📊 HR & Project Data Analysis – Python Capstone Project

📌 Project Overview

Built a structured analytical dataset from multiple HR and project files using Python and Pandas. Cleaned, merged, and automated business rules with custom logic and aggregated project cost reports. Delivered an analysis-ready master dataset with 100% accuracy evaluation for problem-solving and structure.

This capstone project focuses on building a clean, structured, and analysis-ready dataset by processing multiple raw HR and project-related data files using Python.

The objective was to simulate real-world business data processing by performing data cleaning, transformation, merging, automation of HR rules, and cost aggregation using Pandas and NumPy.

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📂 Dataset Details

The project uses three primary datasets:

1. Employee Dataset

   • ID
   • Name
   • Gender
   • City
   • Age

2. Seniority Level Dataset

   • ID
   • Designation Level (1 = Highest, 4 = Lowest)

3. Project Dataset

   • ID
   • Project Name
   • Project Cost
   • Status (Finished / Ongoing / Failed)

All datasets were exported as CSV files and reloaded for structured processing.

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🧹 Data Cleaning & Processing

• Handled missing values in the Project Cost column using custom running-average logic (implemented via for-loop).
• Split name column into First Name and Last Name.
• Removed redundant columns and standardized data formats.
• Merged multiple datasets into a consolidated master DataFrame.
• Applied conditional logic to automate HR designation updates.
• Removed ineligible employees based on business constraints.

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⚙ Business Logic Implementation

• Added 5% performance bonus for successfully completed projects.
• Demoted designation level for failed projects.
• Promoted employees above age 29 using conditional rules.
• Removed employees exceeding designation level eligibility.
• Generated automated validation checks.

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📊 Aggregation & Reporting

• Calculated total project cost per employee.
• Created a new summary DataFrame: TotalProjCost.
• Filtered employees based on city name conditions.
• Produced a reliable master dataset ready for reporting and analysis.

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🛠 Tech Stack

• Python
• Pandas
• NumPy
• Jupyter Notebook
• CSV File Handling

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📈 Skills Demonstrated

Data Cleaning
Data Wrangling
Data Transformation
Conditional Logic Implementation
Loop-Based Computation
DataFrame Merging
Feature Engineering
Aggregation & GroupBy
Business Rule Automation
Analytical Dataset Creation

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🎯 Project Outcome

Successfully built a clean and structured master dataset from multiple raw inputs while automating business rules and reporting logic.

The project received 100% positive evaluation for:

• Accuracy
• Logical structuring
• Problem-solving
• Code organization

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👨‍💻 Author

Ankur Chowdhury
Aspiring Data Analyst | Python | SQL | Excel | Power BI