Predictive Modeling - SAS Enterprise Miner - Regression Models - Predictive Modeling Assignment Help

Internal Code: 1AJGJE

Predictive Modeling Assignment Help

TASK Select from the data sets available (or ones designated by your instructor or other available sources). Provide a thorough description of the data sets to include the number of cases, description of the inputs, target variables, description of the variables that could be used to develop predictive models, etc. For this assignment, you will need at least two data sets (one for linear regression with a continuous target variable and a different one with discrete binary target variable for logistic regression models). Choose your data sets carefully for the assignment: the linear regression data set selected must have a continuous target variable (i.e., the thing being predicted) the logistic regression data set selected must have a binary target variable (i.e., the thing being predicted) which is skewed (at least 75% of the outcomes are one type), 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 techniques. Develop complete prediction models. There should be at least two models of each type (two linear regression models and two logistic regression models) developed. Compare the linear regression models using the appropriate accuracy measures. Compare the logistic regression models using the appropriate accuracy measures 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.