https://github.com/sankaran-s2001/layoffs-sql-analysis
A complete SQL data cleaning and analysis project using MySQL to analyze global company layoffs from 2020-2023.
https://github.com/sankaran-s2001/layoffs-sql-analysis
data-science datacleaning eda kaggle layoffdata layoffs mysql sql
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A complete SQL data cleaning and analysis project using MySQL to analyze global company layoffs from 2020-2023.
- Host: GitHub
- URL: https://github.com/sankaran-s2001/layoffs-sql-analysis
- Owner: sankaran-s2001
- License: mit
- Created: 2025-09-04T15:39:41.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-10-08T04:43:42.000Z (9 months ago)
- Last Synced: 2025-10-08T06:24:11.408Z (9 months ago)
- Topics: data-science, datacleaning, eda, kaggle, layoffdata, layoffs, mysql, sql
- Homepage:
- Size: 413 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# 📊 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](https://www.kaggle.com/datasets/swaptr/layoffs-2022)
- **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**
[](https://github.com/sankaran-s2001) [](https://www.linkedin.com/in/sankaran-s21/) [](mailto:sankaran121101@gmail.com)
***
*This project is part of my data science portfolio, showing my SQL skills and ability to work with real-world data.*