Visualizing Flight Traffic Trends (2015–2019)

Analyze the busiest airline routes between 2015 and 2019 using Python (data prep) and Tableau (visualization).
We work from the large Airline Delay Analysis dataset and produce a clean, focused view of top routes and yearly trends.

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🔍 Overview

• Goal: Identify and visualize the top 5 busiest routes and how their flight counts changed year‑by‑year (2015–2019).
• Stack: Python (Pandas/NumPy/Matplotlib/Seaborn) for cleaning & aggregation, Tableau for final visuals.
• Reason: A compact visualization helps airlines & airports quickly see route importance and seasonality trends.

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