Abstract: Systems and methods for automatically building a machine learning model are disclosed. A plurality of variables is displayed via a graphical user interface (GUI). A target variable and a first independent variable are identified from the plurality of variables. A parameter associated with the machine learning model is identified. Collected data is received via the GUI. A first machine learning model is built using as inputs, the parameter and the collected data associated with the first independent variable and the target variable. A change is made to at least a portion of the inputs used to build the first machine learning model. A second machine learning model is built based on the change. A prediction accuracy of the first machine learning model is compared to the prediction accuracy of the second machine learning model. Either the first or second machine learning model is selected based on the prediction accuracy.

Abstract: A system and method for generating and deploying a machine learning model for a real-time environment. User selected coefficients and training data are received via a graphical user interface. A first machine learning algorithm is invoked for generating a first machine learning model based on the received data. Accuracy of predictions by the first model are tested, and a determination is made that the accuracy of predictions of the first model is below a threshold value. In response to such a determination, a particular criterion is evaluated. In response to determining that the criterion is satisfied, a second machine learning algorithm is invoked for generating a second machine learning model based on the received data. The model is deployed instead of the first model for making real-time predictions based on incoming data.