Machine learning-based methods and systems for modeling user-specific, activity specific engagement predicting scores

A method, comprising:

• obtaining, by a processor, from a plurality of digital resources user-specific, activity-specific data for at least one user from a plurality of users;
• wherein the user-specific, activity-specific data comprises:
• (i) at least one user-specific activity performance data regarding performance of at least one activity by the at least one user,
• (ii) at least one object data for at least one object that allows the at least one user to perform the at least one activity, and
• (iii) at least one user-specific personal data of the at least one user;
• training, by the processor, a neural network machine learning model to obtain a trained neural network machine learning model that is configured to predict:
• (i) a user-specific activity engagement labeling data for the at least one activity based on a first-type data pipeline on the at least one user-specific activity performance data;
• (ii) a plurality of user-specific, activity-specific data features based on a second-type data pipeline on the user-specific, activity-specific data;
• (iii) at least one user-specific activity-specific engagement predicting score, based on the user-specific activity engagement labeling data for the at least one activity and the plurality of user-specific, activity-specific data features;
• wherein the at least one user-specific activity-specific engagement predicting score is based on a plurality of propensities to engage a plurality of utilization actions associated with the at least one activity related to a line of credit by the at least one user of the plurality of users;
• wherein the training of the neural network machine learning model comprises:
• generating a plurality of datasets with a plurality of feature vectors associated with the user-specific activity-specific data for the plurality of users,
• inputting, into a neural network machine learning model, the plurality of datasets with the plurality of feature vectors associated with the user-specific activity-specific data for the plurality of users, and
• applying the plurality of feature vectors to an output associated with the neural network machine learning model;
• utilizing, by the processor, the trained neural network machine learning model to output the at least one user-specific activity-specific engagement predicting score based on the user-specific activity engagement labeling data for the at least one activity and the plurality of user-specific, activity-specific data features;
• and
• instructing, by the processor, based on the at least one user-specific activity-specific engagement predicting score, at least one computing device to present at least one user-specific activity-related action instruction that predicts at least one user-specific activity-related action to be performed with at least one user.