Smart listing creation based on machine-learning analysis

A system comprising:

• one or more hardware processors;
• and
• a memory storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations comprising:
• training, by a machine learning system on a first server, a time series-based machine learning (ML) model that forecasts sales based on stored transaction histories, determining, by the machine learning system, one or more potential categories for a user based on custom preferences and previous analytic queries of the user;
• applying, by the machine learning system, high demand items in the one or more potential categories to the ML model, the ML model outputting a predicted sales count and a probability of predicted sales for each of the high demand items in the one or more potential categories;
• determining, by the machine learning system, items having a potential inventory gap by cross-checking current inventory with a list of one or more items having a probability outputted by the ML model that satisfies a probability threshold;
• for each item that has a potential inventory gap, causing presentation, on a user interface, of competitor sales data, the predicted sales count and the probability derived from the ML model, and editable fields displaying automatic listing thresholds that trigger automatic generation of a corresponding listing;
• based on the automatic listing thresholds being satisfied, triggering, by the machine learning system, a publication system on a second server to automatically generate the corresponding listing;
• continually storing new transactions, the new transactions indicating an actual sales count;
• retraining the ML model based on the new transactions and the actual sales count to account for ongoing trends;
• validating predictive accuracy of the ML model by comparing a predicted sales count outputted by the ML model with the actual sales count;
• and
• tuning one or more hyperparameters of the ML model based on the validating to improve generalization and the predictive accuracy of the ML model.

A machine-storage medium comprising instructions which, when executed by one or more hardware processors of a machine, cause the machine to perform operations comprising:

• training, by a machine learning system on a first server, a time series-based machine learning (ML) model that forecasts sales based on stored transaction histories;
• determining, by the machine learning system, one or more potential categories for a user based on custom preferences and previous analytic queries of the user;
• applying, by the machine learning system, high demand items in the one or more potential categories to the ML model, the ML model outputting a predicted sales count and a probability of predicted sales for each of the high demand items in the one or more potential categories;
• determining, by the machine learning system, items having a potential inventory gap by cross-checking current inventory with a list of one or more items having a probability outputted by the ML model that satisfies a probability threshold;
• for each item that has a potential inventory gap, causing presentation, on a user interface, of competitor sales data, the predicted sales count and the probability derived from the ML model, and editable fields displaying automatic listing thresholds that trigger automatic generation of a corresponding listing;
• based on the automatic listing thresholds being satisfied, triggering, by the machine learning system, a publication system on a second server to automatically generate the corresponding listing;
• continually storing new transactions, the new transactions indicating an actual sales count;
• retraining the ML model based on the new transactions and the actual sales count to account for ongoing trends;
• validating predictive accuracy of the ML model by comparing a predicted sales count outputted by the ML model with the actual sales count;
• and
• tuning one or more hyperparameters of the ML model based on the validating to improve generalization and the predictive accuracy of the ML model.