Abstract: A system may receive a cluster prediction requirement. The system may determine a first node conglomerate by sorting a first dataset into a first plurality of nodes. The system may determine a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement. The system may determine a second node conglomerate for each of the plurality of attributes. A node confidence score may be assigned to each of the second plurality of nodes. The system may determine a node graph based on a comparison between the first node conglomerate and the second node conglomerate. The node graph may be iteratively modified based on a node optimization threshold value to generate a harmonized node graph. The node optimization threshold value may be based on a map confidence score allotted to the node graph.

Abstract: A system may receive a cluster prediction requirement. The system may determine a first node conglomerate by sorting a first dataset into a first plurality of nodes. The system may determine a plurality of attributes by sorting a second dataset associated with the cluster prediction requirement. The system may determine a second node conglomerate for each of the plurality of attributes. A node confidence score may be assigned to each of the second plurality of nodes. The system may determine a node graph based on a comparison between the first node conglomerate and the second node conglomerate. The node graph may be iteratively modified based on a node optimization threshold value to generate a harmonized node graph. The node optimization threshold value may be based on a map confidence score allotted to the node graph.

Abstract: A computer-implemented method includes providing a graphical user interface (GUI) that allows a user working in a notebook computing system environment to generate a source notebook that includes a plurality of cells, each containing code, for the one or more visualizations each including a chart. The GUI allows the user to run the cells before creating the marketing analytic dashboard. The code used to generate the one or more visualizations into a custom dataframe is strung together into a custom dataframe, which is then paired with chart properties. Processing the pairs in an engine creates a preview of each visualization of the dashboard using campaign data of the customer. The marketing analytic dashboard is made available to the customer through a customer-facing marketing analytics application which performs the same processing as the engine to create visualizations on the dashboard using updated campaign data of the customer.