This record may contain features such as age, gender, zip code, number of items purchased in the last six months, and number of items returned, combined with a target variable that may be used to inform us if this customer has churned or not in the past. It puts data in categories based on what it learns from historical data. A well-known learning algorithm used for feed-forward NNs is called back-propagation. Predictive models can also benefit from different modeling techniques at the same time. The rating agencies failed with their ratings on the US$600 billion mortgage backed Collateralized Debt Obligation (CDO) market. It is just one of many. In between input and output layers though, the neural network may be configured with any number of hidden-layers and nodes. The availability of “big data” and cost-efficient processing power is expanding the applicability of predictive data-driven techniques in different industries. Figure 3 shows an example in which input data has been divided into two clusters. In this way, for example, it can be used to predict the risk of customer churn or defection, in case of people data, or the risk of machinery breakdown, in case of sensor data. Note that the tree can be represented by a set of rules. An SVM maps input data vectors into a higher dimensional space, where an “optimal hyperplane” that separates the data is constructed. Once data has been collected for relevant predictors, a statistical model is formulated. Sensor data, on the other hand, comes from a barrage of devices used to monitor roads, bridges, buildings, machinery, the electric grid, and the atmosphere and climate. retention campaign you wish to predict the change in probability that a customer will remain a customer if they are contacted. In fact, data scientists are hard at work building predictive solutions with the data we as a society are gathering in an ever-expanding pace. It allows for the error, or the difference between target and output, to be propagated back through the network, which is then used to adjust the synapse weights linking the network nodes. The larger the distance between the two hyperplanes, the more accurate the model is assumed to be. Selecting an appropriate network structure is important for building a good predictive model. Once represented as a PMML file, a predictive model can be moved right away from the scientist’s desktop, where it was developed, to the operational environment, where it is put to work. In all data collection, the collector first defines the set of variables for which data is collected. No matter the type of model though, one thing is for certain: Predictive models are already shaping our experiences wherever we go and whatever we do. As mentioned above, model building involves a great deal of data analysis and massaging. Predictive modelling is often contrasted with causal modelling/analysis. In the example shown in Figure 3, had the number of clusters been set to three instead of two, an extra cluster would have been created, which would probably have clouded up the true nature of the data (yellow triangles or purple squares?). NNs, SVMs, decision trees, linear and logistic regression, clustering, association rules, and scorecards are the most popular predictive modeling techniques used by data scientists today to learn patterns hidden in the data. Broadly speaking, there are two classes of predictive models: parametric and non-parametric. For instance, when presented with a specific set of inputs, her output will match the target. In the latter, one seeks to determine true cause-and-effect relationships. They help healthcare providers design and implement preventive life saving measures given our susceptibility towards a particular disease. & Newman, D.J. 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