Various embodiments of systems and methods for generating predictive models are described herein. A computer system deployed in a distributed may receive configuration data from multiple electronic devices. The system may select a set of configuration data with respect to a device category and a subcategory to generate a prediction model. The predictive model includes hypothesis, an average deviation and information pertaining to optimal configuration data for the given subcategory and the device category. The computer system may also receive monitoring requests from electronic devices and retrieve appropriate predictive model with respect to the device category and subcategory. The system may reconfigure the electronic device based on the retrieve predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for generating a predictive model to reconfigure an electronic device from a plurality of electronic devices, the method comprising: sampling, at a processor of a computer, a set of configuration data received from the plurality of electronic devices, with respect to a first device category from a plurality of device categories and a first subcategory from a plurality of subcategories, to generate sample data for a time interval; generating, at the processor of the computer, training data and validation data from the sample data for the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; based on the generated training data and the validation data, generating, at the processor of the computer, the predictive model for the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; and reconfiguring, at the processor of the computer, the electronic device from the plurality of electronic devices based on the configuration data included in the generated predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories. 2. The computer implemented method according to claim 1, wherein the method comprises: receiving, at the processor of the computer, configuration data from the plurality of electronic devices located in a plurality of geographical locations; storing, at a memory of the computer, the received configuration data corresponding to the plurality of device categories and the plurality of subcategories; and prior to sampling, retrieving, at the processor of the computer, the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, from the stored configuration data. 3. The computer implemented method according to claim 1, wherein generating the training data and the validation data further includes: selecting, at the processor of the computer, a data sample from the sample data until a first predetermined number of data samples are selected to generate the training data; and selecting, at the processor of the computer, unselected data samples from the sample data to generate the validation data. 4. The computer implemented method according to claim 1, wherein generating the predictive model further includes: computing, at the processor of the computer, a first hypothesis with respect to the generated training data, wherein the first hypothesis is computed by applying regression analysis on the generated training data; computing, at the processor of the computer, an average deviation based on the computed first hypothesis, comprises: computing, at the processor of the computer, a second hypothesis by computing average mean distances of the training data from the first hypothesis, wherein the average mean distances is computed with respect to the subcategory from the plurality of subcategories, of the training data; computing, at the processor of the computer, difference between the first hypothesis and the second hypothesis to obtain a difference value; computing, at the processor of the computer, multiplicative inverse of the computed difference value to obtain a reciprocal value; and computing, at the processor of the computer, a first threshold high and a first threshold low with respect to the first hypothesis, wherein the first threshold high is computed by applying regression analysis on differences between the reciprocal value and the subcategory from the plurality of subcategories, of the training data, wherein the first threshold low is computed by applying regression analysis on summation of the reciprocal value and the subcategory from the plurality of subcategories, of the training data. 5. The computer implemented method according to claim 1, wherein the method further comprises: validating, at the processor of the computer, the generated predictive model based on the validation data generated from the sample data, wherein: upon successful validation, storing, at the memory of the computer, the validated predictive model; and upon unsuccessful validation, generating, at the processor of the computer, another predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories for a non-regular time interval. 6. The computer implemented method according to claim 1, wherein the method further comprises: periodically generating, at the processor of the computer, the predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, in non-regular time intervals to refine the generated predictive model. 7. The method according to claim 1, wherein the method for reconfiguring the electronic device from the plurality of electronic devices further comprises: periodically receiving, at the processor of the computer, a monitoring request to determine health status of the electronic device from the plurality of electronic devices; extracting, at the processor of the computer, an existing configuration data from the received monitoring request; identifying, at the processor of the computer, a second device category from the plurality of the device categories and a second subcategory from the plurality of subcategories, from the extracted configuration data; retrieving, at the processor of the computer, the predictive model from the memory with respect to the identified second device category from the plurality of the device categories and the identified second subcategory from the plurality of subcategories, wherein the retrieved predictive model includes a third hypothesis, a second threshold high, a second threshold low, and a plurality of optimal configuration data; mapping, at the processor of the computer, the extracted configuration data with the retrieved predictive model to determine the health status of the electronic device from the plurality of electronic devices, wherein: when the extracted configuration data lies on the third hypothesis or near to the third hypothesis, categorizing, at the processor of the computer, the health status of the electronic device from the plurality of electronic devices as ideal; when the extracted configuration data lies between the third hypothesis and the second threshold high or between the third hypothesis and the second threshold low, categorizing, at the processor of the computer, the health status of the electronic device from the plurality of electronic devices as change to optimal configurations; selecting, at the processor of the computer, an optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; reconfiguring, the processor of the computer, the electronic device from the plurality of electronic devices, by transmitting the selected optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model to the electronic device from the plurality of electronic devices; and when the extracted configuration data lies beyond the second threshold high or beyond the second threshold low, categorizing, at the processor of the computer, the health status of the electronic device from the plurality of electronic devices as non-ideal; and transmitting, at the processor of the computer, an alert notification to the electronic device from the plurality of devices, to indicate non-ideal health status. 8. The computer implemented method according to claim 7, wherein reconfiguring the electronic device from the plurality of electronic devices further includes: upon successful reconfiguration, receiving, at the processor of the computer, a reconfiguration successful feedback from the electronic device from the plurality of electronic devices; upon unsuccessful reconfiguration, receiving, at the processor of the computer, a reconfiguration unsuccessful feedback from the electronic device from the plurality of electronic devices, wherein: selecting, at the processor of the computer, another optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; iterating, at the processor of the computer, selection of another optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model, for a second predetermined number of attempts; and upon unsuccessful reconfiguration for the second predetermined number of attempts, transmitting, at the processor of the computer, an error message to indicate unsuccessful reconfiguration attempts and service center information based on the geographical location of the electronic device from the plurality of electronic devices. 9. A computer system for generating a predictive model to reconfigure an electronic device from a plurality of electronic devices, the system comprising: a memory to store a program code; a processor communicatively coupled to the memory, the processor configured to execute the program code to: sample a set of configuration data received from the plurality of electronic devices with respect to a first device category from a plurality of device categories and a first subcategory from a plurality of subcategories, to generate sample data for a time interval; generate training data and validation data from the sample data, for the first device category from a plurality of device categories and with respect to a first subcategory from a plurality of subcategories, for the time interval; based on the generated training data and the validation data, generate the predictive model for the set of configuration data received from the plurality of electronic devices with respect to the device category from the plurality of device categories and the subcategory from the plurality of subcategories, for the time interval; and reconfigure the electronic devices from the plurality of electronic devices based on the configuration data included in the generated predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories. 10. The computer system according to claim 9, wherein the processor executes the program code to: receive configuration data from a plurality of electronic devices located in a plurality of geographical locations; store the received configuration data corresponding to the plurality of device categories and the plurality of subcategories; and prior to sampling, retrieve the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, from the stored configuration data. 11. The computer system according to claim 9, wherein the processor executes the program code to: select data sample from the sample data until a first predetermined number of data samples are selected to generate the training data; and select unselected data samples from the sample data to generate the validation data. 12. The computer system according to claim 9, wherein the processor executes the program code to: compute a first hypothesis with respect to the generated training data, wherein the first hypothesis is computed by applying regression analysis on the generated training data; compute an average deviation based on the computed first hypothesis, wherein: compute a second hypothesis by computing average mean distances of the training data from the first hypothesis, wherein the average mean distances is computed with respect to the subcategory from the plurality of subcategories, of the training data; compute difference between the first hypothesis and the second hypothesis to obtain a difference value; compute multiplicative inverse of the computed difference value between the first hypothesis and the second hypothesis to obtain a reciprocal value; and compute first threshold high and a first threshold low with respect to the first hypothesis, wherein the first threshold high is computed by applying regression analysis on differences between the reciprocal value and the subcategory from the plurality of subcategories, of the training data, wherein the first threshold low is computed by applying regression analysis on summation of the reciprocal value and the subcategory from the plurality of subcategories, of the training data. 13. The computer system according to claim 9, wherein the processor executes the program code to: validate the generated predictive model based on the validation data generated from the sample data, wherein: upon successful validation, store the validated predictive model in the memory; and upon unsuccessful validation, generate another predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories for a non-regular time interval. 14. The computer system according to claim 9, wherein the processor executes the program code to reconfigure the electronic device from the plurality of electronic devices: periodically receive a monitoring request to determine health status of the electronic device from the plurality of electronic devices; extract an existing configuration data from the received monitoring request; identify a second device category from the plurality of the device categories and a second subcategory from the plurality of subcategories, from the extracted configuration data; retrieve the predictive model from the memory with respect to the identified second device category from the plurality of the device categories and the identified second subcategory from the plurality of subcategories, wherein the retrieved predictive model includes a third hypothesis, a second threshold high, a second threshold low, and a plurality of optimal configuration data; map the extracted configuration data with the retrieved predictive model to determine the health status of the electronic device from the plurality of electronic devices, wherein: when the extracted configuration data lies on the third hypothesis or near to the third hypothesis, categorize the health status of the electronic device from the plurality of electronic devices as ideal; when the extracted configuration data lies between the third hypothesis and the second threshold high or between the third hypothesis and the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as change to optimal configurations; select an optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; reconfigure the electronic device from the plurality of electronic devices, by transmitting the selected optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model, to the electronic device from the plurality of electronic devices; and when the extracted configuration data lies beyond the second threshold high or beyond the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as non-ideal; and transmit an alert notification to the electronic device from the plurality of devices, to indicate non-ideal health status. 15. A non-transitory computer readable storage medium to tangibly store instructions, which when executed by a computer, cause the computer to: sample a set of configuration data received from a plurality of electronic devices with respect to a first device category from a plurality of device categories and a first subcategory from a plurality of subcategories, to generate sample data for a time interval; generate training data and validation data from the sample data for the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; based on the generated training data and validation data generate the predictive model for the set of configuration data with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; and reconfigure the electronic devices from the plurality of electronic devices based on the configuration data included in the generated predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories. 16. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: receive configuration data from the plurality of electronic devices located in a plurality of geographical locations; store the received configuration data corresponding to the plurality of device categories and the plurality of subcategories; and prior to sampling, retrieve the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, from the stored configuration data. 17. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: select data sample from the sample data until a first predetermined number of data samples are selected to generate the training data; and select unselected data samples from the sample data to generate the validation data. 18. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: compute a first hypothesis with respect to the generated training data, wherein the first hypothesis is computed by applying regression analysis on the generated training data; compute an average deviation based on the computed first hypothesis, wherein: compute a second hypothesis by computing average mean distances of the training data from the first hypothesis, wherein the average mean distances is computed with respect to the subcategory from the plurality of subcategories, of the training data; compute difference between the first hypothesis and the second hypothesis to obtain a difference value; compute multiplicative inverse of the computed difference value between the first hypothesis and the second hypothesis to obtain a reciprocal value; and compute first threshold high and a first threshold low with respect to the first hypothesis, wherein the first threshold high is computed by applying regression analysis on differences between the reciprocal value and the subcategory from the plurality of subcategories, of the training data, wherein the first threshold low is computed by applying regression analysis on summation of the reciprocal value and the subcategory from the plurality of subcategories, of the training data. 19. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: validate the generated predictive model based on the validation data generated from the sample data, wherein: upon successful validation, store the validated predictive model into the memory; and upon unsuccessful validation, generate another predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories for a non-regular time interval. 20. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: periodically receive a monitoring request to determine health status of the electronic device from the plurality of electronic devices; extract an existing configuration data from the received monitoring request; identify a second device category from the plurality of the device categories and a second subcategory from the plurality of subcategories, from the extracted configuration data; retrieve a stored predictive model from the memory with respect to the identified second device category from the plurality of the device categories and the identified second subcategory from the plurality of subcategories, wherein the retrieved predictive model includes a third hypothesis, a second threshold high, a second threshold low, and a plurality of optimal configuration data; map the extracted configuration data with the retrieved predictive model to determine the health status of the electronic device from the plurality of electronic devices, wherein: when the extracted configuration date lies on the third hypothesis or near to the third hypothesis, categorize the health status of the electronic device from the plurality of electronic devices as ideal; when the extracted configuration data lies between the third hypothesis and the second threshold high or between the third hypothesis and the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as change to optimal configurations; select an optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; reconfigure the electronic device from the plurality of electronic devices, by transmitting the selected optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model, to the electronic device from the plurality of electronic devices; and when the extracted configuration data lies beyond the second threshold high or beyond the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as non-ideal; and transmit an alert notification to the electronic device from the plurality of devices, to indicate non-ideal health status.