Layoffs Data Cleaning Project

Cleaned and standardized a raw layoffs dataset using SQL for analysis and reporting purposes

Tools & Technologies Used:

• MySQL 8.0+
• SQL Queries (CTEs, Window Functions, JOINs)
• STR_TO_DATE(), TRIM(), LIKE

Dataset Source:

• Original: https://www.kaggle.com/datasets/swaptr/layoffs-2022
• Columns: company, location, industry, total_laid_off, percentage_laid_off, date, stage, country, funds_raised_millions

Step-by-Step Cleaning Process:

Step 1: Remove Duplicates

• Created layoffs_staging as a copy of original table.
• Added row_num using ROW_NUMBER() PARTITION BY company, industry, total_laid_off, percentage_laid_off, date.
• Created layoffs_staging2 with row_num column.
• Deleted all rows where row_num > 1.

Step 2: Standardize Data

• Trimmed whitespace from company names: UPDATE layoffs_staging2 SET company = TRIM(company);
• Standardized crypto-related industries to 'Crypto': UPDATE layoffs_staging2 SET industry = 'Crypto' WHERE industry LIKE 'cry%';
• Fixed country names: Updated "United States." → "United States" using TRIM(TRAILING '.' FROM country).
• Converted date from TEXT ('MM/DD/YYYY') to DATE type: UPDATE layoffs_staging2 SET date = STR_TO_DATE(date, '%m/%d/%Y'); ALTER TABLE layoffs_staging2 MODIFY COLUMN date DATE;

Step 3: Handle Null / Blank Values

• Changed empty strings in industry to NULL: UPDATE layoffs_staging2 SET industry = NULL WHERE industry = '';
• Filled missing industry values using self-JOIN on company: UPDATE layoffs_staging2 lay2 JOIN layoffs_staging2 lay1 ON lay1.company = lay2.company SET lay1.industry = lay2.industry WHERE (lay1.industry IS NULL OR lay1.industry = '') AND lay2.industry IS NOT NULL;

Step 4: Remove Unnecessary Columns

• Kept all columns (none removed); row_num was dropped after deduplication.

Final Table Structure (layoffs_staging2):

• company (TEXT)
• location (TEXT)
• industry (TEXT) — standardized
• total_laid_off (INT)
• percentage_laid_off (TEXT)
• date (DATE)
• stage (TEXT)
• country (TEXT) — cleaned
• funds_raised_millions (INT)

Sample Queries:

Top 5 Industries by Total Laid Off: SELECT industry, SUM(total_laid_off) AS total_laid_off FROM layoffs_staging2 WHERE total_laid_off IS NOT NULL GROUP BY industry ORDER BY total_laid_off DESC LIMIT 5;

Monthly Layoff Trends: SELECT YEAR(date) AS year, MONTH(date) AS month, SUM(total_laid_off) AS monthly_layoffs FROM layoffs_staging2 WHERE date IS NOT NULL GROUP BY year, month ORDER BY year, month;

Highest % Laid Off by Company: SELECT company, percentage_laid_off, total_laid_off, country FROM layoffs_staging2 WHERE percentage_laid_off IS NOT NULL ORDER BY CAST(percentage_laid_off AS DECIMAL(5,2)) DESC LIMIT 10;

Key Insights:

• Crypto had the highest total layoffs (>100k).
• US, India, Germany were top countries.
• Peak layoffs occurred Q1–Q2 2022.

Next Steps:

• Build dashboard in Tableau/Power BI.
• Join with economic data (interest rates, GDP).
• Add Python EDA notebook.

Acknowledgments:

• Kaggle for dataset
• Public reports from TechCrunch, Bloomberg, LinkedIn

License: Educational use only.

Contact: Email: your.email@example.com LinkedIn: linkedin.com/in/yourprofile GitHub: github.com/yourgithubusername

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