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Predictive analytics extracts information from data sets and uses it to anticipate future trends and behavior patterns based on statistics and data mining Ramakrishnan and Madure, Casino analysis The real trick is to find the predictive model best suited for the outcome one is trying to study Ramakrishnan and Madure, and this is no easy feat.
Data mining — the process whereby hidden patterns within data sets are discovered — is a component of predictive analytics that entails an analysis of data to identify trends and patterns of relationships among data sets Ramakrishnan and Madure, Casino analysis put is simply, data mining helps transform raw data into usable information.
Data must be gathered from disparate sources and then seamlessly integrated into a data warehouse that can then cleanse it and make it ready for consumption. Trends that surface from the data mining can help in monetization, as well as in future advertising and marketing campaigns. For casino properties, data mining can cull through data from such disparate sources and departments as sales, credit, and marketing, thereby allowing users to measure patron behavior on more than a hundred different attributes, which is a far cry from the three or four different variables that statistical modeling offers.
The most common data mining techniques are: These focus on establishing a mathematical equation as a model to represent the interactions between the different variables under consideration. These analyze the relationship between the response or dependent variable and a set of independent or predictor variables.
This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters. These parameters are adjusted so that a measure of fit is optimized. Much of the effort in model fitting is focused on minimizing the size of the residual, as well as ensuring that it is randomly distributed with respect to the model predictions.
These are used when the exact nature of the relationship between inputs and output is not known.
A key feature of neural networks is that they learn the relationship between inputs and output through training. There are three types of training in neural networks used by different networks, supervised and unsupervised training, reinforcement learning, with supervised being the most common one.
This method transforms information about a binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model. Used to identify the strategy that is most likely to reach a goal.
It is a decision support tool that uses a graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. A method of marketing testing by which a baseline control sample is compared to a variety of single-variable test samples in order to improve response rates.
A classic direct mail tactic, this method has been recently adopted within the interactive space to test tactics such as banner ads, emails and landing pages.
These are used to predict or forecast the future behavior of variables. These models account for the fact that data points taken over time may have an internal structure such as autocorrelation, trend or seasonal variation that should be accounted for.
A method used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. A branch of statistics that deals with death in biological organisms and failure in mechanical systems.
It involves the modeling of time to event data; in this context, death or failure is considered an "event" in the survival analysis literature — traditionally only a single event occurs, after which the organism or mechanism is dead or broken.
An optimization problem for finding closest points in metric spaces. The method does not impose a priori any assumptions about the distribution from which the modeling sample is drawn. It involves a training set with both positive and negative values. An area of machine learning in which formal rules are extracted from a set of observations.
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