Abstract
In this paper, we will present a novel approach for classifying and predicting airline passenger ratings for ancillaries using unsupervised learning techniques like K-Means and Expectation Maximization clustering. The datasets chosen for this study belong to Indian Low-Cost Airlines. The goal is to perform an empirical study and exploratory analysis for predicting the overall rating with respect to the individual ancillary services ratings. Our results suggest that while there is no clear pattern among the ratings that can lead to the overall rating from passengers, the factors like value for money can largely influence the overall rating. Low-cost airlines aggressively promote competitive fares and choice of ancillary services hence the passenger behavior towards the overall rating varies across the airline datasets.
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Sankaranarayanan, H.B., Rathod, V. (2018). A Novel Approach for Predicting Ancillaries Ratings of Indian Low-Cost Airlines Using Clustering Techniques. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-319-63673-3_24
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DOI: https://doi.org/10.1007/978-3-319-63673-3_24
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