Abstract: A method and system for performing a prediction of a travel intent of a trip are disclosed. The method includes receiving sets of ticket attribute information, identifying a set of data elements, parsing at least one data element, identifying a pattern among a select portion of the parsed at least one data element and setting the respective portion as a data element, performing hyper parameter tuning to reduce a number of data elements to be included in a training dataset among the data elements, and iteratively training a machine learning model to the training dataset and evaluating accuracy of output provided by the trained machine learning model with respect to a reference threshold predicting whether a trip is a business type or a leisure type based on ticket attribute information associated with the trip.

Abstract: A method and system for providing an airline agnostic dynamic cabin mapping are disclosed. The method includes gathering raw data from one or more data sources for capturing reservation booking designator (RBKD) values for various airlines and executing a fare mapping algorithm for generating a fare type variable. The method further includes compiling the raw data gathered and the fare type variable for generating unlabeled data set, and performing dimensionality reduction on the unlabeled data set for generating a set of input variables to input to a machine learning (ML) model. The ML model is then executed for generating cabin class clusters by inputting the set of input variables, creating percentile-based references to assign class service names for each of the cabin class clusters, and displaying a graphical representation of cabin class mapping for the various airlines based on the percentile-based references.

Abstract: A method and system for performing a prediction of a travel intent of a trip are disclosed. The method includes receiving sets of ticket attribute information, identifying a set of data elements, parsing at least one data element, identifying a pattern among a select portion of the parsed at least one data element and setting the respective portion as a data element, performing hyper parameter tuning to reduce a number of data elements to be included in a training dataset among the data elements, and iteratively training a machine learning model to the training dataset and evaluating accuracy of output provided by the trained machine learning model with respect to a reference threshold predicting whether a trip is a business type or a leisure type based on ticket attribute information associated with the trip.