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Predictive analytics is a set of business intelligence (BI) technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behavior and events. Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future. [3]
A positivistic approach to behavior research, TRA attempts to predict and explain one's intention of performing a certain behavior.The theory requires that behavior be clearly defined in terms of the four following concepts: Action (e.g. to go, get), Target (e.g. a mammogram), Context (e.g. at the breast screening center), and Time (e.g. in the 12 months). [7]
Each behavioural change theory or model focuses on different factors in attempting to explain behaviour change. Of the many that exist, the most prevalent are learning theories, social cognitive theory, theories of reasoned action and planned behaviour, transtheoretical model of behavior change, the health action process approach, and the BJ Fogg model of behavior change.
Moreover, they implement machine learning techniques to predict the future behavior of users based on their past performance. The evidence of the achieved change in behavior, as well as important notifications during self-evaluation, are communicated with visual analytics tools such as performance graphs.
Predictive modelling uses statistics to predict outcomes. [1] Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. [2]
The theory of planned behavior (TPB) is widely utilized in the field of household financial behavior research. This theory helps to understand and predict various financial decisions and behaviors, including investment choices, debt management, mortgage use, cash, saving, and credit management.
The reasoned action approach (RAA) is an integrative framework for the prediction (and change) of human social behavior.The reasoned action approach states that attitudes towards the behavior, perceived norms, and perceived behavioral control determine people's intentions, while people's intentions predict their behaviors.
Every set of input values is fed into a neural network to predict a value . In order to predict the output accurately, the weights of the neural network (which represent how much each predictor variable affects the outcome) must be incrementally adjusted via backpropagation to produce estimates closer to the actual data.