implementing a domain independent data processing module (DIDPM) configured for adding no-code machine learning and artificial intelligence capabilities to intelligence tools, wherein the DIDPM includes a receiving module, a displaying module, a creating module, an executing module, a calling module, a generating module, an integrating module, and an outputting module, each module being called via corresponding application programming interface (API);

receiving data from a plurality of data sources by calling the receiving module;

displaying, onto a graphical user interface (GUI), a plurality of selectable icons for receiving user input in selecting a set of attributes data related to generating a desired data model by calling the displaying module;

receiving the user input of the selected set of attributes data by calling the receiving module, wherein the set of attributes includes features data corresponding to what data to be utilized to generate the desired data model, label data corresponding to what a user wants to predict, and data corresponding to machine learning problem type;

automatically creating, by calling the creating module, an executable custom code by applying a no-code machine learning and artificial intelligence (ML/AI) algorithm onto the received data from the plurality of data sources and the selected set of attributes data and automatically simulating the custom code;

executing the custom code by calling the executing module;

calling, in response to executing, a backend platform for processing the received data from the plurality of data sources and the selected set of attributes data by calling the calling module, wherein the DIDPM imports a workbook, the workbook including the custom code, into an application, wherein the application is a first part of the no-code ML/AI algorithm for democratization of ML/AI and the backend platform is a second part of the no-code ML/AI algorithm;

automatically generating, in response to calling, the desired data model based on the processed received data and the selected set of attributes data by calling the generating module;

receiving user configuration data, wherein the configuration data includes data for a time series problem, data for a classification problem by calling the receiving module, data for an anomaly detection-based problem and data for a clustering problem; and

outputting, by calling the outputting module, a classification-based model based on the classification problem wherein model output is discrete values out of a set of n possible values, an anomaly detection-based model based on the anomaly detection-based problem wherein model output indicates, for each individual instance of data, whether it is considered to be normal or anomalous, and a clustering-based model based on the clustering problem wherein model output is a grouping of data into k different, non-overlapping partitions.