The document introduces various prediction tools in XLMiner including multiple linear regression, K-nearest neighbors, regression trees, and neural networks. It provides brief descriptions of each technique and examples of their applications to prediction problems using different types of predictor variables. Links are provided for more information on each tool's implementation and output in XLMiner.

1. Introduction toXLMiner™:PredictionXLMiner and Microsoft Office are registered trademarks of the respective owners.

2. PREDICTIONPrediction is the process of finding how the value of predictor variables predict the response variable based on the predictor variables, or to study the relationship between the response variable and predictor variables.XLMiner provides different tools to perform this task: Multiple Linear regression.K-nearest neighborsRegression treeNeural Networkhttp://dataminingtools.net

3. PREDICTION- Multiple linear regressionThis procedure performs linear regression on the selected dataset. This fits a linear model of the form Y= b0 + b1X1 + b2X2+ .... + bkXk+ e where Y is the dependent variable (response) and X1, X2,.. .,Xk are the independent variables (predictors) and e is random error. b0 , b1, b2, .... bk are known as the regression coefficients, which have to be estimated from the data. The multiple linear regression algorithm in XLMiner� chooses regression coefficients so as to minimize the difference between predicted values and actual values. Linear regression is performed either to predict the response variable based on the predictor variables, or to study the relationship between the response variable and predictor variables. For example, using linear regression, the crime rate of a state can be explained as a function of other demographic factors like population, education, male to female ratio etchttp://dataminingtools.net

4. PREDICTION- Multiple linear regressionCheck those options that you want to be displayed in the outputhttp://dataminingtools.net

5. PREDICTION- Multiple linear regression(output)Use the navigator to view other outputshttp://dataminingtools.net

6. PREDICTION- Multiple linear regression(output)http://dataminingtools.net

7. PREDICTION- K-nearest neighborsThe k-NN technique is like the regression technique – here, the nearest neighbours to a particular object have more weight than the distant ones.An object is classified according to a vote by its neighbours. It is then classified to the class most common in its k-neighbours.http://dataminingtools.net

8. PREDICTION- K-nearest neighborshttp://dataminingtools.net

9. PREDICTION- K-nearest neighbors(output)http://dataminingtools.net

10. PREDICTION- Regression treeA single output (prediction) variable, which should be numerical, and one or more input (predictor) variables exist. The input variables can be a mixture of continuous and categorical variables. A regression tree is a decision tree where each node of the tree tests the value of the predictor variable to determine the prediction variable. The leaf nodes of the tree contain the output variables. Regression tree is built through a process known as binary recursive partitioning. This is an iterative process of splitting the data into partitions, and then splitting it up further on each of the branchesSince the tree is grown from the training data set, when it has reached full structure it usually suffers from over-fitting (i.e. it is "explaining" random elements of the training data that are not likely to be features of the larger population of data). This results in poor performance on real life data. Therefore, it is pruned using the validation data set.Regression trees are not used for classification; rather, they are used to approximate real-valued functions.http://dataminingtools.net

11. PREDICTION- Regression treehttp://dataminingtools.net

12. PREDICTION- Regression treehttp://dataminingtools.net

13. PREDICTION- Regression treehttp://dataminingtools.net

14. PREDICTION- Regression tree-Full treehttp://dataminingtools.net

15. PREDICTION- Regression tree- Pruned Treehttp://dataminingtools.net

16. PREDICTION- Neural NetworksThe model for the artificial neural network is made when the records from the data base are processed one at a time and the computed value of their output is then compared with the actual value. The difference is again taken into account and fed back to the model so as to perfect it.This can go on for many iterations. XLMiner offers the architecture of multilayer feed-forward for modelling a neural network.http://dataminingtools.net

17. PREDICTION- Neural Networkshttp://dataminingtools.net

18. PREDICTION- Neural Networkshttp://dataminingtools.net

19. PREDICTION- Neural Networkshttp://dataminingtools.net

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