METHOD AND SYSTEM FOR DATA CLASSIFICATION TO GENERATE A SECOND ALIMENTARY PROVIDER
A method of determining a second alimentary provider is disclosed. The method inputs an order for an alimentary combination from a user. The alimentary combination is prepared by a first alimentary provider. The method classifies a plurality of alimentary providers. The method computes an alimentary provider score for 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 scores to alimentary combinations. The method selects a second alimentary provider from the plurality of alimentary providers as a function of the alimentary provider score. The method outputs the second alimentary provider to the user. A system of determining a second alimentary provider is also disclosed.
1 . 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;
generate a plurality of first alimentary providers based on at least a type of cuisine comprising a dieting method;
determine the alimentary combination is not available at the plurality of first alimentary providers;
classify a plurality of alimentary providers;
compute an alimentary combination score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a machine-learning process, wherein each of the plurality of second alimentary combinations comprises at least a replacement for the alimentary combination; and
select a second alimentary provider from the plurality of alimentary providers as a function of the alimentary combination score; and
output the second alimentary provider to the user.
2 . The system of claim 1 , wherein the dieting method comprises a restricted medical diet.
3 . The system of claim 1 , wherein classifying the plurality of alimentary providers further comprises utilizing a machine-learning process to generate an alimentary provider classifier.
4 . The system of claim 3 , wherein utilizing the machine-learning process to generate the alimentary provider classifier comprises:
receiving alimentary provider training data;
training the 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.
5 . The system of claim 1 , wherein computing the alimentary combination score as a function of the machine-learning process further comprises training a machine-learning model with training data correlating an alimentary combination score to alimentary combinations.
6 . The system of claim 1 , wherein generating the plurality of first alimentary providers further comprises filtering the plurality of first alimentary providers as a function of user preferences.
7 . The system of claim 6 , wherein the user preferences include at least a selection of a delivery time.
8 . The system of claim 1 , wherein the computing device is further configured to:
train an alimentary combination classifier as a function of second alimentary combination training data; and
identify the second alimentary combination as a function of the alimentary combination classifier and a requested alimentary combination.
9 . The system of claim 1 , wherein the computing device is further configured to:
receive a geographical parameter of a first alimentary provider of the plurality of first alimentary providers;
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 the first alimentary provider.
10 . The system of claim 9 , wherein the geographical parameter training data correlates a first zip code of the first alimentary provider and a second zip code of the second alimentary provider.
11 . A method of outputting a second alimentary provider, the method including:
inputting, by a computing device, a request for an alimentary combination from a user;
generating, by the computing device, a plurality of first alimentary providers based on at least a type of cuisine including a dieting method;
determining, by the computing device, the alimentary combination is not available at the plurality of first alimentary providers;
classifying, by the computing device, a plurality of alimentary providers;
computing, by the computing device, an alimentary combination score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a machine-learning process, wherein each of the plurality of second alimentary combinations includes at least a replacement for the alimentary combination; and
selecting, by the computing device, a second alimentary provider from the plurality of alimentary providers as a function of the alimentary combination score; and
outputting, by the computing device, the second alimentary provider to the user.
12 . The method of claim 11 , wherein the dieting method includes a restricted medical diet.
13 . The method of claim 11 , wherein classifying, by the computing device, the plurality of alimentary providers further includes utilizing a machine-learning process to generate an alimentary provider classifier.
14 . The method of claim 13 , wherein utilizing the machine-learning process to generate the alimentary provider classifier includes:
receiving alimentary provider training data;
training the 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.
15 . The method of claim 11 , wherein computing the alimentary combination score as a function of the machine-learning process further includes training a machine-learning model with training data correlating an alimentary combination score to alimentary combinations.
16 . The method of claim 11 , wherein generating the plurality of first alimentary providers further includes filtering the plurality of first alimentary providers as a function of user preferences.
17 . The method of claim 16 , wherein the user preferences includes at least a selection of a delivery time.
18 . The method of claim 11 , wherein the computing device is further configured to:
train an alimentary combination classifier as a function of second alimentary combination training data; and
identify the second alimentary combination as a function of the alimentary combination classifier and a requested alimentary combination.
19 . The method of claim 11 , wherein the computing device is further configured to:
receive a geographical parameter of a first alimentary provider of the plurality of first alimentary providers;
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 the first alimentary provider.
20 . The method of claim 19 , wherein the geographical parameter training data correlates a first zip code of the first alimentary provider and a second zip code of the second alimentary provider.