A study on passengers’ satisfaction levels on upcoming airport cities

customer’s satisfaction levels will be affected by a number of factors. In the result of multivariate model most of the variables are correlated. Dimension reduction technique refers a large number of correlated variables into fewer factors. In general, one of the most widely used dimension reduction techniques is factor analysis, in which large number of variables are reduced to a fewer number of variables using principal component analysis, an unsupervised algorithm that focuses on maximizing data point variation while ignoring cases and categories, resulting in the loss of some data.it functions as a classifier and a model for predicting customer satisfaction. The main goal of the paper is to use discriminate analysis to identify passenger satisfaction levels on factors related to the modes of transportation they use, classify them into the appropriate groups based on the cases, and build a predictive satisfaction model