Cleaning a governmental data about labor

Description:

Performed end-to-end data cleaning and preprocessing on a government dataset related to labor statistics, ensuring accuracy, consistency, and readiness for analysis.

Technical Steps:

Data Inspection: Identified missing values, duplicates, and inconsistent formats across multiple columns.

Data Cleaning: Standardized categorical variables, handled null values, corrected outliers, and ensured uniform data types.

Transformation: Structured raw records into a well-organized format suitable for analysis and visualization.

Documentation: Created a data dictionary to improve dataset usability for future research and reporting.

Result: Produced a reliable and analysis-ready dataset to support labor market insights and policymaking.

Tools & Technologies: Python (Pandas, NumPy), Excel, SQL