Abstract:
"In this research we have examined the effect on No-Show of airline passengers with
multiple variables, where we have given the introduction on to the issues related to No-Show and the airlines struggles in the 21st century while maintaining the competitive
advantage staying competitive. We therefore not only primarily indicated the benefits and
the efficient matter how an airline can operate if the No-Show predictions can be classified
using ML.
This Research was mainly done to identify the No-Show by initially collecting the data via
an online survey and then utilizing the primary data, which is then used to build machine
learning via Colab. Where the personal features were examined to identify how these
features explain or effect the NO-SHOW related incidents in the airline industry. Where
these features were transformed and utilized to build the ML models of Random Forest,
Decision Tree, SVM, Logistic Regression and Naïve Bayes.
It was identified, during this study that the logistic regression outperformed the rest of the
algorithms used in this study with an accuracy of 78.13%. by indicating if we were to
develop a model it should be done using logistic regression as per the indicated accuracy
report.
The main struggle during this research would have been collecting higher number of
samples, for this study it was just above the minimum requirements. This resulted in
different methods of evaluation because we were unable to use the test train method event
though the test train is used for reference in this study the main validation method utilized
and appropriate for this study was cross validation. For the hypothesis testing the chi-square test was used to identify if there are any relationship between the variables. The test
indicated there are some significance relationships between few features like age, gender,
education and airline No-Show while other features at this study and sample indicate there
are no significant relationship between them."
