Predictive modeling and analytics for processing and distributing data traffic

1. A method for generating and deploying a machine learning model for a real-time environment, the method comprising:

receiving, via a graphical user interface, user selected coefficients and training data;

invoking a first machine learning algorithm for generating a first machine learning model based on the received coefficients and training data;

testing accuracy of predictions by the first machine learning model;

determining that the accuracy of predictions of the first machine learning model is below a threshold value;

in response to determining that the accuracy of predictions is below the threshold value, evaluating a particular criterion;

in response to the particular criterion being satisfied, invoking a second machine learning algorithm for generating a second machine learning model based on the received coefficients and training data;

deploying the second machine learning model instead of the first machine learning model for making real-time predictions based on incoming data;

receiving the incoming data from a plurality of sources;

invoking the second machine learning model for predicting a likelihood of success associated with the incoming data from a particular source of the plurality of sources; and

transmitting the incoming data from the particular source to a destination in response to determining the likelihood of success.

2. The method of claim 1 , wherein the destination is selected from a plurality of destinations, the method further comprising:

calculating values for the plurality of destinations;

dynamically ranking the plurality of destinations based on the calculated values; and

selecting the destination based on the ranking.

3. The method of claim 2 , wherein each of the values is calculated based on predicting a likelihood of success resulting from the incoming data from the particular source being transmitted to each of the plurality of destinations.

4. The method of claim 2 further comprising:

receiving a signal from the destination in response to transmitting the incoming data;

in response to receiving the signal, identifying a second destination of the plurality of destinations based on the ranking; and

transmitting the incoming data to the second destination.

5. The method of claim 1 , wherein the first machine learning model is a generalized linear model (GLM) associated with a first link function.

6. The method of claim 5 , wherein the second machine learning model is at least one of a principal component regression or a Bayesian GLM.

7. The method of claim 6 , wherein the criterion is size of the training data, wherein the criterion is satisfied in response to determining that the size of the training data is below a threshold size.

8. The method of claim 1 , wherein the likelihood of success includes a likelihood of selling the incoming data to the destination.

9. The method of claim 1 further comprising:

invoking the second machine learning model for predicting a likelihood of success associated with the incoming data from a second source of the plurality of sources;

determining that the likelihood of success is below a threshold amount; and

filtering out the incoming data from the second source in response to determining that the likelihood of success is below the threshold amount.

10. The method of claim 9 further comprising refraining transmitting the incoming data from the second source to the destination in response to the filtering out.

11. A system for generating and deploying a machine learning model for a real-time environment, the system comprising:

processor; and

memory, wherein the memory stores instructions that, when executed by the processor, cause the processor to:

receive, via a graphical user interface, user selected coefficients and training data;

invoke a first machine learning algorithm for generating a first machine learning model based on the received coefficients and training data;

test accuracy of predictions by the first machine learning model;

determine that the accuracy of predictions of the first machine learning model is below a threshold value;

in response to determining that the accuracy of predictions is below the threshold value, evaluate a particular criterion;

in response to the particular criterion being satisfied, invoke a second machine learning algorithm for generating a second machine learning model based on the received coefficients and training data;

deploy the second machine learning model instead of the first machine learning model for making real-time predictions based on incoming data;

receive the incoming data from a plurality of sources;

invoke the second machine learning model for predicting a likelihood of success associated with the incoming data from a particular source of the plurality of sources; and

transmit the incoming data from the particular source to a destination in response to determining the likelihood of success.

12. The system of claim 1 , wherein the destination is selected from a plurality of destinations, wherein the instructions further cause the processor to:

calculate values for the plurality of destinations;

dynamically rank the plurality of destinations based on the calculated values; and

select the destination based on the ranking.

13. The system of claim 12 , wherein each of the values is calculated based on predicting a likelihood of success resulting from the incoming data from the particular source being transmitted to each of the plurality of destinations.

14. The system of claim 12 , wherein the instructions further cause the processor to:

receive a signal from the destination in response to transmitting the incoming data;

in response to receiving the signal, identify a second destination of the plurality of destinations based on the ranking; and

transmit the incoming data to the second destination.

15. The system of claim 11 , wherein the first machine learning model is a generalized linear model (GLM) associated with a first link function.

16. The system of claim 15 , wherein the second machine learning model is at least one of a principal component regression or a Bayesian GLM.

17. The system of claim 16 , wherein the criterion is size of the training data, wherein the criterion is satisfied in response to determining that the size of the training data is below a threshold size.

18. The system of claim 11 , wherein the likelihood of success includes a likelihood of selling the incoming data to the destination.

19. The system of claim 11 , wherein the instructions further cause the processor to:

invoke the second machine learning model for predicting a likelihood of success associated with the incoming data from a second source of the plurality of sources;

determine that the likelihood of success is below a threshold amount; and

filter out the incoming data from the second source in response to determining that the likelihood of success is below the threshold amount.

20. The system of claim 19 , wherein the instructions further cause the processor to refrain transmitting the incoming data from the second source to the destination in response to the filtering out.