BIAM 560 Final Project Exercises
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www.tutorialrank.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. 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. ****************************************
BIAM 560 Week 5 Homework Assignment Neural Networks
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www.tutorialrank.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. ·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. ****************************************
BIAM 560 Week 6 Homework Assignment Cluster Analysis and Principal Components Analysis
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www.tutorialrank.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.
¡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
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www.tutorialrank.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). 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.
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