Flight Data Analysis in Pyhton

Project Overview:

This project aims to analyze flight data to uncover insights and answer questions about various aspects of air travel. The specific focuses and questions being addressed can be further described here (e.g., analyzing flight delays, investigating trends in popular routes, studying the impact of cancellations).

DATA MODEL EXPLANATION DelayedFlightsnew.xlsx with Unique Flight code contains the following information: Year : 2018, Month, Day of Month, Day of Week, Departure Time (Actual and Scheduled), Arrival Time (Actual and Scheduled), Flight Number, Tail Number, Arrival Delay, Origin and Destination.

PROBLEM STATEMENT After examining the data, we will analyze popular flight routes Which routes experience the highest traffic volume, Analyzing arrival and departure status patterns What is the overall on-time performance of flights across different routes and airlines also outlines the goals and challenges of analyzing flight frequencies across months. This project aims to identify the best and worst performing airlines based on their historical flight cancellation rates, using data-driven analysis and visualizations, Identifying the Seasonality of Flight Delays across Month it shows a focused approach for analyzing and visualizing average flight speed across carriers

After exploring and analyzing the data, we have reached a conclusion regarding this project. In the year 2018, approximately 87% of flights were delayed, and the majority of them were in the months of July, August, and September. On the other hand, June and December are the best months to travel as the frequency of delayed flights is comparatively low. By analyzing the flight frequency graph, we can observe that air traffic is higher during these three months, which explains the reason behind the delayed flights in July, August, and September.