Abstract: Systems and methods provide real-time machine learning modeling, evaluation, and visualization. A computing system can receive input values for attributes of a new item listing. Concurrently, the system can analyze previous related item listings. The system can process the attributes and associated values of the new listing and previous listings to extract features and associated values. The system can apply the features of previous listings to machine learning algorithms to generate a machine learning model directed towards a target objective, such as maximizing a selling price or a selling probability for the new listing. The system can determine a likely outcome of the new listing by applying user-input values to the model and alternative outcomes by substituting one or more user-input values. If the alternative outcomes represent better results, then the system can present suggestions to revise the new listing to replace one or more user-input values with substitute values.

Abstract: Systems and methods provide real-time machine learning modeling, evaluation, and visualization. A computing system can receive input values for attributes of a new item listing. Concurrently, the system can analyze previous related item listings. The system can process the attributes and associated values of the new listing and previous listings to extract features and associated values. The system can apply the features of previous listings to machine learning algorithms to generate a machine learning model directed towards a target objective, such as maximizing a selling price or a selling probability for the new listing. The system can determine a likely outcome of the new listing by applying user-input values to the model and alternative outcomes by substituting one or more user-input values. If the alternative outcomes represent better results, then the system can present suggestions to revise the new listing to replace one or more user-input values with substitute values.