MySQL Data Cleaning Project – Layoffs Dataset

Overview

This project focuses on cleaning and standardizing a real-world layoffs dataset using MySQL.
The goal was to transform raw, inconsistent data into a structured and analysis-ready format while preserving data integrity.

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Dataset

• Source: Layoffs data (raw CSV)
• Raw table: layoffs
• Cleaning tables: layoffs_staging, layoffs_staging2

The raw data contained:

• NULL and missing values
• Inconsistent text fields
• Unstandardized date formats
• Duplicate and messy records

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Tools Used

• MySQL
• MySQL Workbench

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Data Cleaning Steps

The following cleaning operations were performed using SQL:

• Removed duplicate records using window functions
• Standardized text fields (industry, stage, country, location)
• Converted dates into a uniform YYYY-MM-DD format
• Handled NULL values without introducing artificial data
• Ensured correct data types for numeric columns
• Created staging tables to preserve raw data

Note: NULL values were intentionally retained where data was genuinely unavailable to avoid introducing bias.

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Before vs After

Before Cleaning

Raw, uncleaned data with inconsistencies and missing values.

After Cleaning

Cleaned, standardized, and analysis-ready data.

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👤 Author

Shruti Singh

• GitHub: https://github.com/Shrutiji007
• LinkedIn: https://www.linkedin.com/in/shruti-singh-7a599030b