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Assignment – Decision Tree model development using Cognos Analytic

Activities

1. Select from the datasetsprovided (or ones designated by your instructor or other available sources). Provide a brief description of the datasets to include the number of cases, description of the inputs, description of the variables that could be used to develop predictive models, etc. (NOTE: predictive models are developed better with larger data sets that have many cases and possible inputs from which to select. Part of your grade for this assignment will be based on the robustness of the data set used.)

2. Examine the datasets and eliminate mistakes, bad records, data entry errors, and outliers as appropriate. You may also want to do some data refinement and/or exploratory analysis prior to developing predictive models to further identify the key purpose of the predictive models.

Using Cognos Analytics:

3. Develop prediction models:
a) Develop complete Decision Tree prediction models.There should be at least two models developed:
• Be sure to specify which variable is being predicted and which variables are to be used to predict the outcomes.
• Discuss in detail the models and why the target variable is important to predict.

b) For each of the models created:
• Drill down into the results and interpret what the results indicate about the relationship between the input variables and the target variable. Which variables are the strongest predictors? What is the single best predictor variable? What combination of variables seems to give the best answer? Why?

• Provide the rules found and discuss how the use of these “rules” would be useful in your organization. How would you implement the use of a rule-based approach? How would the models be accepted?

• Discuss the Sunburst diagram. What additional information and/or insights does it provide?

• Create some other combinations of variables. See if you can derive a more accurate model, or a model that is equally accurate but simpler (i.e., has fewer variables).Consider how to improve the model (e.g., removing extraneous variables, improving data quality, etc.) Discuss your findings in detail. Consider creating additional variables that might add value to the model. Support your findings.

Attachment:- Analytics.rar