Methods and systems for calculating an edible score in a display interface

1. A system for calculating a score for an edible in a display interface, the system comprising:

a sensor, the sensor configured to detect hematological user data;

a computing device communicatively connected to the sensor configured to:

determine an edible of interest relating to a user, wherein determining the edible of interest comprises:

generating a query wherein the query is configured to search for edibles available within the user's geolocation; and

identifying a plurality of available edibles within the user's geolocation;

displaying the plurality of available edibles containing the edible of interest;

receive nourishment information relating to the edible of interest to the user;

generate a score, wherein generating the score further comprises:

training a score machine-learning process using edible training data applied to an input layer of nodes comprising a performance profile input, one or more intermediate layers, and an output layer of nodes comprising a score;

adjusting one or more connections and one or more weights between nodes in adjacent layers of the score machine-learning model to iteratively update the output layer of nodes by updating the zone strategy training data applied to the input layer of nodes; and

generating the score as a function of the score machine-learning process, wherein the score machine-learning process uses the performance profile and the nourishment information relating to the edible of interest as an input, and outputs the score;

determine a cost associated with the edible;

calculate a cost to score ratio as a function of the cost and the score; and

display the cost to score ratio within a display interface.

2. The system of claim 1 , wherein the computing device is configured to calculate a performance profile associated with the user.

3. The system of claim 2 , wherein the performance profile comprises a biological extraction.

4. The system of claim 2 , wherein the performance profile comprises a questionnaire.

5. The system of claim 1 , wherein the edible of interest is determined as a function of a user dietary habit.

6. The system of claim 1 , wherein nourishment information comprises a caloric input.

7. The system of claim 1 , wherein nourishment information comprises a nutrient input.

8. The system of claim 1 , wherein the computing device is further configured to: generate a dietary classifier, wherein the dietary classifier uses the edible of interest as an input, and outputs a dietary label using a classification process.

9. The system of claim 1 , wherein the computing device is further configured to:

calculate a cost to score ratio for a first edible;

calculate a second cost to score ratio for a second edible;

chart the first cost to score ratio as a function of the second cost to score ratio; and

display the first cost to score ratio as a function of the second cost to score ratio within the display interface.

10. A method of calculating a score for an edible in a display interface, the method comprising:

a sensor, the sensor configured to detect hematological user data;

determining an edible of interest relating to a user wherein determining the edible of interest comprises:

generating a query wherein the query is configured to search for edibles available within the user's geolocation; and

identifying a plurality of available edibles within the user's geolocation;

displaying the plurality of available edibles containing the edible of interest;

receiving nourishment information relating to the edible of interest to a user;

generating a score, wherein generating the score further comprises:

training a score machine-learning process using edible training data applied to an input layer of nodes comprising a performance profile input, one or more intermediate layers, and an output layer of nodes comprising a score;

adjusting one or more connections and one or more weights between nodes in adjacent layers of the score machine-learning model to iteratively update the output layer of nodes by updating the zone strategy training data applied to the input layer of nodes; and

generating the score as a function of the score machine-learning process, wherein the score machine-learning process uses the performance profile and the nourishment information relating to the edible of interest as an input, and outputs the score;

determining a cost associated with the edible;

calculating a cost to score ratio as a function of the cost and the score; and

displaying the cost to score ratio within a display interface.

11. The method of claim 10 further comprising calculating a performance profile associated with the user.

12. The method of claim 11 , wherein the performance profile comprises a biological extraction.

13. The method of claim 11 , wherein the performance profile comprises a questionnaire.

14. The method of claim 10 , wherein the edible of interest is determined as a function of a user dietary habit.

15. The method of claim 10 , wherein nourishment information comprises a caloric input.

16. The method of claim 10 , wherein nourishment information comprises a nutrient input.

17. The method of claim 10 further comprising:

generating a dietary classifier, wherein the dietary classifier uses the edible of interest as an input, and outputs a dietary label using a classification process.

18. The method of claim 10 further comprising:

calculating a cost to score ratio for a first edible;

calculating a second cost to score ratio for a second edible;

charting the first cost to score ratio as a function of the second cost to score ratio; and

displaying the first cost to score ratio as a function of the second cost to score ratio within the display interface.