System and method for self-service data analytics

A system for data analytics, comprising:

• one or more memories;
• and
• one or more hardware processors, the one or more memories coupled to the one or more hardware processors, wherein the one or more hardware processors are capable of executing programmed instructions stored in the one or more memories to:
• access sample data associated with a task from one or more sources comprising source data;
• process sample data associated with the task to obtain a processed sample data set comprising at least one or more additional columns than columns in the source data;
• dynamically capture metadata corresponding to the one or more additional columns of the processed sample data set during the processing of the sample data;
• iteratively execute, using the processed sample data set and the metadata, a plurality of data mining models obtained by varying variables and versions of a set of data mining models stored in a model repository to obtain a plurality of metrics corresponding to the plurality of data mining models, the plurality of metrics indicative of performance of the data mining models;
• and
• select, for data analytics, a data mining model from amongst the plurality of data mining models based on the plurality of metrics.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• accessing sample data associated with a task from one or more sources comprising source data, via one or more hardware processors;
• processing sample data associated with the task to obtain a processed sample data set comprising at least one or more additional columns than columns in the source data, via the one or more hardware processors;
• dynamically capturing metadata corresponding to the one or more additional columns of the processed sample data set during the processing of the sample data, via the one or more hardware processors;
• iteratively executing, using the processed sample data set and the metadata, a plurality of data mining models obtained by varying variables and versions of a set of data mining models stored in a model repository to obtain a plurality of metrics corresponding to the plurality of data mining models, via the one or more hardware processors, wherein the plurality of metrics indicative of performance of the data mining models;
• and
• selecting, for data analytics, a data mining model from amongst the plurality of data mining models based on the plurality of metrics, via the one or more hardware processors.