Abstract: A method of for analyzing data using machine learning models comprising: receiving data associated with a request to add a new occasion to an electronic database, wherein: the electronic database includes a plurality of occasions; a portion of the plurality of occasions is associated with a timing value and a substance value; the electronic database is associated with a first progress value; and the data associated with the request to add the new occasion is at least partially automatically generated by a first trained machine learning model; receiving data associated with the new occasion; predicting, by a second trained machine learning model, a timing value and a substance value for the new occasion; calculating a second progress value based on the timing value and the substance value for the new occasion; and causing a graphical user interface to display a notification to add the new occasion to the electronic database.

Abstract: A method of for analyzing data using machine learning models comprising: receiving data associated with a request to add a new occasion to an electronic database, wherein: the electronic database includes a plurality of occasions; a portion of the plurality of occasions is associated with a timing value and a substance value; the electronic database is associated with a first progress value; and the data associated with the request to add the new occasion is at least partially automatically generated by a first trained machine learning model; receiving data associated with the new occasion; predicting, by a second trained machine learning model, a timing value and a substance value for the new occasion; calculating a second progress value based on the timing value and the substance value for the new occasion; and causing a graphical user interface to display a notification to add the new occasion to the electronic database.