Unemployment Analysis with Python

CodeAlpha Data Science Internship — Task 2

Project Overview

This project involves a comprehensive Exploratory Data Analysis (EDA) of the unemployment rate in India, with a focus on the impact of the COVID-19 lockdown in 2020.

Key Visualizations

1. Time-Series Analysis: Tracking the sudden spike in unemployment during April-May 2020.
2. Regional Comparison: Identifying which states and regions (North, South, etc.) were most affected.
3. Sunburst Distribution: A hierarchical view of unemployment intensity across different geographical sectors.

Results & Insights

• The national unemployment rate saw a massive surge during the initial lockdown phase.
• Puducherry and Jharkhand showed some of the highest peak rates during the crisis.
• Urban areas generally faced more volatile fluctuations compared to rural regions.

Visual Proofs

Technologies Used

• Python (Pandas, NumPy)
• Plotly Express (For interactive visualizations)
• Matplotlib & Seaborn

Author

Syed Fazeel Ahmed — Data Science Intern at CodeAlpha