AirFly

About This Project

AirFly is a comprehensive data science and business intelligence project conducting a massive exploratory analysis across 16 years of flight record histories. Spanning millions of data points, the pipeline reveals hidden seasonal patterns, correlates weather events with airport choke points, evaluates carrier efficiency, and provides actionable operational insights for airlines seeking to optimize arrival rates.

What's Included

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  Big Data Optimization — Parallel loading and processing using optimized Pandas and NumPy vectors
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  Exploratory Data Analysis — Deep trend discovery identifying the most delay-prone travel seasons
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  Weather Correlation Pipeline — Merges flight databases with regional climate records
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  Operational BI Visualization — Custom data maps, scatter distributions, and carrier dashboards
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  Outlier Detection Filters — Cleans reporting matrices of extreme weather anomalies

Project Impact

• •Processed over 10 million historic flight entries without memory bottlenecks
• •Pinpointed autumn and mid-summer storm windows as the highest sources of taxi delay
• •Uncovered carrier-specific arrival strategies that reduce layover times
• •Delivered publication-grade statistical visuals using Matplotlib and Seaborn