Prediction on the operational function features that has higher contribution on financial
profitability in Tourism industry
The purpose is to assess the impact of operating revenues and operating costs on profitability of
the Tourism industry and to develop a predictive model for Tourism profit and loss structure. By
using the accumulation of financial data generated, SEMMA can help achieve this goal through
the following process
In Sample stage, the dataset will be reviewed and divided into balanced similar partitions where
there will be a training group and a validation group.
In Explore stage, each variable will be reviewed using the StatExplore function of SAS
Enterpriser Miner to examine missing values, distribution, and statistical profiles.
In Modify stage, the dataset will not be assigned or transformed as there was no assumption
requirement for normality or linearity, and no missing values.
In Model stage, from the modify stage a model is prepared for predicting the operation that have
higher profit therefore the decision trees and logistic regression models will be developed to
predict the probability that a profit or loss would be observed. In order to compare the various
options that separate the individuals of each class
Finally, at the Asses stage the decision tree models will be compared with the outcome of the
regression model.