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A practical SQL data cleaning project that standardizes and prepares the Global Layoffs dataset for analysis using SQL techniques like window functions, staging tables, and data quality checks.

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πŸ”Ή SQL Data Cleaning – Global Layoffs Dataset

This project illustrates practical SQL-based data cleaning using a publicly available Global Layoffs dataset from Kaggle. The focus is on transforming raw data into a standardized, consistent, and high-quality dataset suitable for analysis or reporting.

πŸ” What I Did

  • Preserved the raw dataset and cleaned using staging tables
  • Removed duplicate records using ROW_NUMBER()
  • Standardized company, industry, country, and date fields
  • Handled NULL and missing values logically
  • Filtered out rows lacking usable layoff information

πŸ› οΈ Tools & Concepts Used

  • SQL (MySQL-compatible)
  • Window functions (ROW_NUMBER)
  • Data standardization
  • Date transformation
  • Data quality validation

🎯 Outcome

A clean and consistent dataset that is ready for exploratory data analysis or further reporting tasks.

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A practical SQL data cleaning project that standardizes and prepares the Global Layoffs dataset for analysis using SQL techniques like window functions, staging tables, and data quality checks.

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