Predictive Modeling and Predictive Models

A predictive model is a system created and used to perform prediction. Predictive models can predict or forecast variety of things and events. For example, future share prices, credit defaults, insurance claims, customer ordering products, and so on. Predictive models are developed from past historical data or from purposely collected data through sampling. Typical examples may include;

Insurance
Annual insurance policy applications and claims records can be used to develop models that can predict probability (or level of risk) of insurance claims. Predictive models use demographic and financial information of policy holders along with characteristics of insured objects in determining risk levels. For more, please read Insurance Risk Modeling.

Credit loans
Predicting default risk for credit loan applications is another use of predictive models. Data collected from past customer loans, including demographic and financial information of borrowers, can be used to build predictive models that can predict likelihood of loans being defaulted. For more, please read Credit Risk Modeling.

Marketing
Predictive modeling can be used for various tasks. For example, from past customer purchasing records, you can develop models that can select customers who are likely to buy your new products. Another example is customer churn detection. Using past customer information, models that can predict customers who are likely to churn near future. This can be very useful in retention marketing. For more, please read Customer Churn Modeling.

* For predictive modeling software, please read Data Mining Software.

Predictive Modeling Software Tools

Predictive modeling is done automatically by computer software that can learn patterns from data. CMSR Data Miner supports powerful predictive modeling tools. Users can build models with the help of intuitive model visualization tools. Application of models is very easy. Users can apply models directly to user data using built-in database interface tools. CMSR comes with the following predictive modeling software programs;

  • Neural Network
    Neural network is a predictive model which is based on the architecture of, say, our brains. It can be used to predict both numerical values and categorical classifications. Generally speaking, neural net offers most accurate and versatile predictive models. For more, please read Neural Network.

  • Decision Tree
    Decision tree develops predictive models based on recursive segmentation. Decision tree models have tree-like structures. As the rule, decision is made based on the democratic principle: the winner takes all. If a category of a decision node has the largest number of cases, it will be the predicted category. Of course, this leads to certain limitations! To overcome this, StarProbe data miner also uses probability. For more, please read Decision Tree Classifier Software

  • Regression
    Compared to above methods, regression may be very limiting and inflexible, since all categorical information should be encoded into numerical variables. However, regression can be very useful in developing mathematically oriented models with simple variable sets. Especially, time-series regression analytics are very useful in balanced scorecard applications.

* For predictive modeling software, please read Data Mining Software.

How can you develop predictive models? To learn more about predictive modeling,
please read The Cookbook for Predictive Analytics.

Key requirement for predictive modeling

The most important factor that can lead to successful implementation of predictive modeling is the availability of "predictive" information in your data. It is noted that predictive models are statistically-developed patterns extracted out of past historical data or purposely collected sampling survey data. With proper data representing predictive patterns of application domains, accurate predictive models can be developed quite easily. For more, please read Cookbook for Predictive Analytics.

Neural Network and Deep Learning Style Modeling

Neural networks are very robust and powerful. It can learn very fine details of information. This can be a problem if statistical patterns are desired as in predictive modeling. Large networks with a large number of input and hidden layer nodes can lead to overfitting. A better modeling method is to decompose into multiple models with a fewer number of input and hidden layer nodes. Then integrate models using higher level neural network(s). This deep learning style modeling can produce better predictive result.

For more, please read RME-EP Expert System Shell with Predictive Modeling and Deep Learning.