Systems and methods are disclosed for determining a propensity of an entity to take a specified action. In accordance with one implementation, a method is provided for determining the propensity. The method includes, for example, accessing one or more data sources, the one or more data sources including information associated with the entity, forming a record associated with the entity by integrating the information from the one or more data sources, generating, based on the record, one or more features associated with the entity, processing the one or more features to determine the propensity of the entity to take the specified action, and outputting the propensity.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for determining a propensity of an entity to take a specified action, the system comprising: one or more computer-readable storage media configured to store instructions; and one or more processors configured to execute the instructions to: acquire information associated with the entity from one or more data sources; form a record associated with the entity by integrating the information from the one or more data sources; generate, based on the record, one or more features associated with the entity; process the one or more features to determine the propensity of the entity to take the specified action; and output the propensity. 2. The system of claim 1, wherein the one or more processors are further configured to filter the record for information associated with the specified action. 3. The system of claim 1, wherein the one or more processors are further configured to train a model to predict the propensity of the entity to take the specified action. 4. The system of claim 3, wherein the one are more processors are further configured to determine, based on the trained model and the record, the relative importance of the one or more features. 5. The system of claim 1, wherein the one or more processors are further configured to: acquire a temporal period; and determine the propensity of the entity to take the specified action within the temporal period. 6. The system of claim 1, wherein the one or more processors are further configured to generate a user interface to display the propensity of the entity to take the specified action. 7. The system of claim 1, wherein the entity is a household and the specified action is churn. 8. A method for determining a propensity of an entity to take a specified action, the method being performed by one or more processors and comprising: acquiring information associated with the entity from one or more data sources; forming a record associated with the entity by integrating the information from the one or more data sources; generating, based on the record, one or more features associated with the entity; processing the one or more features to determine the propensity of the entity to take the specified action; and outputting the propensity. 9. The method of claim 8, further comprising filtering the record for information associated with the specified action. 10. The method of claim 8, further comprising training a model to predict the propensity of the entity to take the specified action. 11. The method of claim 10, further comprising determining, based on the trained model and the record, the relative importance of the one or more features. 12. The method of claim 8, further comprising: acquiring a temporal period; and determining the propensity of the entity to take the specified action within the temporal period. 13. The method of claim 8, further comprising generating a user interface to display the propensity of the entity to take the specified action. 14. The method of claim 8, wherein the entity is a household and the specified action is churn. 15. A non-transitory computer-readable medium storing a set of instructions that are executable by one or more processors to cause the one or more processors to perform a method for determining a propensity of an entity to take a specified action, the method comprising: acquiring information associated with the entity one or more data sources; forming a record associated with the entity by integrating the information from the one or more data sources; generating, based on the record, one or more features associated with the entity; processing the one or more features to determine the propensity of the entity to take the specified action; and outputting the propensity. 16. The non-transitory computer-readable medium of claim 15, further comprising instructions executable by the one or more processors to cause the one or more processors to perform: training a model to predict the propensity of the entity to take the specified action. 17. The non-transitory computer-readable medium of claim 16, further comprising instructions executable by the one or more processors to cause the one or more processors to perform: determining, based on the trained model and the record, the relative importance of the one or more features. 18. The non-transitory computer-readable medium of claim 15, further comprising instructions executable by the one or more processors to cause the one or more processors to perform: acquiring a temporal period; and determining the propensity of the entity to take the specified action within the temporal period. 19. The non-transitory computer-readable medium of claim 15, further comprising instructions executable by the one or more processors to cause the one or more processors to perform: generating a user interface to display the propensity of the entity to take the specified action. 20. The non-transitory computer-readable medium of claim 15, wherein the entity is a household and the specified action is churn.