employee-data-analysis

🧹 Employee Data Cleaning and Analysis

This project focuses on cleaning and analyzing a dataset of employee records using Python, Pandas, NumPy, and Matplotlib.

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📁 Dataset Overview

• Contains employee details such as:
  • Name
  • Age
  • City
  • Salary
  • Email
  • Join Date
  • Employee ID

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✅ Cleaning Tasks Performed

1. Missing Data Handling

   • Filled missing values in Age, Salary, City, and Name.

2. Standardization

   • Standardized city names (NY → New York, etc.).
   • Corrected name formatting and removed special characters.
   • Converted Join Date to datetime.

3. Outliers and Validation

   • Removed unrealistic age and salary values.
   • Identified and flagged invalid emails.

4. Duplicates

   • Removed duplicate entries based on Name or Employee ID.

5. Feature Engineering

   • Extracted Join_Year from Join Date.
   • Created valid email formats using employee names.
   • Added a Data_Quality_Flag column for rows with issues.

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📊 Visualizations

• Bar chart showing average age and salary.
• Join trends per year with color-coded bars.

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Matplotlib

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📌 Author

Alok Bhateshwar
GitHub: @alokbhateshwar

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📜 License

This project is open-source and available under the MIT License.