About project

Category: Data Analytics | Workforce Analytics

Tech Stack: Python, Pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook

GitHub: https://github.com/Nishigandha-Wankhade/Layoff_Trend_Analysis/blob/main/Layoff_Trend_Analysis_Project.ipynb

Project Overview

Conducted an in-depth workforce analytics project to analyze global layoff trends across companies, industries, and time periods. The project provides a clear understanding of employee reductions and their broader business implications, enabling stakeholders to make data-driven decisions related to employment policies, investments, and strategic planning.

Problem Statement

Large-scale layoffs impact:

• Workforce stability
• Company reputation
• Investor confidence
• Long-term business strategy

However, layoff data is often scattered and difficult to analyze holistically. The challenge was to transform raw layoff datasets into clear trends, patterns, and insights that explain when, where, and why layoffs occur.

Solution Overview

I built a structured analytics workflow that:

• Cleans and standardizes layoff datasets
• Analyzes layoffs across time, industries, and regions