Data Analytics for Monitoring the Satisfactory Parameters of Airline Passengers using Machine Learning Algorithms in Python
Shaik Javed Parvez1, Arun Sahayadhas2
1Shaik Javed Parvez. Assistant Professor, School of Engineering, Department of Computer Science Engineering, Vels Institute of Science Technology and Advanced Studies (VISTAS), Chennai. India.
2Arun Sahayadhas*, Associate Professor, School of Engineering, Department of Computer Science Engineering, Vels Institute of Science Technology and Advanced Studies (VISTAS), Chennai. India.
Manuscript received on December 19, 2019. | Revised Manuscript received on December 27, 2019. | Manuscript published on January 10, 2020. | PP: 1231-1235 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8677019320/2020©BEIESP | DOI: 10.35940/ijitee.C8677.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An effective representation by machine learning algorithms is to obtain the results especially in Big Data, there are numerous applications can produce outcome, whereas a Random Forest Algorithm (RF) Gradient Boosting Machine (GBM), Decision tree (DT) in Python will able to give the higher accuracy in regard with classifying various parameters of Airliner Passengers satisfactory levels. The complex information of airline passengers has provided huge data for interpretation through different parameters of satisfaction that contains large information in quantity wise. An algorithm has to support in classifying these data’s with accuracies. As a result some of the methods may provide less precision and there is an opportunity of information cancellation and furthermore information missing utilizing conventional techniques. Subsequently RF and GBM used to conquer the unpredictability and exactness about the information provided. The aim of this study is to identify an Algorithm which is suitable for classifying the satisfactory level of airline passengers with data analytics using python by knowing the output. The optimization and Implementation of independent variables by training and testing for accuracy in python platform determined the variation between the each parameters and also recognized RF and GBM as a better algorithm in comparison with other classifying algorithms.
Keywords: Random Forest Algorithm, Gradient Boosting Machine, Decision Tree, Satisfactory Attributes, Python.
Scope of the Article: Machine Learning