Strategy parameters and weights associated with the strategy parameters are received in a predictive analytics application to dynamically rank entities. Raw values associated with the strategy parameters are normalized by applying transformation functions to get normalized values. Based on the normalized values and the weights associated with the strategy parameters, weighted normalized values are computed. Based on the weighted normalized values aggregate scores are computed. The entities based on the computed aggregate score are dynamically ranked. The dynamically ranked entities in descending order of aggregate scores are displayed in a user interface of the predictive analytics application.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable medium to store instructions, which when executed by a computer, cause the computer to perform operations comprising: receive strategy parameters and weights associated with the strategy parameters to dynamically rank entities; normalize raw values associated with the strategy parameters by applying transformation functions; compute weighted normalized values based on the normalized raw values and the weights associated with the strategy parameters; compute aggregate scores based on the weighted normalized values; dynamically rank the entities based on the computed aggregate scores; and display the dynamically ranked entities on a user interface of a predictive analytics application. 2. The computer-readable medium of claim 1, wherein the entities are filtered based on specified filter parameters. 3. The computer-readable medium of claim 2, further comprising instructions which when executed by the computer further causes the computer to: reset the weights associated with the strategy parameters to default weights when the filtering based on the filter parameters is switched off. 4. The computer-readable medium of claim 1, further comprising instructions which when executed by the computer further causes the computer to: determine a set of strategy parameters, weights associated with the set of strategy parameters and filter parameters associated with a first entity for comparison; apply the determined set of strategy parameters, weights associated with the set of strategy parameters, and filter parameters to the entities, wherein the entities comprise an entity to be compared; normalize raw values associated with the set of strategy parameters by applying transformation functions; compute weighted normalized values for the set of strategy parameters based on the normalized raw values and the weights associated with the set of strategy parameters; compute new aggregate scores based on the weighted normalized values for the set of strategy parameters; dynamically rank the entities based on the computed new aggregate scores; and display a relative difference in rank between the entities and the entity to be compared, and the first entity for comparison and the entity to be compared. 5. The computer-readable medium of claim 1, further comprising instructions which when executed by the computer further causes the computer to: apply clustering algorithm on the computed aggregate scores to form clusters of aggregate scores, wherein the aggregate scores correspond to the entities; receive an entity as input to identify entities similar to the received entity; identify a cluster to which the received entity belongs; and display the entities in the identified cluster as similar entities. 6. The computer-readable medium of claim 1, further comprising instructions which when executed by the computer further causes the computer to: dynamically adjust the weights associated with the strategy parameters to re-rank dynamically ranked entities. 7. The computer-readable medium of claim 1, further comprising instructions which when executed by the computer further causes the computer to: assign entity budgets to the dynamically ranked entities as a reference during auction. 8. A computer-implemented method for dynamic predictive analysis in pre-bid of entities, the method comprising: receiving strategy parameters and weights associated with the strategy parameters to dynamically rank entities; normalizing raw values associated with the strategy parameters by applying transformation functions; computing weighted normalized value based on the normalized raw values and the weights associated with the strategy parameters; computing aggregate scores based on the weighted normalized values; dynamically ranking the entities based on the computed aggregate scores; and displaying the dynamically ranked entities on a user interface of a predictive analytics application. 9. The method of claim 8, wherein the entities are filtered based on specified filter parameters. 10. The method of claim 9, further comprising instructions which when executed by the computer further causes the computer to: resetting the weights associated with the strategy parameters to default weights when the filter parameters are switched off. 11. The method of claim 8, further comprising instructions which when executed by the computer further causes the computer to: determining a set of strategy parameters, weights associated with the set of strategy parameters and filter parameters associated with a first entity for comparison; applying the determined set of strategy parameters, weights associated with the set of strategy parameters, and filter parameters to entities, wherein the entities comprise an entity to be compared; normalizing raw values associated with the set of strategy parameters by applying transformation functions; computing weighted normalized value for the set of strategy parameters based on the normalized values and the weights associated with the set of strategy parameters; computing new aggregate scores based on the weighted normalized values for the set of strategy parameters; dynamically ranking the entities based on the computed new aggregate score; and display a relative difference in rank between the entities and the entity to be compared, and the first entity for comparison and the entity to be compared. 12. The method of claim 8, further comprising instructions which when executed by the computer further causes the computer to: applying clustering algorithm on the computed aggregate scores to form clusters of aggregate scores, wherein the aggregate scores correspond to the entities; receiving an entity as input to identify entities similar to the received entity; identifying a cluster to which the received entity belongs; and displaying the entities in the identified cluster as similar entities. 13. The method of claim 8, further comprising instructions which when executed by the computer further causes the computer to: dynamically adjusting the weights associated with the strategy parameters to re-rank dynamically ranked entities. 14. The method of claim 8, further comprising instructions which when executed by the computer further causes the computer to: assigning entity budgets to the dynamically ranked entities as a reference during auction. 15. A computer system for dynamic predictive analysis in pre-bid of entities, comprising: a computer memory to store program code; and a processor to execute the program code to: receive strategy parameters and weights associated with the strategy parameters to dynamically rank entities; normalize raw values associated with the strategy parameters by applying transformation functions; compute weighted normalized value based on the normalized values and the weights associated with the strategy parameters; compute aggregate scores based on the weighted normalized values; dynamically rank the entities based on the computed aggregate score; and display the dynamically ranked entities on a user interface of a predictive analytics application. 16. The system of claim 15, wherein the entities are filtered based on specified filter parameters. 17. The system of claim 16, further comprising instructions which when executed by the computer further causes the computer to: reset the weights associated with the strategy parameters to default weight when the filtering based on the filter parameters are switched off. 18. The system of claim 15, further comprising instructions which when executed by the computer further causes the computer to: determine a set of strategy parameters, weights associated with the set of strategy parameters and filter parameters associated with a first entity for comparison; apply the determined set of strategy parameters, weights associated with the set of strategy parameters, and filter parameters to entities, wherein the entities comprise an entity to be compared; normalize raw values associated with the set of strategy parameters by applying transformation functions; compute weighted normalized values for the set of strategy parameters based on the normalized raw values and the weights associated with the set of strategy parameters; compute new aggregate scores based on the weighted normalized values for the set of strategy parameters; dynamically rank the entities based on the computed new aggregate score; and displaying a relative difference in rank between the entities and the entity to be compared, and the first entity for comparison and the entity to be compared. 19. The system of claim 15, further comprising instructions which when executed by the computer further causes the computer to: apply clustering algorithm on the computed aggregate scores to form clusters of aggregate scores, wherein the aggregate scores correspond to the entities; receive an entity as input to identify entities similar to the received entity; identify a cluster to which the received entity belongs; and display the entities in the identified cluster as similar entities. 20. The system of claim 15, further comprising instructions which when executed by the computer further causes the computer to: dynamically adjust the weights associated with the strategy parameters to re-rank dynamically ranked entities.