Apparatus and methods for success probability determination for a user

An apparatus for success probability determination for a user, the apparatus comprising:

• at least a processor communicatively connected to a user device;
• and
• a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to:
• receive a plurality of criteria;
• generate indicators as a function of the criteria, wherein generating the indicators comprises:
• training, iteratively, a machine learning model using training data and a machine learning algorithm, wherein the training data includes criteria data correlated with indicator data;
• updating the training data with input and output results from the trained machine learning model and retraining the machine learning model with the updated training data using a feedback loop;
• generating the indicators using the retrained machine learning model, wherein the plurality of criteria are provided as an input to the retrained machine learning model to output the indicators;
• and
• determining a weight of the indicators using the machine learning model;
• receive user specifications, the user specifications comprising credentials of a user, wherein the user specifications comprise a video record, wherein the video record is processed to generate an audio vector and an image vector, and wherein the audio vector and the image vector are utilized to determine the credentials of the user, wherein utilizing the audio vector and the image vector to determine the credentials of the user further comprises concatenating the audio vector and the image vector into a resultant vector;
• classify the user specifications, using a classifier, to a performance category of a plurality of performance categories based on the user specifications and the indicators, wherein the classifier comprises a sub-classifier, and wherein classifying the user specifications further comprises:
• representing each category of the plurality of performance categories as performance vectors;
• using one or more measures of vector similarity to determine a degree of similarity between the resultant vector and each performance vector;
• and
• classifying the user specifications to the performance category associated with the performance vector with the highest degree of similarity to the resultant vector and the indicators;
• determine a success of placement for each of one or more previous entities in one or more previous positions, wherein the success of placement includes at least one commonality between the one or more previous entities and the user specifications, wherein the at least one commonality is modeled as a causative link between at least one of the plurality of criteria and the classified user specifications;
• and
• determine a relevancy of the classified user specifications as a function of qualifications of a job position and the success of placement for each of the one or more previous entities utilizing a second machine learning model.