1. Prepare a summary of the paper containing the following sections (You may use bullet points to summarize multiple points if you wish.) (Hint: Be mindful of the bolded phrases below.): 2. Generate an R data frame from the bank-full.csv file. Submit a screen shot. Hint: One way of doing this is to bring up RStudio. Point it to the working directory where the csv and text files are. Import the csv file using RStudio. Using the console or packages in RStudio bring up Rattle. Now Rattle will be able to see the data frame (R dataset / Data name bank.full). 3. Partition the data frame into a 70/30 split using 42 as the seed.Submit a screen shot.

1. BIAM 560 Final Project Exercises
For more classes visit
www.snaptutorial.com
1. Prepare a summary of the paper containing the following sections
(You may use bullet points to summarize multiple points if you wish.)
(Hint: Be mindful of the bolded phrases below.):
2. Generate an R data frame from the bank-full.csv file. Submit a
screen shot. Hint: One way of doing this is to bring up RStudio. Point it
to the working directory where the csv and text files are. Import the csv
file using RStudio. Using the console or packages in RStudio bring up
Rattle. Now Rattle will be able to see the data frame (R dataset / Data
name bank.full).
3. Partition the data frame into a 70/30 split using 42 as the seed.Submit
a screen shot.
4. Execute an Exploratory Data Analysis of your choosing. Submit
screen shot(s) and a summary of what you have found.
5. Generate a decision tree model using the defaults. Submit screen
shot(s) and a summary of what you have found.
6. Evaluate it with ROC and Lift curves. Submit screen shot(s) and a
summary of what you have found.
7. Generate a random forest model using the defaults. Submit screen
shot(s) and a summary of what you have found.
8. Evaluate it with ROC and Lift curves. Submit screen shot(s) and a
summary of what you have found.
9. Generate a support vector machine model using the defaults.Submit
screen shot(s) and a summary of what you have found.

2. 10. Evaluate it with ROC and Lift curves. Submit screen shot(s) and a
summary of what you have found.
11. If this were your data which model would you recommend be
implemented in your bank? Why?
12. Prepare a description of what you have learned and how long it has
taken you to do it.
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BIAM 560 Week 5 Homework Assignment
Neural Networks
For more classes visit
www.snaptutorial.com
In this case study, we will assess neural networks. If you have
installation issues, you will need Version 3 of R and not Version 2.
You will also need to create a working directory for this course and
you can call it “BIAM 560.”
Your job is to do the following.
·Work through the neural networks tutorial in Chapter 8 beginning
on page 194.

3. ·Include screenshots from your efforts that are similar to the
following figures in the text: 8.9 and 8.19.
·Follow the instructions in Section 7 on pages 198–199. Be sure to
provide screenshots of your results to support your description of
the results.
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BIAM 560 Week 6 Homework Assignment
Cluster Analysis and Principal Components
Analysis
For more classes visit
www.snaptutorial.com
In this case study, we will assess Cluster Analysis and Principal
Components Analysis. If you have installation issues, you will need
Version 3 of R and not Version 2. You will also need to create a
working directory for this course and you can call it “BIAM 560.”
Your job is to do the following.
·Work through Section 10.4 Ward's Method Tutorial beginning on
page 248.

4. ·Include screenshots from your efforts that are similar to the
following figures in the text.
·Do Step 13: Answer the request for similarities and be sure and
provide screenshots to substantiate your answer.
**************************************************
BIAM 560 Week 7 Final Course Project
For more classes visit
www.snaptutorial.com
Final Project Exercises
1. Prepare a summary of the paper containing the following sections
(You may use bullet points to summarize multiple points if you wish.)
(Hint: Be mindful of the bolded phrases below.):
2. Generate an R data frame from the bank-full.csv file. Submit a
screen shot. Hint: One way of doing this is to bring up RStudio. Point it
to the working directory where the csv and text files are. Import the csv
file using RStudio. Using the console or packages in RStudio bring up
Rattle. Now Rattle will be able to see the data frame (R dataset / Data
name bank.full).

5. 3. Partition the data frame into a 70/30 split using 42 as the seed.Submit
a screen shot.
4. Execute an Exploratory Data Analysis of your choosing. Submit
screen shot(s) and a summary of what you have found.
5. Generate a decision tree model using the defaults. Submit screen
shot(s) and a summary of what you have found.
6. Evaluate it with ROC and Lift curves. Submit screen shot(s) and a
summary of what you have found.
7. Generate a random forest model using the defaults. Submit screen
shot(s) and a summary of what you have found.
8. Evaluate it with ROC and Lift curves. Submit screen shot(s) and a
summary of what you have found.
9. Generate a support vector machine model using the defaults.Submit
screen shot(s) and a summary of what you have found.
10. Evaluate it with ROC and Lift curves. Submit screen shot(s) and a
summary of what you have found.
11. If this were your data which model would you recommend be
implemented in your bank? Why?
12. Prepare a description of what you have learned and how long it has
taken you to do it.
**************************************************