Flight Delay Prediction using Machine Learning

Inspiration

The COVID-19 or Coronavirus pandemic is changing the way that business owners and people are thinking about all aspects of society and their daily activities, Along with its public health implications and disruptions of several sectors such as the economic sector, education and aviation sector particular, as it has become an obstacle for many countries from opening airlines and traveling to moving from goods from one country to another as to prevent the spread of the virus. Although the aviation industry (air transport including passenger & freight) represents a small share in the GDP, yet it is a major enabler of many of the economy activities in the countries and are an inter-industry connector and serving many segments; health, education, goods and services, rental and leasing services , manufacturing, petroleum and most importantly it has a shared ownership between the private and public , so the domino effect of the consequence of such sector are devastating economically and financially. The impact of the COVID -19 on aviation, tourism trade and economy were enormous since it started until this day. The World passenger traffic evolution has decreased by 60% in May 2020 due to flight cancellations and delays with the new regulations and rules issued. Business Owners and decision makes needs the urgency & necessity of studding the current situation and to be able to produce recovery plans and have the Indicative Scenarios and Paths Forward. New technologies and Data Analysis has spread worldwide without acknowledging borders, and are playing a main role of assuring, classifying and predicting the impacts of COVID-19 on aviation and airports flights in specific.

What it does

In this project we will tackle and address the important problems in the aviation sector, and its impact on the delay and cancelation of flights during the corona pandemic. The data set to be worked on are obtained from the department of transportation for the United States of America from January 2020 to June 2020. the Objective is to predict whether these flights are delayed or not using classification algorithms and to provide the business owners and decision makes with a prediction model to set their future plans to minimize the cost and increase the benefits and to overcome the big financial loses faced since the pandemic started.

How we built it

a) Data analysis b) Data collection c) Data processing d) Data modeling (build machine learning and deep learning models ) e) Planning and direction Finally, the outcomes of the Data Analytics Lifecycle can now be used by government officials and policy makers to make data-driven decisions about the most effective means to combat the pandemic. Better data analytics not only gives us better insights into flight delays, it also helps our policy makers make better-informed choices about which strategies are most effective.in addition to that this will be beneficial also for the travelers to understand aircraft departure and arrival dates in various cases, whether they are normal or there are delays if there are natural disasters or fluctuating weather conditions. using python Programming Language for building Machine learning and Deep Learning Models and Tableau for holistic view of aviation industry using storytelling dashboards