Likhitha Veganti

Likhitha Veganti

Data Science Student at Seattle University

Departure Delays Analysis for United Airlines (R Project)

Link to my GitHub Repository Github Code.

Introduction

Air travel plays a vital role in connecting people and businesses across the globe. Ensuring the timely departure of flights is not only crucial for the efficiency of airline operations but also directly impacts customer satisfaction. In this project, we delve into the analysis of departure delays specifically for United Airlines (UA), one of the prominent carriers in the aviation industry. The objective of this analysis is to gain insights into the factors influencing departure delays for UA flights, using data provided by the nycflights13 package. By focusing on UA, we aim to provide tailored recommendations and strategies to enhance both operational efficiency and the overall travel experience for United Airlines passengers. The project addresses various aspects, including the impact of time of day, time of year, temperature, wind speed, precipitation, and visibility on departure delays for UA flights. Through statistical analyses and hypothesis testing, we aim to uncover patterns, correlations, and significant relationships that can guide decision-making within the airline industry. This project is not only a technical exploration but also an endeavor to communicate findings effectively to a non-technical audience. The ultimate goal is to present actionable insights for United Airlines, enabling them to make informed decisions to reduce departure delays and enhance the overall quality of service provided to their passengers.

Project Structure

The project is structured into different sections, each examining the impact of a specific factor on departure delays for UA flights. The report is tailored for a non-technical audience and includes visualizations and tables to illustrate key findings. The analysis incorporates hypothesis testing, particularly permutation tests, to support conclusions.

1. Time of Day Analysis

Findings:

  • Average departure delays for UA flights increase throughout the day.
  • Permutation test confirms a significant difference in departure delays between morning and evening hours for UA flights.

2. Time of Year Analysis

Findings:

  • UA flights experience variations in average departure delays based on different seasons.
  • Permutation tests reveal significant differences in departure delays between summer and other seasons for UA flights.

3. Temperature Analysis

Findings:

  • Departure delays for UA flights show an increasing trend with higher temperatures.
  • Permutation test supports the conclusion that departure delays are higher at temperatures above 50 degrees for UA flights.

4. Wind Speed Analysis

Findings:

  • No significant evidence found to suggest a relationship between wind speed and departure delays for UA flights.

5. Precipitation Analysis

Findings:

  • Higher precipitation values correlate with increased departure delays for UA flights.
  • Permutation test supports the conclusion of a significant difference in departure delays with high precipitation for UA flights.

6. Visibility Analysis

Findings:

  • Lower visibility values are associated with higher departure delays for UA flights.
  • Permutation test confirms a significant difference in departure delays between low and high visibility conditions for UA flights.

7. Conclusion

The analysis specifically tailored for United Airlines reveals key factors influencing departure delays. Time of day, time of year, temperature, precipitation, and visibility all play a role in UA flight delays. However, wind speed does not show a significant relationship with departure delays. The insights gained from this analysis can aid UA in improving efficiency and customer satisfaction.