Abstract: A method for performing data prediction includes: obtaining a dataset; generating prediction parameters using the dataset; identifying significant variables in the dataset; predicting seasonality of the dataset based on the significant variables; determining uncertainty of the prediction parameters; performing the data prediction by minimizing randomness and uncertainty of the dataset; and displaying the data prediction on a graphical user interface.

Abstract: A mechanism is provided to proactively forecast gross margin for a business unit of an organization utilizing machine learning techniques. Embodiments provide a cascading-architecture machine-learning model to predict gross margin for a period (e.g., an upcoming quarter), utilizing metrics both internal and external to the organization. Internal metrics can include list price change, discounting change, cost impact, and the like. External metrics can include customer information such as propensity to purchase and purchase consumption.

Abstract: A method, system and computer-usable medium are disclosed for machine learning to identify service request records associated with an account that is likely to escalate. Certain aspects of the disclosure include generating a random forest model using a training set of service request records to determine a probability of escalation for service requests of the training set; applying the random forest model to a current set of service request records to determine an escalation probability for service requests in the current set; and assigning service request records in the current set to a plurality of escalation probability bins, wherein the service request records of the current set are generally equally divided between the plurality of escalation probability bins, and wherein the service request records of the current set are assigned to a probability bin based on the escalation probability of the service request record.

Abstract: A mechanism is provided to proactively forecast gross margin for a business unit of an organization utilizing machine learning techniques. Embodiments provide a cascading-architecture machine-learning model to predict gross margin for a period (e.g., an upcoming quarter), utilizing metrics both internal and external to the organization. Internal metrics can include list price change, discounting change, cost impact, and the like. External metrics can include customer information such as propensity to purchase and purchase consumption.

Abstract: A method, system and computer-usable medium are disclosed for machine learning to identify service request records associated with an account that is likely to escalate. Certain aspects of the disclosure include generating a random forest model using a training set of service request records to determine a probability of escalation for service requests of the training set; applying the random forest model to a current set of service request records to determine an escalation probability for service requests in the current set; and assigning service request records in the current set to a plurality of escalation probability bins, wherein the service request records of the current set are generally equally divided between the plurality of escalation probability bins, and wherein the service request records of the current set are assigned to a probability bin based on the escalation probability of the service request record.

Abstract: A method, system and computer-usable medium are disclosed for generating a stacked prediction model and using the stacked prediction model to forecast market behavior. One embodiment is directed to a computer-implemented method for forecasting market behavior comprising: accessing stored time-series sequenced data representing historical market behavior; applying multiple prediction models to the time-series sequenced data; determining a respective error associated with application of each multiple prediction model to the time-series sequenced data; generating a stacked prediction model using at least two of the multiple prediction models, wherein the stacked prediction model includes a weighting factor for each of the prediction models used in the stacked prediction model, wherein the weighting factor for each of the prediction models in the stacked prediction model employs an inversion of the respective error in the prediction model; and applying the stacked prediction model to forecast market behavior.

Abstract: A system, method, and computer-readable medium are disclosed for manufacturing and configuring an information handling system, comprising: generating a first level forecast prediction, the first level forecast prediction being based upon seasonal factors and a trend component; generating a second level forecast prediction, the second level forecast prediction being based upon an average error between current time period revenue data and a plurality of previous time periods revenue data; generating a third level forecast prediction, the third level forecast prediction being based upon a remaining portion of a particular time period and data relating to an already completed portion of the particular time period; generating a final forecast prediction, the final forecast prediction being based upon the first level forecast prediction, the second level forecast prediction and the third level forecast prediction; and, adjusting inventory used for manufacturing and configuring the information handling system based

Abstract: Chat categorization uses semi-supervised clustering to provide Voice of the Customer (VOC) analytics over unstructured data via an historical understanding of topic categories discussed to derive an automated methodology of topic categorization for new data; application of semi-supervised clustering (SSC) for VOC analytics; generation of seed data for SSC; and a voting algorithm for use in the absence of domain knowledge/manual tagged data. Customer service interactions are mined and quality of these interactions is measured by “Customer's Vote” which, in turn, is determined by the customer's experience during the interaction and the quality of customer issue resolution. Key features of the interaction that drive a positive experience and resolution are automatically learned via machine learning driven algorithms based on historical data. This, in turn, is used to coach/teach the system/service representative on future interactions.