METHOD AND SYSTEM TO GENERATE AN INSTANT APPLICATION

1 . A method for generating an instant application, the method comprising:

receiving a selection of one or more features and an application template;

determining a linkage between each pair of features of the one or more selected features;

processing the one or more selected features based on the determined linkage; and

generating the instant application based on the processing and using the application template.

2 . The method of claim 1 , wherein processing the one or more selected features based on the determined linkage comprises:

classifying the one or more features as unconnected features and connected features;

identifying one or more potential hotspots in the unconnected features; and

predicting the linkage for the unconnected features with the connected features based on the one or more identified potential hotspots.

3 . The method of claim 2 , wherein predicting the linkage for the unconnected features with the connected features comprises:

retrieving one or more clickable items mapped to the identified one or more potential hotspots, wherein the one or more clickable items are included in the one or more features; and

predicting the linkage for the unconnected features with the connected features based on the retrieved one or more clickable items.

4 . The method of claim 2 , wherein predicting the linkage for the unconnected features with the connected features comprises:

inputting the identified one or more potential hotspots to a first machine learning model;

estimating one or more clickable items as an output of the first machine learning model, wherein the one or more clickable items are included in the one or more features; and

predicting the linkage for the unconnected features with the connected features based on the estimated one or more clickable items.

5 . The method of claim 1 , further comprises recommending one or more launch screens for the application based on the application template.

6 . The method of claim 1 , wherein determining the linkage between each pair of features of the one or more selected features comprises:

retrieving historical data from a database;

selecting a machine learning model from a plurality of machine learning models, wherein each of the plurality of machine learning models includes a Light Gradient Boosting model;

inputting the historical data and one or more inputs to the selected machine learning model; and

determining the linkage between each pair of features based on an output of the selected machine learning model.

7 . The method of claim 6 , wherein selecting the machine learning model from the plurality of machine learning models comprises:

inputting the one or more inputs and the retrieved historical data to each of the plurality of machine learning model by assigning a weightage factor to each of the one or more inputs;

determining a value of one or more parameters, wherein at least one parameter of the one or more parameters includes a F1 score; and

selecting the machine learning model based on values of the one or more parameters.

8 . A computer system to generate an instant application, the system comprising:

a memory; and

a processor coupled to the memory and configured to:

receive a selection of one or more features and an application template;

determine a linkage between each pair of features of the one or more selected features;

process the one or more selected features based on the determined linkage; and

generate the instant application based on the processing and using the application template.

9 . The system of claim 8 , wherein to process the one or more selected features based on the determined linkage, the processor is configured to:

classify the one or more features as unconnected features and connected features;

identify one or more potential hotspots in the unconnected features; and

predict the linkage for the unconnected features with the connected features based on the one or more identified potential hotspots.

10 . The system of claim 9 , wherein to predict the linkage for the unconnected features with the connected features, the processor is configured to:

retrieve one or more clickable items mapped to the identified one or more potential hotspots, wherein the one or more clickable items are included in the one or more features; and

predict the linkage for the one or more unconnected features with the connected features based on the retrieved one or more clickable items.

11 . The system of claim 10 , wherein to predict the linkage for the unconnected features with the connected features, the processor is configured to:

input the identified one or more potential hotspots to a first machine learning model;

estimate one or more clickable items as an output of the first machine learning model, wherein the one or more clickable items are included in the one or more features; and

predict the linkage for the unconnected features with the connected features based on the estimated one or more clickable items.

12 . The system of claim 8 , wherein the processor is further configured to recommend the one or more launch screens for the application based on the application template.

13 . The system of claim 8 , wherein to determine the linkage between each pair of features of the one or more selected features, the processor is configured to:

retrieve historical data from a database stored in the memory;

select a machine learning model from a plurality of machine learning models, wherein each of the plurality of machine learning models includes a Light Gradient Boosting model;

input the historical data and one or more inputs to the selected machine learning model; and

determine the linkage between each pair of features based on an output of the selected machine learning model.

14 . The system of claim 13 , wherein to select the machine learning model from the plurality of machine learning models, the processor is configured to:

input one or more inputs and the retrieved historical data to each of the plurality of machine learning model by assigning a weightage factor to each of the one or more inputs;

determine a value of one or more parameters, wherein at least one parameter of the one or more parameters includes a F1 score; and

select the machine learning model based on values of the one or more parameters.

15 . A computer readable storage medium having data stored therein representing software executable by a computer, the software comprising instructions that, when executed, cause the computer readable storage medium to perform:

receiving a selection of one or more features and an application template;

determining a linkage between each pair of features of the one or more selected features;

processing the one or more selected features based on the determined linkage; and

generating an instant application based the processing and using the application template.

16 . The computer readable storage medium of claim 15 , wherein processing the one or more selected features based on the determined linkage comprises:

classifying the one or more features as unconnected features and connected features;

identifying one or more potential hotspots in the unconnected features; and

predicting the linkage for the unconnected features with the connected features based on the one or more identified potential hotspots.

17 . The computer readable storage medium of claim 16 , wherein predicting the linkage for the unconnected features with the connected features comprises:

retrieving one or more clickable items mapped to the identified one or more potential hotspots, wherein the one or more clickable items are included in the one or more features; and

predicting the linkage for the unconnected features with the connected features based on the retrieved one or more clickable items.

18 . The computer readable storage medium of claim 15 , wherein predicting the linkage for the one or more unconnected features with the one or more features comprises:

inputting the identified one or more potential hotspots to a first machine learning model;

estimating one or more clickable items as an output of the first machine learning model, wherein the one or more clickable items are included in the one or more features; and

predicting the linkage for the unconnected features with the connected features based on the estimated one or more clickable items.

19 . The computer readable storage medium of claim 15 , further comprises recommending one or more launch screens for the application based on the application template.

20 . The computer readable storage medium of claim 15 , wherein determining the linkage for each pair of features of the one or more selected features comprises:

retrieving historical data from a database;

selecting a machine learning model from a plurality of machine learning models, wherein each of the plurality of machine learning models includes a Light Gradient Boosting model;

inputting the historical data and one or more inputs to the selected machine learning model; and

determining the linkage between each pair of features based on an output of the selected machine learning model.