use r and sas to predict mortgage loan pass rate

I want to predict the mortage loan pass rate, you can use those Predictive model as follow:

– Clustering Analysis

– Regression Model with Groups based on Clustering

– Simple, Multiple Regression on linear or nonlinear models

– Discrete Probability Model : Logistic Model

– Machine Learning techniques: Random Forest, Neural Network Analysis

before that, please to some

Descriptive Analytics – Proc mean, Proc summary, Proc Uniariate, Proc sgplot of gplot, and Maps – Correlation analysis and Analysis of Variance (ANOVA) – Other relative analyses including histograms and statistics

I attached the link to access the data: https://bigblue.depaul.edu/jlee141/econdata/hmda/

You need to finish those part.

2. Empirical Methodology (1 to 2 pages) • Describe your estimating equation(s) in words and in math (i.e. include the exact regression equation(s) in this section). • Describe how the methodology is going to help you answer your question comparing to a regression model.

3.Results (3 to 4 pages) •

Present your results with tables and/or graphs/charts (not raw output from SAS or R please).

• Descriptive Analytics – Proc mean, Proc summary, Proc Uniariate, Proc sgplot of gplot, and Maps – Correlation analysis and Analysis of Variance (ANOVA) – Other relative analyses including histograms and statistics

• Predictive Analytics (All required to apply to your model)

– Clustering Analysis

– Regression Model with Groups based on Clustering

– Simple, Multiple Regression on linear or nonlinear models

– Discrete Probability Model : Logistic Model

– Machine Learning techniques: Random Forest, Neural Network Analysis

• Describe your results in words (both the signs and magnitudes). The emphasis should be on coefficients that relate to your research question, but you may mention others. Certainly, you do NOT need to describe (in words) ALL of the coefficients, just the important ones.

• Performance your model depending upon the predictability. You need to show the strength of your model by comparing other alternative models. The performances of models should be measured using a test data set, that was not used to estimate the main model.

4.Summary of Project (1 to 2 pages) • Summarize everything briefly (i.e. in one paragraph you should be able to state your project question, empirical approach, and results). • Potential shortcoming of your project and desirable future works.

5.Bibliography (1 page) Any related work with your work

Appendix: SAS or R command file Include all SAS or R commands used to generate the output. Codes and Data needs to be included in separate files. Make sure all submitted SAS or R codes without any errors. There will be very high penalty if they are not working with errors

I attached the Guideline and already done the introduction and data part. I need both code and report.