Machine learning artificial intelligence system for predicting popular hours

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to operate system for generating a graphical user interface in a mobile device by performing operations comprising:

• transmitting, to a server, a request for occupancy information of a merchant;
• receiving, from the server, a transaction frequency array, the transaction frequency array comprising weekdays and aggregated weighted transactions associated with the weekdays, the weighted transactions being determined by multiplying a number of transactions by an associated transaction amount;
• modifying the transaction frequency array by applying a transformation to the aggregated transactions;
• generating a smoothed array by applying a kernel density estimate to the modified transaction frequency array;
• and
• generating a graphical user interface displaying a prediction window, the prediction window comprising a histogram displaying hours-based information in the smoothed array.

A computer implemented method for generating a graphical user interface in a mobile device, the method comprising:

• transmitting, to a server, a request for occupancy information of a merchant;
• receiving, from the server, a transaction frequency array, the transaction frequency array comprising weekdays and weighted aggregated transactions associated with the weekdays, the weighted aggregated transactions being determined by multiplying a number of transactions by an associated transaction amount;
• modifying the transaction frequency array by applying a transformation to the aggregated transactions;
• generating a smoothed array by applying a kernel density estimate to the modified transaction frequency array;
• and
• generating a graphical user interface displaying a prediction window, the prediction window comprising a histogram displaying hours-based information in the smoothed array.