Australian Domestic Flight Reliability Analysis

Executive Summary

In the high-stakes world of aviation, reliability is currency. This project analyzes historical data from Australia's top 5 domestic flight routes to identify operational bottlenecks, seasonal disruption patterns, and airline performance benchmarks.

The goal is to provide data-driven recommendations for travelers and operational teams to minimize the impact of flight cancellations.

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Key Business Insights

1. The "Winter Crunch" (Seasonality Analysis)

• Finding: Flight cancellations spike significantly during the Australian winter months (June & July).
• Impact: The Sydney ↔ Melbourne route is the most heavily affected, with cancellation rates reaching up to 11% due to weather conditions.
• Strategic Recommendation: Operational buffers (standby crews/aircraft) should be increased by 15% during Q2/Q3 on southern routes.

2. Airline Reliability Benchmarking

• Top Performers: Qantas and Virgin Australia demonstrate the highest stability with minimal variance in schedule adherence.
• High Variance: Jetstar and Tigerair show a wider spread in cancellation rates, indicating higher operational risk.
• Traveler Advice: For time-sensitive business travel, legacy carriers (Qantas/Virgin) are the statistically safer choice.

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Visualizations

1. Cancellation Distribution

Most flights run smoothly, but the "long tail" of outliers reveals days of major disruption.

2. Airline Performance Comparison

A side-by-side comparison showing Qantas's stability vs. Jetstar's variability.

3. Heatmap of Disruption (Route vs. Month)

The "Red Zone" in June/July clearly marks the winter operational challenge.

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Tech Stack & Methodology

• Python: Core programming language.
• Pandas: Data manipulation, pivot tables, and aggregation.
• Seaborn & Matplotlib: Advanced data visualization and statistical graphics.
• Jupyter Notebook: Interactive development environment.

Key Techniques Used:

• Data Cleaning & Preprocessing.
• Feature Engineering (Calculated Cancellation_Rate).
• Exploratory Data Analysis (EDA).
• Statistical Analysis (Distribution & Outlier Detection).

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How to Run

1. Clone the repository:

   git clone [https://github.com/DiaaShousha/Australian-Flight-Reliability-Analysis.git](https://github.com/DiaaShousha/Australian-Flight-Reliability-Analysis.git)
   

2. Install dependencies:

   pip install pandas seaborn matplotlib
   

3. Open the notebook:

   jupyter notebook Australian_Flight_Reliability_Analysis.ipynb
   

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Author

Diaa Shousha

• Role: Data Analyst & AI Engineer
• Focus: Turning raw data into strategic business stories.
• LinkedIn Profile