A predictive Analysis of airline passengers demand between (Eco and Business class) during and post economic recession using machine leaning algorithm

Arinze Jude, Ugwuanyi (2020) A predictive Analysis of airline passengers demand between (Eco and Business class) during and post economic recession using machine leaning algorithm. Masters thesis, Dublin, National College of Ireland.

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Abstract

Naturally, the airline industry is competitive, a strategic process is needed to make more profit, understanding customers demand will increase the service performance, Economy or Business class airline are the most often use airline, the requirements and price differ, the need to find out the root cause of demand imbalance has been a challenge, this research aims to predict with accuracy business class users and the major factor the effect passenger preference, it is important in this is economic downturn to know the factors that influence the demands of a passenger. This research applied a machine learning approach to give detail analysis of the factors that contribute to the customers’ demand. The application of data science on IBM survey data, help to predictive demand on Business class flight from the attributes, removing some attributes that do not relate or contribute was done using Principal Component Analysis (PCA) it helps improves the accuracy performance of the model. An exploratory data analysis (EDA) performed to identify the significant factors, machine learning algorithm (KNearest Neighbour (KNN), Random Forest, Logistic Regression and Decision Tree (C50) was employed for the prediction. The machine learning model performance was evaluated using Recall, Accuracy, Precision, and FI-score parameters, the best result was gotten from KNN when K=1 with an accuracy of 91.3%.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Dan English
Date Deposited:	21 Jan 2021 11:38
Last Modified:	21 Jan 2021 11:38
URI:	https://norma.ncirl.ie/id/eprint/4425

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