🚨 Recent Layoffs Case Study 📊

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📌 Overview

This project presents a data-driven analysis of recent layoffs across industries using structured datasets and visual insights. The goal is to explore patterns, trends, and correlations in workforce reductions to inform stakeholders, analysts, and decision-makers about how layoffs evolved over time.

The repository includes:

• layoffs.csv – structured layoff dataset
• study.sql – SQL queries for exploration & aggregation
• study.ipynb – Jupyter Notebook with full analysis & visualizations

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🗂 Repository Structure

File / Folder	Description
layoffs.csv	Dataset with metadata (date, company, industry, region, roles affected).
study.sql	SQL queries for data aggregation & insights.
study.ipynb	Jupyter Notebook for cleaning, visualization, and analysis.
README.md	This file.
LICENSE	MIT License

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🎯 Objectives

1. Data Exploration – Explore layoffs across industries, regions & timelines
2. Trend Analysis – Identify peaks, sector hotspots & recurring patterns
3. Contextual Insights – Link data trends with economic & industry events
4. Reporting – Create visual & tabular insights for decision-making

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🔍 Key Insights

1️⃣ Layoff Peaks Correspond to Economic Shifts
Layoffs surge during economic downturns & industry contractions. Publicly reported layoffs, e.g., in tech, show significant reductions during slowdowns.

2️⃣ Tech Sector Shows High Volatility
Major tech companies announced large layoffs. Example: GitHub India laid off 140+ engineers during a strategic reorganization.
Source

3️⃣ Mass Layoffs Often Not Performance-Driven
Layoffs often happen due to restructuring or cost-cutting rather than individual performance.

4️⃣ Role of Sudden Economic Events
Startups shutting down, funding droughts, or global recessions trigger abrupt layoffs with minimal notice.

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🛠 Data & Methods

Data Cleaning

• Standardized dates & types
• Handled missing values
• Normalized categorical fields (industry, region)

SQL Exploration (study.sql)

• Aggregate layoffs by year/industry/company
• Identify top companies with maximum layoffs

Visualization & Statistics

• 📈 Time series of layoffs by quarter/year
• 📊 Bar charts by sector
• 🌍 Heatmaps for regional layoff density

All visualizations & analyses are in study.ipynb.

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🚀 How to Run

Prerequisites

pip install pandas matplotlib seaborn sqlalchemy jupyter

# Clone the repo
git clone https://github.com/neerajcodes888/Recent-Layoffs-Case-Study.git
cd Recent-Layoffs-Case-Study

# Open notebook
jupyter notebook study.ipynb


## 🌟 Future Extensions

- 📉 **Predictive Modeling:** Forecast layoff trends using time-series models  
- 📰 **Sentiment Analysis:** Correlate layoffs with news & social media trends  
- 📊 **Interactive Dashboards:** Real-time data exploration for stakeholders  

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## ⚖️ License

This project is licensed under the **MIT License** – see the `LICENSE` file.

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## 🎨 Fun Animations & Badges

![Animated](https://media.giphy.com/media/3oKIPwoeGErMmaI43C/giphy.gif)

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## 💡 Author

**Neeraj Kumar**  
- GitHub: [neerajcodes888](https://github.com/neerajcodes888)  
- LinkedIn: [Neeraj Kumar](https://www.linkedin.com/in/neeraj-kumar-9a75811a2/)  
- Email: neerajmail888@gmail.com