In-Depth Analysis of Global Internet Shutdown TrendsIn-Depth Analysis of Global Internet Shutdown Trends
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Global Internet Shutdown Analysis
What the project was about:
I analyzed global internet shutdown data to understand where, when, and why shutdowns happen. The goal was to uncover patterns across countries and time periods and highlight their impact on digital access.

What I did:
Collected and cleaned real-world dataset
Performed EDA (Exploratory Data Analysis)
Analyzed shutdowns by:
Country
Year/month
Reasons (political, security, protests, etc.)
Built Power BI dashboards to visualize trends
Identified high-risk regions and frequent shutdown patterns

Key Findings:
Certain countries showed repeated shutdown patterns over time
Most shutdowns were linked to:
Political instability
Government control during protests/elections
Some regions experienced long-duration shutdowns, affecting communication and businesses
There was a rise in shutdown frequency during specific global events

Challenges I faced:
Data cleaning issues
Missing values, inconsistent country names
Unstructured reasons
Different formats for shutdown causes → needed standardization
Time-based analysis
Converting dates and extracting meaningful trends
Visualization complexity
Making dashboards simple yet insightful

How I solved them:
Cleaned and standardized data using Python (Pandas)
Grouped and categorized shutdown reasons
Used time-series analysis for trends
Designed clear and interactive Power BI dashboards

Impact / Outcome:
Provided a clear view of global digital disruptions
Helped identify high-risk regions and key causes
Created dashboards that make complex data easy to understand
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Anil's avatar
This is a very insightful analysis on a highly relevant topic. I like how you handled data challenges like inconsistencies and unstructured inputs and still managed to build clear Power BI dashboards. The findings around political and regional shutdown patterns are particularly interesting.
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