Predictive Analysis of Hospitality Industry to Global Economic Growth

Abstract

In this research study the researcher proposed predictive models on Hospitality Industries to Global Economic Growth.  The researcher used the 20 features based on customer personal and professional data and their travel history around the world. The total customer’s data 4888 are considered to develop this predictive model. The researcher used the supervised machine learning algorithms to classify the category of customers  and  Hospitality Industry to Global Economic Growth at world level. The researcher used the accuracy level of predictive model, recall  and precision which are given such as  Decision Tree Accuracy  0.855974%, Recall- 0.891156 , Precision 0.670330, Random Forest Accuracy0.891980%, Recall 1.000000, Precision 0.775076, Ada-Boost Classifier Accuracy 0.845336%, Recall 0.790244, Precision 0.685083, Gradient-Boost Classifier 0.900982%,Recall 1.000000, Precision 0.804805, XG-Boost Accuracy 0.892390%, Recall 1.000000, Precision 0.767647.  The researcher found that as far as the model is concerned, 90% accuracy, 72% Recall, and 74% Precision was obtained, which was the best scores out of all the models that were evaluated. While this model may be sufficient to obtain better than chance results for an initial marketing program, it is recommended that to Visit Us collect  data on Wellness Tourism package sales and customers as the product is being rolled out so that the model can be updated with current data get better predictions as soon as possible.