System and method for performing airline agnostic cabin class mapping

A system for providing an airline agnostic dynamic cabin mapping, the system comprising:

• a memory;
• a display;
• and
• a processor,
• wherein the system is configured to perform:
• gathering raw data from one or more data sources for capturing reservation booking designator (RBKD) values for a plurality of airlines, wherein each of the plurality of airlines utilize a different mechanism for designating a plurality of cabin classes;
• determining airline data participation for each of the plurality of airlines, wherein the airline data participation is direct or indirect;
• executing a fare mapping algorithm for generating a fare type variable based on the raw data gathered;
• compiling the raw data gathered and the fare type variable for generating unlabeled data set
• performing dimensionality reduction on the unlabeled data set for generating a set of input variables to input to a machine learning (ML) model, wherein the ML model is an unsupervised K-means clustering algorithm model that calculates a distance between each data point and a centroid to assign a cabin cluster and assigns each data point to the nearest centroid;
• creating a first training set comprising a mapping between the RKBD values and the cabin class clusters;
• first training the ML model in a first stage using the first training set;
• creating a second training set comprising the first training set and a portion of the mapping between the RKBD values and the cabin class clusters that are incorrectly determined after the first stage of training;
• second training the first trained ML model in a second stage using the second training set;
• executing the second trained ML model for generating a plurality of cabin class clusters by inputting the set of input variables and partitioning the unlabeled data set into a predetermined number of clusters using centroid-based clustering calculations;
• creating percentile-based references to assign class service names for each of the plurality of cabin class clusters;
• and
• contemporaneously displaying, on a single screen of the display, a graphical representation of cabin class mapping for the plurality of airlines based on the percentile-based references.

A non-transitory computer readable storage medium that stores a computer program for providing an airline agnostic dynamic cabin mapping, when executed by a processor, causing a system to perform a plurality of processes comprising:

• gathering raw data from one or more data sources for capturing reservation booking designator (RBKD) values for a plurality of airlines, wherein each of the plurality of airlines utilize a different mechanism for designating a plurality of cabin classes;
• determining airline data participation for each of the plurality of airlines, wherein the airline data participation is direct or indirect;
• executing a fare mapping algorithm for generating a fare type variable based on the raw data gathered;
• compiling the raw data gathered and the fare type variable for generating unlabeled data set;
• performing dimensionality reduction on the unlabeled data set for generating a set of input variables to input to a machine learning (ML) model, wherein the ML model is an unsupervised K-means clustering algorithm model that calculates a distance between each data point and a centroid to assign a cabin cluster and assigns each data point to the nearest centroid;
• creating a first training set comprising a mapping between the RKBD values and the cabin class clusters;
• first training the ML model in a first stage using the first training set;
• creating a second training set comprising the first training set and a portion of the mapping between the RKBD values and the cabin class clusters that are incorrectly determined after the first stage of training;
• second training the first trained ML model in a second stage using the second training set;
• executing the second trained ML model for generating a plurality of cabin class clusters by inputting the set of input variables and partitioning the unlabeled data set into a predetermined number of clusters using centroid-based clustering calculations;
• creating percentile-based references to assign class service names for each of the plurality of cabin class clusters;
• and
• contemporaneously displaying, on a single display, a graphical representation of cabin class mapping for the plurality of airlines based on the percentile-based references.