📊 SQL Layoffs Data Analysis

A complete SQL data cleaning and analysis project using MySQL to analyze global company layoffs from 2020-2023.

🎯 What This Project Does

This project takes messy, real-world layoffs data and cleans it up using SQL, then finds interesting patterns and insights about which companies, industries, and locations were most affected by layoffs.

📋 About the Data

Source: Layoffs Dataset from Kaggle

• Size: 2,300+ layoff records
• Time: 2020-2023
• Coverage: Companies worldwide
• Industries: Tech, Finance, Retail, Healthcare, and more

🧹 What I Did - Data Cleaning

Step 1: Remove Duplicates

• Found and removed duplicate records
• Used SQL window functions to identify copies

Step 2: Fix Data Problems

• Cleaned company names (removed extra spaces)
• Fixed industry names (made "Crypto" consistent)
• Fixed country names (removed dots from "United States.")
• Changed date format from text to proper dates

Step 3: Handle Missing Data

• Filled in missing industry info when possible
• Removed records that had no useful layoff numbers

📊 Key Findings

🏢 Top 5 Companies with Most Layoffs

🌍 Top 5 Locations with Most Layoffs

🏭 Top 5 Industries with Most Layoffs

📈 Biggest Single Layoff Event

💔 Companies That Shut Down Completely (100% Layoffs)

🛠️ SQL Skills Used

• Data Cleaning: Removing duplicates, fixing messy data
• Window Functions: ROW_NUMBER(), RANK(), SUM() OVER()
• Joins: Connecting tables to fill missing data
• Date Functions: Converting text to dates
• Aggregation: GROUP BY, SUM(), COUNT(), MAX()
• CTEs: Common Table Expressions for complex queries

📁 Project Files

📦 layoffs-sql-analysis
├── 📄 README.md                  (This file)
├── 📂 data/
│   └── 📄 layoffs.csv            (Original dataset)
├── 📂 sql/
│   ├── 📄 data_cleaning.sql      (Cleaning queries)
│   └── 📄 eda.sql               (Analysis queries)
└── 📂 images/
    ├── 📷 top_companies.png      (Results screenshots)
    ├── 📷 top_locations.png
    ├── 📷 top_industries.png
    ├── 📷 max_layoffs.png
    └── 📷 complete_shutdowns.png

🚀 How to Run This Project

What You Need

• MySQL installed on your computer
• MySQL Workbench (makes it easier)

Steps

1. Download the files from this repository
2. Open MySQL Workbench
3. Create a new database called world_layoff
4. Import the layoffs.csv file as a table called layoffs
5. Run the data_cleaning.sql file first
6. Run the eda.sql file second to see the analysis

💡 What I Learned

• How to clean messy real-world data
• Advanced SQL techniques for data analysis
• Finding business insights from raw data
• Documenting and presenting data projects

🎓 Why This Project Matters

This project shows I can:

• ✅ Take messy data and make it clean and usable
• ✅ Write complex SQL queries to find insights
• ✅ Present findings in a clear, understandable way
• ✅ Work with real business data to solve problems

✉️ Contact

Sankaran S

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This project is part of my data science portfolio, showing my SQL skills and ability to work with real-world data.