Efficient flight schedules with utilizing Machine Learning prediction algorithms

While data is becoming more and more pervasive and ubiquitous in today’s life, businesses in modern societies prefer to take advantage of using data, in particular Big Data, in their decision-making and analytical processes to increase their product efficiency. Software applications which are being utilized in the airline industry are one of the most complex and sophisticated ones for which conducting of data analyzing techniques can make many decision making processes easier and faster. Flight delays are one of the most important areas under investigation in this area because they cause a lot of overhead costs to the airline companies on one hand and airports on the other hand. The aim of this study project is to utilize different machine learning algorithms on real world data to be able to predict flight delays for all causes like weather, passenger delays, maintenance, airport congestion etc in order to create more efficient flight schedules. We will use python as the programming language to create an artifact for our prediction purposes. We will analyse different algorithms from the accuracy perspective and propose a combined method in order to optimize our prediction results