Computer-based systems including machine learning models trained on distinct dataset types and methods of use thereof

A system comprising:

• at least one processor in communication with a non-transitory computer readable medium storing software instruction, wherein the at least one processor is configured to execute the software instructions which, upon execution, cause the at least one processor to perform steps to:
• collect historical product information for a plurality of products;
• wherein the product information for each product of the plurality of products comprises:
• i) one or more attributes, and
• ii) a date associated with each attribute of the one or more attributes;
• collect historical event information from one or more online sources;
• wherein the historical event information comprises a date associated with each historical event of a plurality of historical events;
• determine at least one impacted product of the plurality of products based at least in part on:
• the one or more attributes having an impact comprising a fluctuation exceeding a predetermined impact fluctuation value over a duration exceeding a predetermined impact fluctuation duration and
• the predetermined impact fluctuation duration being within at least one historical event of the plurality of historical events based at least in part on the date associated with each historical event of the plurality of historical events;
• generate at least one event-dependent products training dataset based at least in part on the product information of the at least one impacted product and the historical event information;
• wherein the at least one event-dependent products training dataset comprises the at least one impacted product of the plurality of products and the at least one event of the plurality of historical events associated with the at least one impacted product;
• determine an impact period for each historical event of the plurality of historical events based on the predetermined impact fluctuation duration;
• train an attribute prediction machine learning model based at least in part on the at least one event-dependent products training dataset to obtain a trained attribute prediction machine learning model;
• wherein the trained attribute prediction machine learning model comprises a regression layer formed of model parameters trained to correlate each event of the plurality of historical events to:
• i) the impact on each product of the plurality of products, and
• ii) the impact period of each product of the plurality of products.