🚀 HR Analytics Dashboard Project

From Messy Data → Clean Dataset → Interactive Power BI Dashboard

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📌 Overview

This project demonstrates a complete end-to-end data analytics workflow, transforming raw, unstructured employee data into a clean, analysis-ready dataset and building an interactive HR dashboard.

It showcases skills in:

• Data cleaning & preprocessing
• Data transformation using Python
• Data modeling & visualization
• Business insight generation

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🎯 Project Objective

To simulate a real-world business scenario where messy HR data is cleaned and transformed into meaningful insights that support decision-making.

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📂 Dataset

• Source: messy_employee_data.xlsx
• Sheet Used: Employee_Data_RAW

The dataset initially contained:

• Inconsistent column names
• Missing values
• Duplicate records
• Incorrect data formats
• Invalid entries (emails, phone numbers, etc.)

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🧹 Data Cleaning Process

1. Column Standardization

• Removed extra spaces
• Converted all column names to lowercase
• Replaced spaces with underscores
• Renamed inconsistent columns

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2. Removing Invalid Data

• Dropped completely empty rows
• Removed rows containing placeholder values (---)
• Removed duplicate employee records

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3. Data Type & Format Cleaning

👤 Employee ID

• Removed prefixes (EMP-)
• Converted to consistent format

🧑 Names

• Trimmed spaces
• Standardized capitalization

📧 Email

• Converted to lowercase
• Removed invalid entries

📞 Phone Numbers

• Extracted digits only
• Standardized format: XXX-XXX-XXXX

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4. Data Normalization

💰 Salary

• Removed symbols ($, ,)
• Converted to numeric format

📅 Hire Date

• Converted to standard date format (YYYY-MM-DD)

🏢 Department

• Standardized naming (title case)

🌍 Location

• Cleaned city names (e.g., "nyc" → "New York")
• Standardized country names (e.g., "us", "usa" → "USA")

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5. Data Validation Rules

📊 Performance Score

• Ensured values between 0–100

🎁 Bonus Percentage

• Handled missing and invalid values
• Defaulted to 0 where necessary

🎂 Age

• Valid range enforced (18–75)

⚧ Gender

• Standardized categories:

  • Male
  • Female
  • Non-Binary
  • Other
  • Not Specified

📌 Status

• Cleaned into:

  • Active
  • Inactive
  • On Leave
  • Terminated
  • Unknown

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💾 Output

• Cleaned dataset saved as: cleaned_employee_data.xlsx

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📊 Dashboard Features

The cleaned dataset was used to build an interactive HR Analytics Dashboard with the following insights:

🔝 Key Metrics (KPIs)

• Total Employees
• Average Salary
• Average Performance Score
• Average Bonus Percentage

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📈 Visual Insights

• Hiring trends over time
• Department-wise employee distribution
• Salary vs performance analysis
• Top job roles by employee count
• Gender distribution
• Geographic employee distribution

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🎛️ Interactivity

• Filters (Department, Gender, Country, Status)
• Drill-down capabilities
• Dynamic visuals responding to user input

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🧠 Key Insights Derived

• Identification of top-performing departments
• Salary distribution patterns across roles
• Workforce composition by region and gender
• Relationship between salary and performance
• Hiring trends over time

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🛠️ Technologies Used

• Python

  • pandas
  • re (regex)

• Excel

• Power BI

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⚙️ How to Run the Project

1. Install Dependencies

pip install pandas openpyxl

2. Run the Script

python your_script_name.py

3. Output

• Clean dataset will be generated automatically

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💡 Why This Project Matters

This project demonstrates the ability to:

• Handle real-world messy datasets
• Apply data cleaning best practices
• Transform raw data into business insights
• Build professional dashboards

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🚀 Future Improvements

• Add predictive analytics (employee attrition)
• Integrate real-time data sources
• Enhance dashboard storytelling
• Deploy dashboard to cloud platforms

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📬 Contact

If you're a recruiter or collaborator interested in this project, feel free to connect!

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