https://github.com/shubhamgoyal575/hr-data-analysis-and-dashboard
https://github.com/shubhamgoyal575/hr-data-analysis-and-dashboard
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/shubhamgoyal575/hr-data-analysis-and-dashboard
- Owner: shubhamgoyal575
- Created: 2025-04-05T05:27:38.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-05T05:44:42.000Z (6 months ago)
- Last Synced: 2025-04-05T06:25:29.542Z (6 months ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# HR Analytics Dashboard – Power BI + EDA in Python
## 📊 Project Overview
This project provides a comprehensive analysis of HR data through two components:
- **Exploratory Data Analysis (EDA)** in a Jupyter Notebook using Python.
- **Interactive HR Analytics Dashboard** built in Power BI.
The goal is to uncover trends, patterns, and key HR metrics that help organizations make data-driven decisions about workforce management.
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## 🎯 Objectives
- Understand employee attrition, tenure, salary distribution, and demographic patterns.
- Build an interactive dashboard to monitor key HR metrics.
- Support strategic decisions in hiring, retention, and diversity.
## 📌 Key Components
### 📘 1. EDA in Jupyter Notebook
The `HR_Analytics_EDA.ipynb` notebook includes:
- Data Cleaning & Preprocessing
- Univariate and Bivariate Analysis
- Correlation Matrix
- Visualizations using Matplotlib and Seaborn
- Key insights on attrition, age, salary, and department trends
### 📊 2. Power BI Dashboard
The Power BI dashboard offers:
- Executive KPIs: Total Employees, Attrition Rate, Avg Tenure, Avg Salary
- Attrition Analysis by Age, Gender, Department, etc.
- Demographic Distribution
- Salary Slab wise Attrition
- Interactive slicers for dynamic filtering
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## ⚙️ Tools & Technologies Used
- Python (Pandas, Matplotlib, Seaborn)
- Jupyter Notebook
- Power BI Desktop
- Power Query Editor
- DAX (Data Analysis Expressions)
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## 📈 Insights You Can Gain
Which departments have the highest attrition?
Are there salary or age patterns among those who leave?
Does education or marital status impact attrition?
Gender-wise distribution across departments and job roles
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## 🧠 Future Enhancements
Add predictive modeling for attrition risk
Integrate with real-time HR data sources
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## 📞 Contact
For queries, suggestions, or collaboration opportunities, feel free to reach out:
Shubham
Linkedin: https://www.linkedin.com/in/shubham-goyal-95344a152/