Various systems, mediums, and methods for providing services may involve data engines configured to generate scores associated with one or more entities and then to classify the entities based on the scores. The data engine may collect data and based on the collected data may generate a first behavior model in a first time span, and a second and third behavior models in a second time span. The data engine may generate a first score based on the first behavior model, a second score based on the second behavior model, and a third score based on the third behavior model. The data engine may generate a final score based on the first, second, and third scores. The data engine can classify the entity based on the final score. The data engine can then automatically adjust one of the services provided to the entity based on the final score.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for providing one or more services to an entity, comprising: a non-transitory memory storing at least one database; and one or more hardware processors configured to execute instructions to cause the system to perform operations comprising: collecting, by a data collection and processing module of a data engine, from the database, data corresponding to a performance of the entity; generating, by a modeling module of the data engine, a first behavior model associated with the performance of the entity in a first time span based at least on the collected data; determining whether a predetermined event occurred in the first time span; generating, by the modeling module and based at least on the collected data, a second behavior model and a third behavior model associated with the performance of the entity in a second time span, wherein the second behavior model is further based on the occurrence of the predetermined event and the third behavior model is further based on a non-occurrence of the predetermined event; determining a predefined outcome in the second time span; generating, by a score generating module of the data engine, a first score based at least on the first behavior model and corresponding to the occurrence of the predetermined event, a second score based at least on the second behavior model, and a third score based at least on the third behavior model, wherein the second and third scores correspond to a likelihood that the predefined outcome occurs; generating, by the score generating module, and based at least on the first score, the second score, and the third score, a final score that corresponds to achieving the predefined outcome corresponding to the performance of the entity in a final time span, the final time span having a duration greater than or equal to a minimum of a duration of the first time span and a duration of the second time span; classifying the entity into two or more groups based at least on the final score by a classifier module of the data engine; and automatically adjusting at least one of the one or more services provided to the entity based on the final score, the classification, or both. 2. The system of claim 1, wherein the modeling module is configured to implement Bayesian methodology, Gradient Boosting techniques, or both to generate the first, the second, and the third behavior models. 3. The system of claim 1, wherein at least the first score is generated on a probability scale between zero and one, and the final score is calculated as second score*first score+third score*(1−first score). 4. The system of claim 1, wherein the first score has a first accuracy, the second score has a second accuracy, the third score has a third accuracy, and the final score has a final prediction accuracy better than the first accuracy, the second accuracy, and the third accuracy; and wherein the operations further comprise: determining, by the score generating module and based at least on the first behavior model, the second behavior model, and the third behavior model, the first accuracy, the second accuracy, the third accuracy, and the final prediction accuracy. 5. The system of claim 1, wherein collecting data by the data collection and processing module further comprises normalizing data and removing one or more outliers. 6. The system of claim 1, wherein generating the second behavior model and the third behavior model further comprises determining independent relationships in the collected data in different spans of time equal to the first time span and the second time span. 7. The system of claim 1, wherein the operations performed further comprise: receiving updated data for the entity by an incremental data module of the data engine; monitoring the received updated data; automatically updating the first score, the second score, and the third score based at least on the received updated data; automatically generating an updated final score based at least on the updated first score, the second score, and the third score; and automatically reclassifying the entity based at least on the updated final score. 8. The system of claim 1, wherein the classification of the entity further comprises: assigning the entity to a success group if the final score is greater than a first predetermined threshold, assigning the entity to a failure group if the final score is less than a second predetermined threshold, and assigning the entity to a neutral group if the final score is between the first and second predetermined thresholds; and wherein adjusting the services further comprises sending notifications or stopping at least one of the one or more services when the entity is assigned to the failure group. 9. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising: providing one or more services to an entity; collecting, by a data collection and processing module of a data engine, from a database, data corresponding to a performance of the entity; generating, by a modeling module of the data engine, a first behavior model associated with the performance of the entity in a first time span based at least on the collected data; determining whether a predetermined event occurred in the first time span; generating, by the modeling module and based at least on the collected data, a second behavior model and a third behavior model associated with the performance of the entity in a second time span, wherein the second behavior model is further based on the occurrence of the predetermined event and the third behavior model is further based on a non-occurrence of the predetermined event; determining a predefined outcome in the second time span; generating, by a score generating module of the data engine, a first score based at least on the first behavior model and corresponding to the occurrence of the predetermined event, a second score based at least on the second behavior model, and a third score based at least on the third behavior model, wherein the second and third scores correspond to a likelihood that the predefined outcome occurs; and generating, by the score generating module, and based at least on the first score, the second score, and the third score, a final score that corresponds to achieving the predefined outcome corresponding to the performance of the entity in a final time span, the final time span having a duration greater than or equal to a minimum of a duration of the first time span and a duration of the second time span. 10. The non-transitory machine-readable medium of claim 9, where in the operations further comprises: classifying the entity into two or more groups based at least on the final score by a classifier module of the data engine; and automatically adjusting at least one of the one or more services provided to the entity based on the final score, the classification, or both. 11. The non-transitory machine-readable medium of claim 10, wherein the first score has a first accuracy, the second score has a second accuracy, the third score has a third accuracy, and the final score has a final prediction accuracy better than the first accuracy, the second accuracy, and the third accuracy. 12. The non-transitory machine-readable medium of claim 11, the operations further comprise: determining, by the score generating module and based at least on the first behavior model, the second behavior model, and the third behavior model, the first accuracy, the second accuracy, the third accuracy, and the final prediction accuracy. 13. The non-transitory machine-readable medium of claim 10, wherein generating the second behavior model and the third behavior model further comprises determining relationships in the collected data in different spans of time equal to the first time span and the second time span. 14. The non-transitory machine-readable medium of claim 10, wherein the operations further comprise: receiving updated data for the entity by an incremental data module of the data engine; monitoring the received updated data; automatically updating the first score, the second score, and the third score based on the received updated data; automatically generating an updated final score based at least on the updated first score, the second score, and the third score; and automatically reclassifying the entity based at least on the updated final score. 15. A method of providing one or more services to an entity, comprising: collecting, by a data engine executing on a server, from a database, data corresponding to a performance of the entity; generating a first behavior model associated with the performance of the entity in a first time span, wherein the collected data is used by the data engine to generate the first behavior model; determining whether a predetermined event occurred in the first time span; generating a second behavior model and a third behavior model associated with the performance of the entity in a second time span using the collected data, wherein the second behavior model is based at least on the occurrence of the predetermined event and the third behavior model is based at least on a non-occurrence of the predetermined event; determining a predefined outcome in the second time span; generating, by the data engine, a first score based at least on the first behavior model and corresponding to the occurrence of the predetermined event, a second score based at least on the second behavior model, and a third score based at least on the third behavior model, wherein the second and third scores correspond to a likelihood that the predefined outcome occurs; generating, based at least on the first score, the second score, and the third score, a final score that corresponds to achieving the predefined outcome corresponding to the performance of the entity in a final time span, the final time span having a duration greater than or equal to a minimum of a duration of the first time span and a duration of the second time span; classifying the entity into two or more groups based at least on the final score by the data engine; and adjusting at least one of the one or more services provided to the entity based on the final score, the classification, or both. 16. The method of claim 15, further comprising: generating the first, the second, and the third behavior models based on Bayesian methodology, Gradient Boosting techniques, or both. 17. The method of claim 15, further comprising: generating, at least the first score, on a probability scale between zero and one; and calculating the final score as second score*first score+third score*(1−first score). 18. The method of claim 15, further comprising: receiving updated data for the entity by the data engine; monitoring the received updated data; automatically updating the first score, the second score, and the third score based on the received updated data; automatically generating an updated final score based at least on the updated first score, the second score, and the third score; and automatically reclassifying the entity based at least on the updated final score. 19. The method of claim 15, wherein the first score has a first accuracy, the second score is has a second accuracy, the third score has a third accuracy, and the final score has a final prediction accuracy better than the first accuracy, the second accuracy, and the third accuracy; and wherein the method further comprises: determining, by the data engine and based at least on the first behavior model, the second behavior model, and the third behavior model, the first accuracy, the second accuracy, the third accuracy, and the final prediction accuracy. 20. The method of claim 15, wherein collecting data corresponding to the performance of the entity comprises normalizing data and removing one or more outliers