Organized Crime and GDP Analysis
This project was developed as part of CIS 9650 – Programming for Analytics at Baruch College.
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Overview
This project explores how economic strength (GDP per capita) relates to organized crime activity across 193 countries. We combined global crime and financial datasets, cleaned and merged them using Python, and visualized trends through Seaborn and Plotly. The goal was to identify patterns in criminal markets, resilience scores, and their economic impacts.
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What we built
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Cleaned and merged crime and GDP datasets using Python
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Standardized inconsistent country names and metrics across files
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Visualized key indicators using Seaborn and Plotly (e.g., criminality vs GDP, human trafficking)
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Built continent-based comparisons using FacetGrid
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Ran regression using statsmodels to uncover GDP influencers
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Outcome
The analysis revealed that resilience is the most significant factor influencing GDP.
Criminality levels don’t always correlate with GDP — especially in regions like Africa.
The model explained around 36% of GDP variation, highlighting the complex nature of crime and economy.
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