Methods and systems for geographically tracking nourishment selection

A method of geographically tracking nourishment selection, the method comprising:

• receiving, by a computing device, a location associated with a user's commute from a user device;
• determining by the computing device, a plurality of nourishment possibilities within the location;
• locating by the computing device, a performance character associated with the user,
• wherein the performance character contains a nourishment score and at least a behavior relating to nourishment possibilities;
• generating by the computing device, a user profile wherein the user profile comprises the location, the plurality of nourishment possibilities, and the performance character;
• generating by the computing device, a first machine-learning process, wherein the first machine-learning process is configured to utilize the user profile as an input, and output a plurality of corresponding nourishment possibility indexes, wherein generating the first machine-learning process comprises:
• providing a training set including a plurality of entries correlating user profile data to previous user selections of nourishment possibilities;
• and
• training the first machine-learning process as a function of the training data and a supervised machine-learning algorithm;
• generating by the computing device and the first machine learning process, the plurality of corresponding nourishment possibility indexes as a function of the user profile and the first machine-learning process;
• grading by the computing device and a second machine-learning process, the plurality of nourishment possibilities using the plurality of corresponding nourishment possibility indexes and;
• wherein the system is implemented using a shared-nothing architecture which caches nourishment data to enable scalability of the system.