Simplistic machine learning model generation tool for predictive data analytics

1. A method implemented by a computing device for constructing a machine learning model generation tool, the method comprising:

generating a guided user interface (GUI) that enables a user to construct the machine learning model generation tool via one or more user operations on the GUI, the one or more user operations including:

obtaining, from a database, a dataset associated with a plurality of data objects;

generating, on the GUI, a visualization of a correlation of the dataset;

based on the visualization:

determining one or more characteristics associated with a first data object of the plurality of data objects;

identifying a subset of the dataset based at least in part on the one or more characteristics;

selecting at least one machine learning algorithm; and

configuring the machine learning model generation tool to generate a machine learning (ML) model with respect to the first data object based on the subset of the dataset and the at least one machine learning algorithm; and

implementing the machine learning model generation tool and the ML model with respect to the first data object in a cloud server to enable distributing at least one of the machine learning model generation tool or the ML model with respect to the first data object to a plurality of second computing devices via a network.

2. The method of claim 1 , wherein the one or more user operations on the GUI further comprising at least one of:

determining dimensions of the dataset;

determining statistic information associated with the dataset;

performing a null value treatment on the dataset; or

performing an outlier value treatment on the dataset.

3. The method of claim 1 , wherein the one or more characteristics associated with the first data object indicates correlations between the first data object and other data objects of the plurality of data objects.

4. The method of claim 1 , wherein identifying a subset of the dataset based at least in part on the one or more characteristics further comprises:

determining influence degrees between the first data object and other data objects of the plurality of data objects; and

performing a dimension reduction on the dataset by mapping the dataset to the subset of the dataset based at least in part on the influence degrees,

wherein a dimension of the subset of the dataset is less than a dimension of the dataset.

5. The method of claim 4 , wherein the dimension reduction is performed using at least one of a random forest algorithm, a single variable logistic regression algorithm, or a variable clustering algorithm.

6. The method of claim 1 , further comprising:

receiving a request to predict a target value associated with a target object, the request including a new dataset; and

executing the machine learning model generation tool to

generate an additional machine learning model with respect to the target object;

configure one or more parameters associated with the additional ML model with respect to the target object, the one or more parameters including at least one of an ML algorithm, one or more additional objects in the new dataset correlated to the target object, or a cross-validation parameter; and

determine the target value, using the additional ML model, based at least in part on the target object and the one or more parameters.

7. The method of claim 1 , wherein the cloud server is further configured to:

receive, from a second computing device, a request for downloading a second machine learning model associated with a second data object; and

determine whether the second machine learning model exists in the cloud server, wherein:

in response to determining the second machine learning model exists in the cloud server, distribute an instance of the second machine learning model to the second computing device, and

in response to determining the second machine learning model does not exist in the cloud server, distribute an instance of the machine learning model generation tool to the second computing device, causing an execution of the machine learning model generation tool on the second computing device to generate the second machine learning model associated with the second data object.

8. A system for constructing a machine learning model generation tool, comprising:

at least one processor; and

memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform actions comprising:

generating a guided user interface (GUI) that enables a user to construct the machine learning model generation tool, the one or more user operations including:

obtaining, from a database, a dataset including a plurality of data objects;

generating, on the GUI, a visualization of a correlation of the dataset;

based on the visualization,

determining one or more characteristics associated with a first data object of the plurality of data objects;

identifying a subset of the dataset based at least in part on the one or more characteristics;

selecting at least one machine learning algorithm; and

configuring the machine learning model generation tool to generate a machine learning (ML) model with respect to the first data object based on the subset of the dataset and the at least one machine learning algorithm; and

implementing the machine learning model generation tool and the ML model with respect to the first data object in a cloud server to enable distributing at least one of the machine learning model generation tool or the ML model with respect to the first data object to a plurality of second computing devices via a network.

9. The system of claim 8 , wherein the one or more operations further comprise at least one of:

determining dimensions of the dataset;

determining statistic information associated with the dataset;

performing a null value treatment on the dataset; or

performing an outlier value treatment on the dataset.

10. The system of claim 8 , wherein the one or more characteristics associated with the first data object indicates correlations between the first data object and other data objects of the plurality of data objects.

11. The system of claim 8 , wherein identifying a subset of the dataset based at least in part on the one or more characteristics further comprises:

determining influence degrees between the first data object and other data objects of the plurality of data objects; and

performing a dimension reduction on the dataset by mapping the dataset to the subset of the dataset based at least in part on the influence degrees,

wherein the dimension of the subset of the dataset is lower than the dimension of the dataset.

12. The system of claim 8 , wherein the dimension reduction is performed using at least one of a random forest algorithm, a single variable logistic regression algorithm, or a variable clustering algorithm.

13. The system of claim 8 , wherein the one or more operations further comprise:

receiving a request to predict a target value associated with a target object, the request including a new dataset; and

executing the machine learning model generation tool to

generate an additional machine learning model with respect to the target object;

configure one or more parameters associated with the additional ML model with respect to the target object, the one or more parameters including at least one of an ML algorithm, one or more additional objects in the new dataset correlated to the target object, or a cross-validation parameter; and

determine the target value, using the additional ML model, based at least in part on the target object and the one or more parameters.

14. A computer-readable storage medium storing computer-readable instructions executable by one or more processors for constructing a machine learning model generation tool, that when executed by the one or more processors, cause the one or more processors to perform actions comprising:

generating a guided user interface (GUI) that enables a user to construct the machine learning model generation tool via one or more user operations on the GUI, the one or more user operations including:

obtaining, from a database, a dataset including a plurality of data objects;

generating, on the GUI, a visualization of a correlation of the dataset;

based on the visualization,

determining one or more characteristics associated with a first data object of the plurality of data objects;

identifying a subset of the dataset based at least in part on the one or more characteristics;

selecting at least one machine learning algorithm; and

configuring the machine learning model generation tool to generate training a machine learning (ML) model with respect to the first data object using based on the subset of the dataset and the at least one machine learning algorithm; and

implementing the machine learning model generation tool and the trained ML model with respect to the first data object in a cloud server to enable distributing at least one of the machine learning model generation tool or the ML model with respect to the first data object to a plurality of second computing devices via a network.

15. The computer-readable storage medium of claim 14 , wherein the one or more operations further comprise at least one of:

determining dimensions of the dataset;

determining statistic information associated with the dataset;

performing a null value treatment on the dataset; or

performing an outlier value treatment on the dataset.

16. The computer-readable storage medium of claim 14 , wherein the one or more characteristics associated with the first data object indicates correlations between the first data object and other data objects of the plurality of data objects.

17. The computer-readable storage medium of claim 16 , wherein identifying a subset of the dataset based at least in part on the one or more characteristics further comprises:

determining influence degrees between the first data object and other data objects of the plurality of data objects; and

performing a dimension reduction on the dataset by mapping the dataset to the subset of the dataset based at least in part on the influence degrees,

wherein the dimension of the subset of the dataset is lower than the dimension of the dataset.

18. The computer-readable storage medium of claim 14 , wherein the dimension reduction is performed using at least one of a random forest algorithm, a single variable logistic regression algorithm, or a variable clustering algorithm.

19. The computer-readable storage medium of claim 14 , wherein the one or more operations further comprise:

receiving a request to predict a target value associated with a target object, the request including a new dataset; and

executing the machine learning model generation tool to

generate an additional machine learning model with respect to the target object;

configure one or more parameters associated with the additional trained ML model with respect to the target object, the one or more parameters including at least one of an ML algorithm, one or more additional objects in the new dataset correlated to the target object, or a cross-validation parameter; and

determine the target value, using the additional ML model, based at least in part on the target object and the one or more parameters.