Predictive Analytics: A Survey, Trends, Applications, Opportunities’ and Challenges for Smart City Planning
Autour(s)
- Chidi Yun, Miki Shun, Utian Junta, Ibrina Browndi
Abstract
Predictive analysis is an advanced branch of data engineering which generally predicts some occurrence or probability based on data. Predictive analytics uses data-mining techniques in order to make predictions about future events, and make recommendations based on these predictions. The process involves an analysis of historic data and based on that analysis to predict the future occurrences or events. A model can be created to predict using Predictive Analytics modeling techniques. The form of these predictive models varies depending on the data they are using. Classification & Regression are the two main objectives of predictive analytics. Predictive Analytics is composed of various statistical & analytical techniques used to develop models that will predict future occurrence, events or probabilities. Predictive analytics is able to not only deal with continuous changes, but discontinuous changes as well. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics.