Python-based Flight Data Analysis

Analyze flight data using Python and Pandas. Explore flight duration distribution with Seaborn to gain insights for the aviation industry.

Table of Contents

• Introduction
• Dataset Overview
• Data Preprocessing
• Analysis and Insights
• Conclusion
• Future Work

Introduction

This project demonstrates a data analysis workflow in Python to explore and uncover insights from flight data. By utilizing the Pandas library for data manipulation and Seaborn for visualization, we delve into the distribution of flight durations.

Dataset Overview

The dataset contains flight details, including flight numbers, origin and destination airports, departure and arrival times, and distances. The data is loaded into a Pandas DataFrame for analysis.

Data Preprocessing

Minimal preprocessing is performed due to the clean dataset structure. 'DepartureTime' and 'ArrivalTime' columns are converted to datetime format for calculations involving time intervals.

Analysis and Insights

The primary focus is on visualizing the distribution of flight durations using Seaborn's histogram. The insights gained offer valuable information about flight patterns and characteristics.

Conclusion

This project underscores the effectiveness of Pandas and Seaborn in extracting insights from data. The findings have implications for informed decision-making in the aviation sector.

Future Work

Future analysis could involve exploring correlations between flight duration and factors such as distance and departure time. Advanced visualization techniques could provide a spatial perspective on flight routes.