1. A method for predicting demand of networked computing resources, comprising: generating and testing multiple models comprising a linear model, a quadratic model, and a cubic model for predicting website data demand of an event; selecting a reference model with a lowest level of error among the multiple models; training the selected reference model by boosting the selected reference model; training the selected reference model by bagging the selected reference model; selecting one of the boosted selected reference model, the bagged selected reference model, and the selected reference model with the lowest level of error; inputting historical information which corresponds to data demand of events similar to the event into the selected one of the boosted selected reference model, the bagged selected reference model, and the selected reference model; generating, by at least one computing device, a predicted demand spike curve having a Gaussian distribution using the selected one of the boosted selected reference model, the bagged selected reference model, and the selected reference model and the historical information; generating a total predicted demand curve by combining the predicted demand spike curve with predicted cyclical demand; provisioning, by the at least one computing device, a plurality of website servers to handle the total predicted demand curve; dynamically updating the total predicted demand curve by using updated historical information corresponding to a sliding window of time; evaluating an accuracy of the selected one of the boosted selected reference model, the bagged selected reference model, and the selected reference model based on the dynamically updated total predicted demand curve by generating a graph to determine a success level of the selected one of the boosted selected reference model, the bagged selected reference model, and the selected reference model for a specified period of time during the event; switching to another model for a next period of time during the event in response to the selected one of the boosted selected reference model, the bagged selected reference model, and the selected reference model not achieving the success level for the specified period of time during the event; and provisioning based on the evaluating and the switching, by the at least one computing device, the website servers based on an updated demand curve.