METHOD AND APPARATUS FOR FACILITATING ON-DEMAND BUILDING OF PREDICTIVE MODELS
First Claim
1. A computer-implemented method, comprising:
- receiving, by a processor, from a user;
at least one specification for developing at least one predictive model, andan input identifying one or more data sources storing data related to a plurality of customers of an enterprise;
retrieving, by the processor, the data related to the plurality of customers from the one or more data sources;
generating, by the processor, a training data sample and a testing data sample from the retrieved data;
performing, by the processor, at least one of structured categorical variable binning, unstructured categorical variable binning, and numeric variable binning;
generating, by the processor, transformed variables from variables identified for developing the at least one predictive model;
developing, by the processor, one or more predictive models based, at least in part, on the transformed variables and the training data sample;
generating, by the processor, at least one score corresponding to each predictive model from among the one or more predictive models based, at least in part, on the testing data sample;
selecting, by the processor, a predictive model from among the one or more predictive models based on the at least one score associated with each predictive model;
publishing the predictive model, by the processor, on a prediction platform; and
applying, by the processor, the published predictive model to predict outcomes related to customers of the enterprise.
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Abstract
A computer-implemented method and an apparatus facilitate on-demand building of predictive models. Data corresponding to a plurality of customers is retrieved from data sources and a training data sample and a testing data sample are generated from the retrieved data. Variables for developing the predictive model are identified from the retrieved data and are subjected to any of structured categorical variable binning, unstructured categorical variable binning, and numeric variable binning to generate transformed variables. The transformed variables and the training data sample are used to develop predictive models. The developed predictive models are tested using the testing data sample and scores are generated corresponding to the developed predictive models. A predictive model is selected from among the developed predictive models based on the scores. The selected predictive model is published on a prediction platform to facilitate prediction of outcomes related to customers of the enterprise.
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Citations
33 Claims
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1. A computer-implemented method, comprising:
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receiving, by a processor, from a user; at least one specification for developing at least one predictive model, and an input identifying one or more data sources storing data related to a plurality of customers of an enterprise; retrieving, by the processor, the data related to the plurality of customers from the one or more data sources; generating, by the processor, a training data sample and a testing data sample from the retrieved data; performing, by the processor, at least one of structured categorical variable binning, unstructured categorical variable binning, and numeric variable binning; generating, by the processor, transformed variables from variables identified for developing the at least one predictive model; developing, by the processor, one or more predictive models based, at least in part, on the transformed variables and the training data sample; generating, by the processor, at least one score corresponding to each predictive model from among the one or more predictive models based, at least in part, on the testing data sample; selecting, by the processor, a predictive model from among the one or more predictive models based on the at least one score associated with each predictive model; publishing the predictive model, by the processor, on a prediction platform; and applying, by the processor, the published predictive model to predict outcomes related to customers of the enterprise. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. An apparatus, comprising:
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an input/output (I/O) module configured to receive from a user; at least one specification for developing at least one predictive model, and an input identifying one or more data sources storing data related to a plurality of customers of an enterprise; a communication interface communicably coupled with the I/O module, the communication interface configured to retrieve the data related to the plurality of customers from the one or more data sources; at least one processor; and a memory having stored therein machine executable instructions, that when executed by the at least one processor, cause the apparatus to; generate a training data sample and a testing data sample from the retrieved data; perform any of structured categorical variable binning, unstructured categorical variable binning, and numeric variable binning; generate transformed variables from variables identified for developing the at least one predictive model; develop one or more predictive models based, at least in part, on the transformed variables and the training data sample; generate at least one score corresponding to each predictive model from among the one or more predictive models based, at least in part, on the testing data sample; select a predictive model from among the one or more predictive models based on the at least one score associated with each predictive model; publish the predictive model on a prediction platform; and apply the published predictive model to predict outcomes related to customers of the enterprise. - View Dependent Claims (19, 20, 21, 22, 23, 24, 25, 26, 27)
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28. An apparatus, comprising:
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an input/output (I/O) module configured to receive from a user; at least one specification for developing at least one predictive model, and an input identifying one or more data sources storing data related to a plurality of customers of an enterprise; a communication interface communicably coupled with the I/O module, the communication interface configured to facilitate retrieval of the data related to the plurality of customers from the one or more data sources; a data ingestion module configured to generate a training data sample and a testing data sample from the retrieved data; a transformation module configured to perform at least one of structured categorical variable binning, unstructured categorical variable binning, and numeric variable binning to generate transformed variables from variables identified for developing the at least one predictive model; a model building module configured to develop one or more predictive models based, at least in part, on the transformed variables and the training data sample; a model validating module configured to generate at least one score corresponding to each predictive model from among the one or more predictive models based, at least in part, on the testing data sample; and a model publishing module configured to publish a predictive model on a prediction platform to facilitate prediction of outcomes related to customers of the enterprise, the predictive model selected from among the one or more predictive models based on the at least one score associated with each predictive model. - View Dependent Claims (29, 30, 31, 32, 33)
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Specification