Approximately 30% of airlines’ operational expense is attributed to fuel cost, leading to direct impact on bottom-line because of increase in crude oil prices. By travelling at a reduced speed leads to saving fuel cost but at the same time reducing the on-time arrivals of flights, thereby leading to reduced customer satisfaction. Losing out loyal customers to the competitors is causing a huge dent into the profits, leading to bankruptcy of companies.

Using Data Science and Machine Learning, we can build a prediction model to predict on the fuel consumption across different stages of flight (taxing stage to landing stage). Model can also optimize solution to determine the appropriate speed of flights at all stages of the flight journey to give the best mileage
Predicting the on-time arrival of flights so that appropriate action is devised in case of delay so that cascading delay is not experienced in all the routes of that flight journey.