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Predictive Analytics is a solution used by many companies today to obtain better use of large quantities of raw data by applying techniques used to predict future behavior within an organization, customers, products and services. Predictive analytics encompasses a variety techniques of data mining, stastics and game theory to analyze the current and historical facts to make predictions about events future.

Predictive models, the trends of the company and examine historical data to identify opportunities and risks. The forecasting models capture the relationships between the many factors leading to the evaluation of risks and potential with given conditions, determines decision making for candidate transactions are linked.

There are some basic and more sophisticated automatic analysis techniques. Three techniques basis include:

Profile data and transformations
Analysis of sequential patterns
Time Tracking Series

Profiling data and transformations are functions that analyze the row and column attributes and dependencies, formats for data exchange of merge fields, add records, and make the rows and columns.

The model of sequential analysis to discover relationships between rows of data. The sequential pattern analysis is used frequently observed to identify the products by sequential occurrence actions can be ordered in time. This frequently observed sequential occurrence of items (a so-called Sequential Patterns) must meet at least one user specified support. Understanding customer behavior is an example of long-term market analysis sequential pattern. More Examples include sequences of buying customers click stream meetings and calling patterns.

Time Series Monitoring tracks metric behavior the most important or strategies are undertaken. This is an ordered sequence of values a variable at regular intervals. Time Series Analysis says that the data points in time in May an internal structure (such as autocorrelation trend, or seasonal fluctuations) should be considered. Examples are the distribution channels show appreciation to customers, products and buying habits, budget analysis, stock analysis, analysis of census and projected staff.

Advanced predictive analytic techniques are:

"Time Series Forecasting
Profile data transformations and
"A Bayesian analysis
Regression
Classification
Unit of analysis or Association
Simulation
Optimization

Time Series Forecasting predicts the future value of a measure on the values of the past. The analysis time series with a model to predict future events based on known events of the past. Examples are stock prices and turnover.

Profiling data and processing functions to analyze the attributes of the row and column and dependencies, exchange formats data merge field records and take his turn and columns.

Bayesian analysis of capture concepts for predicting the likelihood of use. It is a statistical methods for estimating parameters of the underlying distribution on the observed distribution. An example is introduced in a court by a sworn individual used systematically "collect evidence for and against the guilt of the accused and see if it meets in its entirety, the threshold of "beyond a reasonable doubt."

Regression analysis is a statistical tool for the study relationships between variables. Normally, researchers have attempted to assess the effect of a cause variable on another, the impact of price increases in demand, for example, or the impact of changes in inflation in the money supply.

Classification of attributes used in the data to assign an object to a predefined class or predict the value of a numeric variable of interest. These examples are the analysis credit risk, the probability of purchase. Examples include acquisition, cross selling, attrition, credit rating and collections.

Clustering or segmentation of data into homogeneous subgroups based on the attributes separately. The group has a number of observations into subsets (clusters) so that the observations in the same group are similar. An example is the segmentation of the customer population.

The organization or analysis of association described significant associations between data elements. An example is the analysis of market basket. Analysis the market basket is a modeling technique on the theory that if you buy a certain product group more (or less) likely to buy another set articles.

The simulation models of system structure to assess the impact of management decisions or changes. The behavior of the simulation model will change according to individual models for all parameters is primarily for the environment. For example, restocking policies, contracts, reports, military training.

Optimization models of a system in terms apply restrictions, the best solution. Optimization models is part of a larger system that helps people to use to make decisions. The user can influence the model produces solutions and opinions before making a final decision on what to do. Examples include planning of shift workers, the problem of loading the train and book tickets.

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Article Source: ArticlesBase.com – Twelve Basic Predictive Analytics Techniques

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