Data Mining

• Example 1: A simple project for creating standard reports and summaries
  
• Example 2: Visual Data Mining
  Statistica and Statistica Data Miner include a large selection of graphs and charts that can be specified and edited interactively or inserted as nodes into a workspace. The purpose of this example is to illustrate how graphical methods alone can provide valuable insights into interesting (important) relationships between the variables in a data set.
• Example 3: Predictive data mining and deployment for a continuous output variable
  
• Example 4: Predictive Data Mining for Categorical Output Variable (Classification)
  
• Example 1: Association Rules Applied to Consumer Preferences
  
• Boosted Trees
  
• Developing Credit Scoring Model for Data Miner Recipe - Example
  After the Data preparation step is completed, the Data for analysis step is selected automatically.
• Example 1: CHAID Classification Tree
  
• General Classification and Regression
  
• Generalized Additive Models Example
  
• Generalized EM and k-Means Cluster Analysis
  
• Example: Goodness of Fit Indices for Regression Predictions
  
• Independent Component Analysis Example
  You can use your saved ICA models for deployment whenever needed. To do this, you first need to have a data set loaded in the Statistica environment. This data set need not be the same as the one you used to create the model, i.e., training data. In fact, this data set is often different from the training data.
• Interactive Trees (C&RT, CHAID) Example
  
• Machine Learning
  
• MARSplines Example
  
• Example: Using SIC Codes for Building a Predictive Model
  This example is based on the example data set AnalyzingSICCodes.sta . This data file contains a categorical dependent variable with information on the profitability of a transaction with industrial clients in various industries, as recorded in the SIC Codes variable.
• Process Optimization
  
• Random Forests
  
• Rapid Deployment of Predictive Models
  
• Sequence, Association, and Link Analysis
  
• Statistica Automated Neural Networks (SANN)
  
• Text and Document Mining, Web Crawling
  
• Weight of Evidence (WoE) example
  This example illustrates how the Weight of Evidence (WoE) module can be used in an analysis project for risk assessment. Input a set of predictor variables into the analysis to find optimal coding for both continuous and categorical variables. Their resulting weight of evidence can be used as continuous inputs for Logistic Regression, improving that model’s performance.

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