Company Performance Data Analysis 📊

This project focuses on cleaning, merging, and analyzing employee performance data using Python and Pandas.

🛠️ Tools & Libraries Used

• Language: Python
• Libraries: Pandas (Data Manipulation), Matplotlib (Visualization)
• Environment: VS Code / Jupyter Notebook

📋 Project Overview

The goal was to integrate two separate datasets containing employee details and their performance scores/salaries. I performed extensive data cleaning to ensure the accuracy of the final insights.

🚀 Step-by-Step Process

1. Data Cleaning

• Employee IDs: Standardized IDs by removing 'EMP' prefixes and stripping whitespace to ensure consistency.
• Name Cleaning: Used Regex to remove numeric suffixes from names (e.g., 'Aisha1' -> 'Aisha') for a cleaner report.
• Handling Missing Values: - Missing Departments were filled with 'Unknown'.
  • Missing Salaries were imputed using the mean salary of the dataset.
  • Performance scores were converted to numeric values, treating 'Excellent' as 5.0.

2. Data Merging

• Performed an Outer Join on two datasets based on Employee_ID to ensure no employee record was lost during the integration process.

3. Sorting & Final Export

• Sorted the combined data by Employee_ID and exported it to a professional multi-sheet Excel workbook (Final_Company_Report.xlsx).

💡 Key Insights

• Top 5 Performers: Identified the highest-scoring employees for rewards and recognition based on their performance metrics.
• Departmental Salary Trend: Generated a visualization to compare average salaries across different departments, providing insights into compensation distribution.

📊 Visualization

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Created by Inamul Hasan