Abstract: Systems and methods associated with incident prediction using machine learning techniques are disclosed. In one embodiment, an exemplary method may comprise obtaining current raw log data from at least one application log of at least one software application, converting the current raw log data into current tabular log data, applying one or more sampling techniques to the current tabular log data to form current balanced log data, the current balanced log data including incidents of failures, applying one or more machine learning techniques to the current balanced log data to generate an application failure predictive model, and predicting, based on future balanced log data, at least one future failure of the software application using the application failure predictive model.

Abstract: Systems and methods associated with incident prediction using machine learning techniques are disclosed. In one embodiment, an exemplary method may comprise obtaining current raw log data from at least one application log of at least one software application, converting the current raw log data into current tabular log data, applying one or more sampling techniques to the current tabular log data to form current balanced log data, the current balanced log data including incidents of failures, applying one or more machine learning techniques to the current balanced log data to generate an application failure predictive model, and predicting, based on future balanced log data, at least one future failure of the software application using the application failure predictive model.

Abstract: Systems and methods associated with incident prediction using machine learning techniques are disclosed. In one embodiment, an exemplary method may comprise obtaining current raw log data from at least one application log of at least one software application, converting the current raw log data into current tabular log data, applying one or more sampling techniques to the current tabular log data to form current balanced log data, the current balanced log data including incidents of failures, applying one or more machine learning techniques to the current balanced log data to generate an application failure predictive model, and predicting, based on future balanced log data, at least one future failure of the software application using the application failure predictive model.

Abstract: Systems and methods associated with incident prediction using machine learning techniques are disclosed. In one embodiment, an exemplary method may comprise obtaining current raw log data from at least one application log of at least one software application, converting the current raw log data into current tabular log data, applying one or more sampling techniques to the current tabular log data to form current balanced log data, the current balanced log data including incidents of failures, applying one or more machine learning techniques to the current balanced log data to generate an application failure predictive model, and predicting, based on future balanced log data, at least one future failure of the software application using the application failure predictive model.

Abstract: Systems and methods associated with incident prediction using machine learning techniques are disclosed. In one embodiment, an exemplary method may comprise obtaining current raw log data from at least one application log of at least one software application, converting the current raw log data into current tabular log data, applying one or more sampling techniques to the current tabular log data to form current balanced log data, the current balanced log data including incidents of failures, applying one or more machine learning techniques to the current balanced log data to generate an application failure predictive model, and predicting, based on future balanced log data, at least one future failure of the software application using the application failure predictive model.