Data Mining and Predictive Modelling

Introduction

Data mining is usually described as collecting information from recorded data, hence the name, mining. Predictive modeling is the event in which a model is made or chosen to accurately predict an outcome. Businesses have grown and science is a lot more complex, therefore data mining requires more computerized method of doing this. Software and computer applications have made data mining seem an effortless job, because it does not require a direct hands-on approach.

Data Mining

Data mining can be defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Data mining may also be defined as "the science of extracting useful information from large data sets or databases". Data mining is the principle of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but it is increasingly used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods.

Predictive Modeling

Predictive modeling is the process by which a model is created or chosen to try to best predict the probability of an outcome. In many cases the model is chosen on the basis of detection theory to try to guess the probability of a signal given a set amount of input data, for example given an email determining how likely that it is spam. Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set, say spam or 'ham'. Also, large organizations such as the Intergovernmental Panel on Climate Change use Predictive modeling to estimate sea level drops and rises.