The primary goal of this project is to predict airline delays caused by various factors. Flight delays lead to negative impacts, mainly economical for commuters, airline industries and airport authorities. Furthermore, in the domain of sustainability, it can even cause environmental harm by the rise in fuel consumption and gas emissions.
Hence, these factors indicate how necessary and relevant it has become to predict the delays no matter the wide-range of airline meshes. To carry out the predictive analysis, which encompasses a range of statistical techniques from supervised machine learning and, data mining, that studies current and historical data to make predictions or just analyze about the future delays, with help of Regression Analysis.
Using “sklearn” library and linear regression we were able to train a model that predicts the flight delays.