📉 Layoffs Data Cleaning

� Introduction

This project focuses on cleaning a dataset of global layoffs from major companies (2020-2023). Using SQL, I ensured the data is clean, standardized, and ready for analysis.

🔍 Check out the SQL queries used in this project: SQL Cleaning Scripts

🔎 Background

Handling real-world data often involves dealing with duplicates, inconsistencies, and missing values. This project applies a structured SQL-based cleaning process to prepare the layoffs dataset for meaningful analysis.

🛠 Data Cleaning Process

🔹 Removing Duplicates

• Used ROW_NUMBER() with PARTITION BY to identify duplicate records
• Created a staging table (layoffs_staging2) to store deduplicated data
• Ensured only unique entries were retained

🔹 Standardizing Data

• Fixed spelling inconsistencies in company names, locations, and industries
• Standardized text formats

🔹 Handling Missing Values

• Replaced missing values where necessary
• Removed rows with crucial missing data

🔹 Filtering Unwanted Rows

• Removed incomplete and erroneous records

⚙️ Tools Used

• 🛢 MySQL Workbench for SQL queries and data cleaning
• 📂 Kaggle for dataset sourcing

📚 What I Learned

• The importance of data cleaning in ensuring accuracy and reliability for analysis
• How to use SQL functions like ROW_NUMBER() to detect duplicates efficiently
• Best practices for handling missing data and maintaining data integrity
• The significance of standardizing values to avoid inconsistencies

🔍 Conclusion

Data cleaning is a crucial step in any data analysis project. By applying structured SQL techniques, we transformed raw, messy data into a reliable dataset suitable for insights and decision-making.

💡 Closing Thoughts

This project reinforced the importance of systematic data cleaning, preparing me for future data-driven projects. Next steps include automating the cleaning process and integrating it with visualization tools for deeper analysis.