Internal Code: 1IBJF
Topic: Data Set - Special Air Services (SAS) Assignment Help
1. Select from the data sets available (car lemon data set - to be sent via email). Provide a thorough description of the data set(s) to include the number of cases, description of the inputs, target variable, description of the variables that could be used to develop predictive models, etc.
2. Briefly explain the content of the data to include a description of the variables in the data set, the number of cases, etc. Include a screenshot of the data (not all cases need be shown, but be sure all relevant variables are visible). Provide a clear description of the purpose of the model being developed.
3. Explore the data by searching for anticipated relationships, unanticipated trends and anomalies – to gain deeper understanding and ideas. Use the SEMMA explore option to examine the data set you have created and look for interesting anomalies or relationships.
4. Cleanse and modify the data by removing errors, imputing missing values (as appropriate), transforming the variable distributions as necessary, and creating and selecting appropriate variables. Use the appropriate SEMMA options to cleanse the dataset as necessary. Investigate and discuss any “feature engineering” done for the data set.
5. Develop predictive models using the appropriate predictive modeling technique. Develop complete prediction models. At least three different models utilizing the ensemble node must be developed, evaluated, and compared.
6. Using appropriate accuracy measures, assess the resultant models. Provide a complete assessment of the different models created using the SAS Enterprise Miner assessment options. Explain clearly any insights or conclusions from the accuracy measures. NOTE: Assignment must include a comparison of the ensemble node models results created for this assignment with at least three simpler models such as decision tree, regression, neural networks, etc.
7. Conclusions and takeaways. Provide clear and concise conclusions about the project to include lessons learned and any suggested improvements for future development. Suggest future enhancements for the analysis SAS Enterprise Miner MUST BE USED!!! Log in information for SAS Enterprise Miner will be sent via email In addition, rubic will be sent to email.
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