This project delves into the intricacies of flight delays and cancellations in the United States, leveraging data from the US Bureau of Transportation Statistics spanning from July 2019 to July 2022. Using Python, I employ the Pandas library for robust data manipulation and analysis, while Matplotlib and Seaborn are harnessed for insightful visualisations.
The analysis aims to identify the airlines most affected by delays and cancellations over time. The project also investigates the underlying causes of these disruptions, with a particular focus on the impact of the COVID-19 pandemic on overall flight cancellations.
The project is structured into four key phases:
1. Data Exploration: Initial examination of the dataset to understand its structure and key attributes.
2. Data Transformation: Processing and transforming raw data into a suitable format for analysis.
3. Data Cleaning: Identifying and rectifying any inconsistencies or missing values to ensure data integrity.
4. Data Analysis: Conducting detailed analysis to uncover patterns, trends and insights.