The global job market has seen significant fluctuations, with mass layoffs impacting various industries. This project performs an Exploratory Data Analysis (EDA) using SQL to uncover trends in layoffs across companies, industries, and countries.
🔹 Tech Stack: SQL (MySQL)
🔹 Dataset: Layoffs data sourced from public records
🔹 Scope: Identifying layoff patterns, trends, and economic impacts
Major spikes in layoffs observed during economic downturns (e.g., pandemic years 2022).
Consumer, Retail, and Finance sectors experienced the most significant workforce reductions.
Some of the biggest layoffs were from Fortune 500 companies and startups with high funding like Amazon, Google, Meta. Google during 2023 has the biggest contribution in layoffs.
The Post-IPO, Acquired, and C-Series Funding stage companies reported the highest number of layoffs.
Some startups that raised billions in funding still conducted mass layoffs due to economic pressures. Some of the Comapnies are Google,Amazon
The dataset contains transaction records of a retail store, including:
| ColumnName | Description |
|---|---|
| Company | Name of the Company |
| Location | Company Headquaters |
| Industry | Business Sector |
| Total_laid_off | Total No. Of Employees got laid off from company |
| Percentage_laid_off | Percentage of Employees got laid off from company |
| Date | The Specific Day on laid off announced |
| Stage | Development Phase (Series-A,B ; Post-IPO;Pre_IPO |
| Country | Nation where company founded |
| Funds_raised_millions | Total amount of money raised by Investors |
📌 This SQL-driven analysis provided valuable insights into layoff trends, helping us understand which industries, companies, and countries were most affected.
📌 The project highlights the impact of economic downturns, funding challenges, and market shifts on employment.
📌 LinkedIn : www.linkedin.com/in/devsarthak24
📌 Gmail : [email protected]
Want to contribute? Feel free to fork the repository and explore new insights! 🚀