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Regression uses existing values to forecast what other values will be. In the simplest case, regression uses standard statistical techniques such as linear regression. Unfortunately, many real-world problems are not simply linear projections of previous values. For instance, sales volumes, stock prices, and product failure rates are all very difficult to predict because they may depend on complex interactions of multiple predictor variables. Therefore, more complex techniques (e.g., logistic regression, decision trees, or neural nets) may be necessary to forecast future values.

Poll: Which of the following would you recommend as the best introductory book on data mining?

	Data Mining: Concepts and Techniques - Han & Kamber
	Data Preparation for Data Mining - Pyle
	Introduction to Data Mining - Tan, Steinbach & Kumar
	Principles of Data Mining - Hand, Mannila & Smyth
	Machine Learning - Mitchell
	The Elements of Statistical Learning - Hastie, Tibshirani & Friedman
	Introduction to Business Data Mining - Olson & Shi
	Predictive Data Mining: a practical guide - Weiss & Indurkhya
	Other
	Books are way too structured and expensive for me!

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The same model types can often be used for both regression and classification. For example, the CART (Classification And Regression Trees) decision tree algorithm can be used to build both classification trees (to classify categorical response variables) and regression trees (to forecast continuous response variables). Neural nets too can create both classification and regression models.

Regression is the oldest and most well-known statistical technique that the data mining community utilizes. Basically, regression takes a numerical dataset and develops a mathematical formula that fits the data. When you're ready to use the results to predict future behavior, you simply take your new data, plug it into the developed formula and you've got a prediction! The major limitation of this technique is that it only works well with continuous quantitative data (like weight, speed or age). If you're working with categorical data where order is not significant (like color, name or gender) you're better off choosing another technique.

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