Repository for all the code used to obtain the models shown in the Milestones of PROY III, Data Science Course
The air travel industry is a really volatile market, where sudden events can lead to unexpected results in flight transactions. Companies of the sector must be ahead of the curve in predicting and understanding the impact of these events.
ForwardKeys, a data collection and analysis company, has provided us data about flight transactions in Paris during November 2014 and 2015. In this study we use association rules to discover the main repeating patterns among the data, specially those who are related to cancellations, to better explain and outline the clients profile of the market.
Furthermore, to better understand what determines the profile of a client, a linear discriminant analysis is performed to assign importance to attributes of our dataset.
Forwardkeys is a well-known company in charge of collecting travel data to help travel-dependent companies. Due to its innovative technology, Forwardkeys is able to get useful information such as ticketing data, airlines capacity or the overall market. Thus, we are aware of the change of numbers in Paris flights around November 2014 and November 2015.
Forwardkeys may find it interesting to study changes in flight bookings because it can provide useful information for other companies on how to act in a situation similar to the terrorist attacks. This company provides useful strategies that give an advantage when exploiting the air travel industry. In order to achieve it, they perform exhaustive data analysis to extract knowledge about the data.
Our main goal is to study and model flight trends to Paris and explain the irregularities some periods may have, in order to provide useful information to some sectors, such as catering, hospitality and travel agencies, to adopt specific strategies during certain time intervals, and even personalise custom offers to potential targets of clients.