Method and system for data classification to generate a second alimentary provider

1. A method of data classification to generate a second alimentary provider, the method comprising:

inputting, by a computing device, a request for an alimentary combination from a user,

wherein the alimentary combination is prepared by a first alimentary provider;

determining, by the computing device, the alimentary combination is not available at the first alimentary provider;

classifying, by the computing device, a plurality of alimentary providers, wherein classifying further comprises:

receiving alimentary provider training data, wherein the alimentary provider training data includes a plurality of data entries, each of the data entries including one or more elements of the each of the plurality of alimentary providers and one or more correlated alimentary combinations;

training an alimentary provider classifier as a function of the alimentary provider training data; and

outputting the plurality of alimentary providers as a function of the alimentary provider classifier and the request for the alimentary combination;

determining, by the computing device, an alimentary provider score of a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a first machine-learning process, the machine learning process trained by training data correlating alimentary provider score to alimentary combinations, wherein each of the plurality of second alimentary combinations comprises a replacement for the alimentary combination prepared by the first alimentary provider;

selecting, by the computing device, a second alimentary provider from the plurality of alimentary providers as a function of the alimentary provider score; and

outputting, by the computing device, the second alimentary provider to the user.

2. The method of claim 1 , wherein the plurality of alimentary providers serves an identical cuisine type as the first alimentary provider.

3. The method of claim 1 , further comprising:

filtering the plurality of alimentary providers as a function of the type of cuisine.

4. The method of claim 1 , further comprising:

filtering the plurality of alimentary providers as a function of the user preferences.

5. The method of claim 1 , further comprising:

training an alimentary combination classifier as a function of second alimentary combination training data; and

identifying the second alimentary combination as a function of the alimentary combination classifier and the requested alimentary combination.

6. The method of claim 1 , wherein selecting a second alimentary provider further comprises:

receiving a geographical parameter of the first alimentary provider;

receiving geographical parameter training data;

training a geographical parameter classifier as a function of geographical parameter training data; and

identifying a second alimentary provider as a function of geographical parameter training data and first alimentary provider.

7. The method of claim 1 , further comprising:

determining that the alimentary combination is not available at the first alimentary provider.

8. The method of claim 1 , wherein outputting the second alimentary provider to the user, further comprises:

confirming the selection of the alimentary provider by receiving a message from the user; and

transmitting the request for the requested alimentary combination to the second alimentary provider.

9. The method of claim 1 , further comprising:

rejecting the selection of the alimentary provider by receiving a message from the user; and

transmitting a second selection of the second alimentary provider to the user.

10. The method of claim 1 , further comprising:

determining a projected delivery time for the second alimentary combination from the second alimentary provider by:

training a second machine-learning process using training data correlating past delivery times data with delivery parameters;

receiving at least a delivery parameter; and

outputting the projected delivery time as a function at the at least delivery parameter using the second machine-learning process.

11. A system of outputting a second alimentary provider, the system comprising:

a computing device configured to:

input a request for an alimentary combination from a user, wherein the alimentary combination is prepared by a first alimentary provider;

determine the alimentary combination is not available at the first alimentary provider;

classify a plurality of alimentary providers, wherein classifying further comprises:

receiving alimentary provider training data, wherein the alimentary provider training data includes a plurality of data entries, each of the data entries including one or more elements of the each of the plurality of alimentary providers and one or more correlated alimentary combinations;

training an alimentary provider classifier as a function of the alimentary provider training data; and

outputting the plurality of alimentary providers as a function of the alimentary provider classifier and the request for the alimentary combination;

compute an alimentary provider score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a machine-learning process, the machine learning process trained by training data correlating alimentary provider score to alimentary combinations, wherein each of the plurality of second alimentary combinations comprises a replacement for the alimentary combination prepared by the first alimentary provider;

select a second alimentary provider from the plurality of alimentary providers as a function of the alimentary provider score; and

output the second alimentary provider to the user.

12. The system of claim 11 , wherein the plurality of alimentary providers serves an identical cuisine type as the first alimentary provider.

13. The system of claim 11 , wherein computing device is further configured to:

filter the plurality of alimentary providers as a function of the type of cuisine.

14. The system of claim 11 , wherein computing device is further configured to:

filter the plurality of alimentary providers as a function of the user preferences.

15. The system of claim 11 , wherein computing device configured to select a second alimentary combination is further configured to:

train an alimentary combination classifier as a function of second alimentary combination training data;

identify the second alimentary combination as a function of the alimentary combination classifier and the requested alimentary combination.

16. The system of claim 11 , wherein computing device configured to select a second alimentary combination is further configured to:

receive a geographical parameter of the first alimentary provider;

receive geographical parameter training data;

train a geographical parameter classifier as a function of geographical parameter training data; and

identify a second alimentary provider as a function of geographical parameter training data and first alimentary provider.

17. The system of claim 11 , wherein computing device is further configured to:

determine that the alimentary combination is not available at the first alimentary provider.

18. The system of claim 11 , wherein the computing device configured to output the second alimentary provider to the user is further configured to:

confirm the selection of the alimentary provider by receiving a message from the user; and

transmit the request for the requested alimentary combination to the second alimentary.

19. The system of claim 11 , wherein computing device configured to output the second alimentary provider to the user is further configured to:

reject the selection of the alimentary provider by receiving a message from the user; and

transmit a second selection of the second alimentary provider to the user.

20. The system of claim 11 , wherein the computing device is further configured to:

determine a projected delivery time for the second alimentary combination from the second alimentary provider by:

training a second machine-learning process using training data correlating distance from the second alimentary provider to the user;

receiving at least a delivery parameter; and

computing the projected delivery time as a function at the at least delivery parameter using the fourth machine-learning process.