| CPC G06Q 10/06395 (2013.01) [G06Q 10/06398 (2013.01); H04L 63/126 (2013.01)] | 20 Claims |
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11. An apparatus for routine improvement for an entity, comprising:
at least a processor; and
a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to:
receive an entity profile, wherein receiving the entity profile comprises:
receiving an IP address associated with a known location of an entity and appended to a data packet containing entity profile data;
authenticating the entity by:
determining an actual time lapse between a computing device associated with the entity and the at least a processor;
comparing the actual time lapse to an expected time lapse, wherein the expected time lapse is determined based on entity location and data packet size; and
comparing the IP address to a plurality of stored flagged IP addresses previously identified by the at least a processor;
generate a first datum;
receive a second datum;
generate at least an entity-specific improvement recommendation as a function of the second datum and first datum utilizing a recommendation machine learning model which further comprises:
receiving a first training data, wherein the first training data correlates the first datum and second datum to an entity-specific improvement recommendation;
training, iteratively, the recommendation machine learning model using the first training data, wherein training the recommendation machine learning model includes retraining the recommendation machine learning model with feedback from previous iterations of the recommendation machine learning model; and
generating the at least an entity-specific improvement recommendation as a function of the first datum and the second datum using the trained recommendation machine learning model;
calculate an impact metric as a function of a plurality of attribute clusters, wherein calculating the impact metric comprises:
training an impact metric machine learning model using a second training data, wherein the second training data comprises the plurality of attribute clusters as inputs correlated to the impact metric as outputs, wherein training the impact metric machine learning model comprises:
iteratively updating the second training data as a function of adjusted inputs and desired outputs of the impact metric machine learning model;
retraining the impact metric machine learning model using the updated second training data; and
calculating the impact metric using the trained impact metric machine learning model; and
determine at least a user interface element as a function of the at least an entity-specific improvement recommendation;
transmit the at least an interface element to a display.
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