Disclaimer - No copyright infringement intended.
Dataset link in the comments.
In this project, I learned how to effectively analyze airline data using a combination of SQL and Python. The project involved several key libraries and techniques:
SQLite3: Utilized for database management and querying various tables such as aircrafts_data, airports_data, and bookings.
Pandas: Employed for data manipulation and analysis, allowing me to read SQL queries directly into DataFrames for further exploration.
NumPy: Used for numerical operations to enhance data processing capabilities.
Matplotlib & Seaborn: Leveraged these visualization libraries to create insightful graphs that depict relationships like aircraft models versus their ranges and fare distributions across different classes.
Additionally, I implemented data cleaning techniques, such as JSON parsing for extracting meaningful information from complex data structures, and utilized grouping and aggregation functions to summarize ticket sales and revenue over time. This project not only honed my technical skills but also deepened my understanding of the aviation industry through data analysis.
#dataanalysis #pythonprogramming #sql #datascience #airlineindustry
