Course Code: 0A035G
IBM : Advanced Predictive Modeling Using IBM SPSS Modeler (v16)
Velisa Africa is the official Global Knowledge partner in Sub-Saharan Africa. Course details : https://www.globalknowledge.co.uk/courses/ibm/business_analysis/0a035g.html
This course builds on the courses Classifying Customers Using IBM SPSS Modeler (V16) and Predicting Continuous Targets Using IBM SPSS Modeler (V16). It presents advanced techniques to predict categorical and continuous targets. Before reviewing the modeling techniques, data preparation issues are addressed such as partitioning and detecting anomalies. Also, a method to reduce the number of fields to a number of core fields, referred as components or factors, is presented. The next two modules focus on advanced predictive models, such as Decision List, Support Vector Machines and Bayes Net. Following this presentation, two modules present methods to combine individual models into a single model in order to improve predictive power, including running and evaluating many models in a single run, both for categorical and continuous targets.
You should have:
- Completion of the course Introduction to IBM SPSS Modeler and Data Mining (V16) or experience in analyzing data with IBM SPSS Modeler.
- Familiarity with basic modeling techniques, either through completion of the courses Classifying Customers Using IBM SPSS Modeler (V16) and Predicting Continuous Targets Using IBM SPSS Modeler (V16), or by experience with predictive models in IBM SPSS Modeler.
Preparing Data for Modeling
- Addressing general data quality issues
- Handling anomalies
- Selecting important predictors
- Partitioning the data to better evaluate models
- Balancing the data to build better models
- Using the Ensemble node to combine model predictions
- Improving the model performance by meta-level modeling
Finding the Best Predictive Model
- Find the best model for categorical targets
- Find the best model for continuous targets
This intermediate-level course is for users of IBM SPSS Modeler responsible for building predictive models (also known as classification models).
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