Chapter $1$

Problem understanding and project planning :

$1\mathrm{.}1$ Introduction

Flight execution on time is a significant proportion of an air terminal's and carrier's administration greatness, the forecast of flight defer length over a foreordained skyline can assist carriers with taking on emergency courses of action straightaway and dispose of missed income and punishment costs $[1].$ Airlines, passengers, and airports all suffered greatly from flight delays, whether they were for arrival or departure. Flight appearance delays are one of the main purposes behind business carriers' misfortunes and traveler grumblings $[2].$ In $2019,$ the Government Avionics Organization FAA guessed that postponements would cost $ $33$ billion every year $[3].$ It likewise impacts the climate since it raises petroleum emanations through fuel utilization, to save fuel, aircrafts are additionally continuously searching for new innovation and further developing flight techniques $[4].$ The airport, on the other hand, benefits from flight departure delay prediction by allocating unused airport capacity and airspace to other airlines, providing customers with reliable travel plans, and improving the performance of airline service by changing schedules in advance $[5].$ Generally the appearance and takeoff defer expectation gives air terminal organizers compelling staff responsibility bend since flight time changes in genuine time while they rely upon flight plan time when arranged

$1\mathrm{.}2$ Problem definition :

Flight delay is a serious and a recurring problem that causes various problems ranging from overwhelming airport employees to causing unnecessary anxiety to passengers $.$ Thus, we need ways to mitigate this problem by predicting the expected time of delay.

$1\mathrm{.}3$ The Recommended Solution

The recommended solution for processing aircraft delay data using data science techniques is to utilize deep learning models that can analyze the data and discover patterns and trends related to delays. These models can be used to predict the occurrence of delays and identify the influencing factors such as weather conditions, air traffic congestion, and technical faults.

For example, deep learning techniques can be employed to analyze historical data on aircraft delays and build predictive models. These models can identify temporal patterns and factors affecting delays and forecast their occurrence in the future. This can help in making strategic decisions to improve air transportation performance and reduce delays.

Additionally, classification techniques in data science can be used to classify aircraft, airports, and airlines based on their performance in dealing with delays. These classifications can be utilized to enhance planning, organization, and efforts to improve air transportation performance.

Ultimately, the recommended solutions should be selected and implemented based on the specific circumstances and requirements of each aircraft delay problem. Integration of data science techniques with other domains and collaboration among airlines, airports, and relevant entities are crucial to achieving the best results in reducing delays and enhancing air transportation performance.

$1\mathrm{.}4$ Project Scope and objectives

The project scope includes developing a system that uses historical data and machine learning algorithms to predict the expected time of delay for flights. The objectives of the project are to develop a system that can accurately predict the expected time of delay for flights, help airports and airlines to better manage their resources and passengers' expectations, and ultimately improve the overall efficiency and effectiveness of the air travel industry.

$1\mathrm{.}5$ Target User

and pilot

$1\mathrm{.}6$ Methodology

$1\mathrm{.}7$ Project Plan $($The Gantt Chart $)$

$1\mathrm{.}9$ Tools & requirements

$1\mathrm{.}10$ Conclusion