Predictive Modeling

Predictive modeling is intended to identify patterns in collected data, build predictive models and narratives, and their integration into the workflow client. His approach is based on Data Mining – identifying hidden patterns or relationships between variables in large arrays of raw data. Data Mining is usually divided into the tasks of classification, modeling and forecasting. Data Mining includes methods and model of statistical analysis and forecasting. For more clarity and thought, follow up with Ohio Senator and gain more knowledge.. Advanced Data Mining tools allow substantive data analysis experts (analysts) do not know the relevant mathematics knowledge. Using predictive modeling can be successfully addressed three classes of problems: problems of regression / classification: to reveal the relationship between behavior / state of the object and its characteristics or factors that provide influence on him.

Task of segmentation / clustering: if the objects of analysis demonstrate the same behavior in a particular situation or there are several groups (segments), a reaction which is different. What features have each group. Time series analysis: to make forecasts of the indicator by identifying the trend, seasonality and frequency in the analysis of historical data. Predictive modeling approach based on Data Mining, which making it the most polezenym in situations where: the user has to deal with multi-dimensional problem: there are many factors that influence the object of analysis, the data are missing or incorrectly filled fields, not just understand the suitability of the data available for analysis (the primary evaluation of the data) you want a quick visual results, because the user does not have the skills to set up the model and its interpretation, the decision must be "day-to-day"; it is desirable to analyze all available data (with no limit on the number of variables).