Systems and methods for training and executing a machine learning model for analyzing an electronic database
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.
1. A computer-implemented method for analyzing data using machine learning models, the method comprising:
receiving, by one or more processors, data associated with a request to add a new occasion to an electronic database, wherein:
the electronic database includes a plurality of occasions;
at least 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 determined based on the timing values and the substance values of the plurality of occasions;
the data associated with the request to add the new occasion is at least partially automatically generated by a first trained machine learning model; and
the first trained machine learning model is trained based on (i) training occasion data that includes information regarding one or more occasions associated with one or more electronic databases and (ii) progress value data including a progress value for each of the one or more electronic databases to learn relationships between the training occasion data and the progress value data, such that the first trained machine learning model is configured to use the learned relationships to generate a new occasion that will result in a second progress value that exceeds the first progress value;
receiving, by the one or more processors, data associated with the new occasion;
predicting, by a second trained machine learning model executed by the one or more processors, a timing value and a substance value for the new occasion,
wherein the second trained machine learning model is trained, based on (i) training occasion data that includes information regarding one or more occasions associated with one or more electronic databases and (ii) training value data that includes a prior timing value and substance value for each of the one or more occasions, to learn relationships between the training occasion data and the training value data, such that the second trained machine learning model is configured to use the learned relationships to determine the substance value and timing value for the new occasion in response to input of the data associated with a request to add a new occasion to the electronic database and data associated with the new occasion;
calculating, by the one or more processors, a second progress value for the electronic database based on the timing value and the substance value for the new occasion; and
upon determining that the second progress value exceeds the first progress value, causing, by the one or more processors, a graphical user interface to display a notification to add the new occasion to the electronic database.
2. The computer-implemented method of claim 1 , further comprising:
upon determining that the second progress value does not exceed the first progress value, automatically declining, by the one or more processors, the request to add the new occasion to the electronic database.
3. The computer-implemented method of claim 1 , further comprising:
receiving user input data indicating the substance value and the timing value associated with one or more of the plurality of occasions; and
generating, by the one or more processors, a tuned machine learning model, by further training the second trained machine learning model using (i) the user input data, and (ii) data associated with one or more of the plurality of occasions, such that the learned relationships are updated based on the user input data and the data associated with the one or more of the plurality of occasions.
4. The computer-implemented method of claim 1 , wherein the data associated with the request to add the new occasion is at least partially received by the graphical user interface.
5. The computer-implemented method of claim 1 , further comprising:
upon determining that the second progress value exceeds the first progress value, calculating, by the one or more processors, a coverage value based on attendee data for the new occasion; and
upon determining that the coverage value does not exceed a predetermined threshold, causing, by the one or more processors, the graphical user interface to display a notification to decline adding the new occasion to the electronic database.
6. The computer-implemented method of claim 1 , further comprising:
causing, by the one or more processors, the graphical user interface to display the electronic database, a selectable visual priorities tab, a selectable manage priorities tab, and a selectable trending data tab; and
upon a user selection of the selectable manage priorities tab, causing, by the one or more processors, the graphical user interface to display the timing value and the substance value for each occasion of the plurality of occasions.
7. The computer-implemented method of claim 6 , further comprising:
calculating, by the one or more processors, a plurality of progress values for the electronic database over a time period; and
upon a user selection of the selectable trending data tab, causing, by the one or more processors, the graphical user interface to display a graphic depicting the plurality of progress values and the time period.
8. The computer-implemented method of claim 7 , further comprising:
upon a user selection of the selectable visual priorities tab, causing, by the one or more processors, the graphical user interface to display graphical depictions of each of the plurality of occasions arranged according to the timing value and substance value of each of the plurality of occasions,
wherein the graphical depictions of each of the plurality of occasions are further arranging on a coordinate graph with an x-axis corresponding to substance values and a y-axis corresponding to timing values.
9. The computer-implemented method of claim 1 , wherein at least a portion of the plurality of occasions of the electronic database were generated based on one or more user inputs via the graphical user interface.
10. The computer-implemented method of claim 1 , wherein the information regarding the one or more occasions associated with one or more electronic databases further comprises information including attendee data, subject data, and time data for at least a portion of the one or more occasions associated with the one or more electronic databases.
11. A system for analyzing data using machine learning models, the system comprising:
at least one memory storing instructions; and
at least one processor executing the instructions to perform a process including:
receiving data associated with a request to add a new occasion to an electronic database, wherein:
the electronic database includes a plurality of occasions;
at least 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 determined based on the timing values and the substance values of the plurality of occasions;
the data associated with the request to add the new occasion is at least partially generated automatically by a first trained machine learning model; and
the first trained machine learning model is trained based on (i) training occasion data that includes information regarding one or more occasions associated with one or more electronic databases and (ii) progress value data including a progress value for each of the one or more electronic databases to learn relationships between the training occasion data and the progress value data, such that the first trained machine learning model is configured to use the learned relationships to generate a new occasion that will result in a second progress value that exceeds the first progress value;
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,
wherein the second trained machine learning model is trained, based on (i) training occasion data that includes information regarding one or more occasions associated with one or more electronic databases and (ii) training value data that includes a prior timing value and substance value for each of the one or more occasions, to learn relationships between the training occasion data and the training value data, such that the second trained machine learning model is configured to use the learned relationships to determine the substance value and timing value for the new occasion in response to input of the data associated with a request to add a new occasion to the electronic database and data associated with the new occasion;
calculating a second progress value for the electronic database based on the timing value and the substance value for the new occasion; and
upon determining that the second progress value exceeds the first progress value, causing a graphical user interface to display a notification to add the new occasion to the electronic database.
12. The system of claim 11 , wherein the process further includes:
upon determining that the second progress value does not exceed the first progress value, automatically declining the request to add the new occasion to the electronic database.
13. The system of claim 11 , wherein the process further includes:
receiving user input data indicating the substance value and the timing value associated with one or more of the plurality of occasions; and
generating a tuned machine learning model, by further training the second trained machine learning model using (i) the user input data, and (ii) data associated with the one or more of the plurality of occasions, such that the learned relationships are updated based on the user input data and the data associated with the one or more of the plurality of occasions.
14. The system of claim 11 , wherein the data associated with the request to add the new occasion is at least partially received by the graphical user interface.
15. The system of claim 11 , wherein the process further includes:
upon determining that the second progress value exceeds the first progress value, calculating a coverage value based on attendee data for the new occasion; and
upon determining that the coverage value does not exceed a predetermined threshold, causing a graphical user interface to display a notification to decline adding the new occasion to the electronic database.
16. The system of claim 11 , wherein the process further includes:
causing the graphical user interface to display the electronic database, a selectable visual priorities tab, a selectable manage priorities tab, and a selectable trending data tab; and
upon a user selection of the selectable manage priorities tab, causing, by the one or more processors, the graphical user interface to display the timing value and the substance value for each occasion of the plurality of occasions.
17. The system of claim 16 , wherein the process further includes:
calculating a plurality of progress values for the electronic database over a time period; and
upon a user selection of the selectable trending data tab, causing the graphical user interface to display a graphic depicting the plurality of progress values and the time period.
18. The system of claim 17 , wherein the process further includes:
upon a user selection of the selectable visual priorities tab, causing the graphical user interface to display graphical depictions of each of the plurality of occasions arranged according to the timing value and substance value of each of the plurality of occasions,
wherein the graphical depictions of each of the plurality of occasions are further arranging on a coordinate graph with an x-axis corresponding to substance values and a y-axis corresponding to timing values.
19. The system of claim 11 , wherein the information regarding the one or more occasions associated with one or more electronic databases further comprises information including attendee data, subject data, and time data for at least a portion of the one or more occasions associated with the one or more electronic databases.
20. A computer-implemented method for analyzing data using machine learning models, the method comprising:
receiving, by one or more processors, data associated with a request to add a new occasion to an electronic database, wherein:
the electronic database includes a plurality of occasions;
at least a portion of the plurality of occasions is associated with a timing value and a substance value; and
the electronic database is associated with a first progress value determined based on the timing values and the substance values of the plurality of occasions;
receiving, by the one or more processors, data associated with the new occasion;
training, by the one or more processors, a machine learning model to predict a timing value and a substance value for the new occasion,
wherein the machine learning model is trained, based on (i) training occasion data that includes information regarding one or more occasions associated with one or more electronic databases and (ii) training value data that includes a prior timing value and substance value for each of the one or more occasions, to learn relationships between the training occasion data and the training value data, such that the machine learning model is configured to use the learned relationships to determine the substance value and timing value for the new occasion in response to input of the data associated with a request to add a new occasion to the electronic database and data associated with the new occasion;
receiving, by the one or more processors, user input data indicating the substance value and the timing value associated with one or more of the plurality of occasions;
tuning, by the one or more processors, the machine learning model to generate a tuned machine learning model by using (i) the user input data, and (ii) data associated with the one or more of the plurality of occasions, such that the learned relationships are updated based on the user input data and the data associated with the one or more of the plurality of occasions;
predicting, by the one or more processors, a timing value and a substance value for the new occasion;
calculating, by the one or more processors, a second progress value for the electronic database based on the timing value and the substance value for the new occasion; and
upon determining that the second progress value exceeds the first progress value, causing, by the one or more processors, a graphical user interface to display a notification to add the new occasion to the electronic database.