description
Total Employees Total number of employees in the dataset
Active Employees Employees who have not left the organization
Attrition Employees Employees who left the organization
Attrition Rate Percentage of employees who left
Average Age Average age of employees
Average Monthly Income Average monthly income of employees
Average Years at Company Average employee tenure
Average Job Satisfaction Average job satisfaction rating
Average Work-Life Balance Average work-life balance rating
Average Performance Rating Average performance rating

📖 7. Data Dictionary

| Column | Description |

| --------------- | ------------------------------------------ |

| EmployeeNumber | Unique employee identifier |

| Age | Employee age |

| Gender | Employee gender |

| Department | Employee department |

| JobRole | Employee job role |

| MonthlyIncome | Employee monthly income |

| YearsAtCompany | Number of years spent at the company |

| Attrition | Whether the employee left the organization |

| Overtime | Whether the employee works overtime |

| JobSatisfaction | Employee job satisfaction rating |

| WorkLifeBalance | Employee work-life balance rating |

| BusinessTravel | Frequency of business travel |

| EducationField | Employee education field |

| JobLevel | Employee job level |

| JobInvolvement | Employee job involvement level |

🔎 8. Key Insights

The analysis focuses on identifying important employee attrition patterns, including:

  • Departments with comparatively higher employee attrition

  • Job roles with higher employee turnover

  • Attrition patterns across different age groups

  • Relationship between overtime and employee attrition

  • Salary bands associated with employee turnover

  • Relationship between job satisfaction and attrition

  • Relationship between work-life balance and attrition

  • Business travel patterns associated with employee attrition

  • Employee tenure and attrition patterns

These insights can help HR teams identify employee segments that may require additional retention strategies.

🛠️ 9. Tools & Technologies

Data Analysis

  • Python

  • Pandas

  • NumPy

  • Matplotlib

  • Jupyter Notebook

Database

  • Microsoft SQL Server

  • SQL

  • CTEs

  • Window Functions

Business Intelligence

  • Microsoft Power BI

  • DAX

  • Tableau

Development & Version Control

  • Git

  • GitHub

🎯 10. Skills Demonstrated

This project demonstrates practical skills in:

  • Data Cleaning

  • Data Validation

  • Exploratory Data Analysis

  • SQL Querying

  • Advanced SQL

  • CTEs

  • Window Functions

  • Data Aggregation

  • KPI Development

  • DAX

  • Tableau Calculated Fields

  • Data Visualization

  • Dashboard Development

  • Interactive Filtering

  • Dashboard Navigation

  • Business Analysis

  • HR Analytics

  • Data Storytelling

  • Cross-Platform BI Development

🧭 11. Dashboard Navigation

The Tableau dashboards include an interactive navigation bar that allows users to switch between the two dashboards:


HR Workforce & Attrition Overview

              ↕

HR Attrition & Performance Analysis

The active dashboard button uses a highlighted background to clearly indicate the current dashboard.

🎨 12. Dashboard Design & Formatting

The dashboards were designed with a clean, professional, and business-oriented layout.

Design Features

  • Consistent dashboard titles

  • KPI card formatting

  • Consistent chart titles

  • Proper chart alignment

  • Structured rows and columns

  • Balanced dashboard spacing

  • Organized filters

  • Interactive navigation

  • Consistent visual hierarchy

  • Business-focused dashboard presentation

📁 13. Project Structure

The GitHub version of this project is organized to showcase the analysis while avoiding publication of the original Power BI and Tableau packaged files.

HR_Analytics_Portfolio_Project/
│
├── README.md
│
├── 1_Data/
│   └── Cleaned_Data/
│       └── cleaned_data.csv
│
├── 2_SQL/
│   └── HR_Analytics_SQL.sql
│
├── 3_Python/
│   └── HR_EDA.ipynb
│
├── 4_PowerBI/
│   └── HR_Analytics_Dashboard.pdf
│
├── 6_Documentation/
│   ├── Project_Documentation.docx
│   └── Project_Documentation.pdf
│
└── 7_Images/
    ├── Jupyter Notebook/
    │   ├── age_distribution_histogram.png
    │   ├── correlation_heatmap.png
    │   ├── department_attrition_bar_chart.png
    │   └── income_vs_attrition_boxplot.png
    │
    ├── Power BI/
    │   ├── Dashboard_1_Executive_Overview.png
    │   ├── Dashboard_2_Employee_Demographics_Job_Factors.png
    │   ├── Dashboard_3_Salary_Performance_Attrition.png
    │   └── Data_Dictionary_Metric_Documentation.png
    │
    └── Tableau/
        ├── Dashboard_1_Workforce_Overview.png
        └── Dashboard_2_Attrition_Performance.png

Files intentionally excluded from the public GitHub repository

  • Original Power BI .pbix file
  • Original Tableau .twbx file
  • Raw HR dataset
  • Jupyter checkpoint files
  • Windows desktop.ini

The cleaned dataset is included as the reproducible public analysis dataset.

🖼️ 14. Dashboard Screenshots

Power BI

Dashboard 1 – Executive HR Overview

Power BI Dashboard 1

Dashboard 2 – Department Analysis

Power BI Dashboard 2

Dashboard 3 – Employee Insights

Power BI Dashboard 3

Data Dictionary & Metric Documentation

Power BI Data Dictionary

Tableau

Dashboard 1 – Workforce Overview

Tableau Dashboard 1

Dashboard 2 – HR Attrition & Performance Analysis

Tableau Dashboard 2

Jupyter Notebook

EDA Visualizations

Age Distribution

Correlation Heatmap

Department Attrition

Income vs Attrition

📦 15. BI Source Files

The original Power BI and Tableau source files were used during dashboard development but are intentionally not included in the public GitHub repository.

The repository instead includes:

  • Power BI dashboard PDF
  • Power BI dashboard screenshots
  • Tableau dashboard screenshots
  • Final project documentation
  • SQL analysis
  • Python EDA notebook
  • Cleaned dataset

This keeps the portfolio repository focused on reproducible analysis and public-facing project evidence.

🏆 16. Project Outcome

This project demonstrates an end-to-end approach to solving a real-world HR analytics problem.

Starting from raw employee data, the project covers:


Data Preparation

      ↓

SQL Analysis

      ↓

Python EDA

      ↓

Power BI Development

      ↓

Tableau Development

      ↓

Dashboard Design

      ↓

Business Insights

The final solution provides an interactive analytical framework for understanding:

  • Workforce composition

  • Employee attrition

  • Compensation

  • Job satisfaction

  • Work-life balance

  • Business travel

  • Employee tenure

  • Department and job-role level attrition

The project also demonstrates the ability to apply consistent business logic across SQL, Python, Power BI, and Tableau, making it a strong portfolio project for Data Analyst and Business Analyst roles.

🔐 Public Repository Notes

This GitHub repository is prepared as a portfolio version of the project. The public version includes the cleaned dataset, SQL analysis, Python notebook, dashboard PDF/screenshots, and final documentation. Original .pbix and .twbx files are intentionally excluded from the public repository.

The analysis uses 1 = Left and 0 = Active for the binary attrition representation used in the project.

👨‍💻 Author

Ravindra Deo Kuldeep

Data Analytics · SQL · Python · Power BI · Tableau

⭐ Project Highlights


✔ End-to-End HR Analytics Project

✔ SQL Server Data Analysis

✔ Advanced SQL Queries

✔ Python Exploratory Data Analysis

✔ Power BI Dashboard Development

✔ Tableau Dashboard Development

✔ DAX Measures

✔ Tableau Calculated Fields

✔ KPI Development

✔ Interactive Filters

✔ Dashboard Navigation

✔ Business-Oriented Insights

✔ Data Visualization

✔ Data Storytelling

✔ Cross-Platform BI Development

✔ Portfolio-Ready Project

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HR Analytics portfolio project focused on employee attrition, performance, compensation, and workforce insights using SQL, Python, Power BI, and Tableau.

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