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DATA 640: Ensemble I model using Statistical Analysis System Enterprise Miner | SAS Assignment Help
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Internal Code: 1IABE
Topic: Ensemble I model using SAS Enterprise Miner
Tasks:
- Select from the data sets available (CAR LEMON DATASET - PROVIDED). 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. Data sets selected for use must have at least 2,000 cases, a binary outcome, and a heavily skewed (at least 75% one outcome) target variable.
- Briefly explain the content of the data to include a description of the variables in the data sets, 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.
- 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.
- 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.
- Develop predictive models using the appropriate predictive modeling technique. Develop complete prediction models. At least one model of each type (e.g., bagging, boosting, random forest) must be developed, evaluated, and compared.
- 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.
- 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 and/or specific areas where such a model would be useful in your organization.