DATA640 – Advanced Predictive Modeling, SAS Enterprise Miner, Analytics Project - Statistics Assignment Help

Assignment Task -                 
 

Advanced Predictive Modeling
1-Include the previous description (improved or modified as required) of the variables in the data sets, the number of cases, etc. Include a screenshot of the data (not all cases need to be shown, but be sure all relevant variables are visible).   Provide a clear description of the purpose of the models being developed.

2-Cleanse and modify the data (if necessary) by removing errors, imputing missing values (as appropriate), transforming the variable distributions as necessary, and creating and selecting appropriate variables.   You may use the same section from assignment 1 (with any necessary improvements or modifications).  Investigate and discuss any “feature engineering” done for the data set.

2-Outline and explain the predictive models developed. Outline prediction models developed. There should be at least two models developed for each of the model types (bagging, boosting, random forest, gradient boosting). Vary the appropriate parameters of the models accordingly. Provide a table outlining the models developed (without results) which denotes aspects that differentiate the models (i.e., the differences in the models). 
  
3-The imbalanced target variable must be addressed and accounted for using one or more of the three methods outlined in the classroom resources (e.g., balancing the data set, utilizing a cost function, changing the cutoff criterion). Explain your approach for adjusting for the imbalanced target distribution.

4-Assess the resultant models. Provide a complete assessment of all of the different models created.  Include all relevant aspects of the model assessment in a clear table, labeled accurately. Explain clearly insights and results gleaned from the table.  Compare the Ensemble models with previously created models from assignment 1.

5-Conclusions and takeaways. Provide specific and concise conclusions about the analytics project to include identification of the “champion” model and why.

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