In this project, I aim to showcase my proficiency in SQL by analyzing flight data. Utilizing a combination of SQL queries and data visualization techniques, I'll delve into various aspects of the provided flight dataset to extract meaningful insights.
The primary objective of this project is to demonstrate my SQL skills in handling and analyzing real-world data. By working with flight data, I intend to showcase my ability to write complex queries, perform data manipulation tasks, and generate informative visualizations.
The dataset used in this project comprises detailed information about flights, including departure and arrival times, airlines, airports, and other relevant attributes. This dataset serves as the foundation for my SQL analysis, allowing me to explore different facets of the aviation industry.
Data Source: The data is obtained from Kaggle. These datasets are provided by the U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics, which tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled, and diverted flights is published in DOT's monthly Air Travel Consumer Report and in this dataset of 2015 flight delays and cancellations.
The output of the Jupyter Notebook containing the SQL queries, data analysis, and visualizations is available here.
For this project, I'll primarily be using SQL to query and manipulate the dataset. Additionally, I'll leverage Python for any necessary data preprocessing tasks and to create interactive visualizations within a Jupyter Notebook environment.
This repository contains the following files and directories:
Data/: Directory containing the flight dataset files.Creation_DB_python.ipynb: Jupyter Notebook containing the python code that generate the DBSQL_code.ipynb: Jupyter Notebook containing the SQL queriesREADME.md: This file, providing an overview of the project.
To replicate and explore the analysis performed in this project:
- Clone this repository to your local machine.
- Navigate to the
SQL_code.ipynbfile. - Open the notebook using Jupyter Notebook or JupyterLab.
- Execute the cells sequentially to observe the SQL queries, analysis, and visualizations.
Through this project, I aim to demonstrate my proficiency in SQL and showcase my ability to derive actionable insights from complex datasets. By analyzing flight data, I hope to provide valuable perspectives on various aspects of air travel, ranging from punctuality and route optimization to airline performance and passenger preferences.
Feel free to explore the notebook and reach out with any questions or feedback!