First,

- select a classification methodology to learn in your project. Select either Classification Trees or Support Vector Machines.

- To actually get the data used in the ISLR examples, you will likely need to download an R package called ISLR; it contains the data sets used in the text.

- Begin by introducing your reader to the corporation from which your stock data (Leggett & Platt Incorporated) comes. Tell the reader something you learned about that corporation that you found interesting, something which would demonstrate to a recruiter that you possess curiosity and the ability to employ it.

- Then, using trimmed screenshots where needed from Excel, sketch out for the reader how you converted your Yahoo-sourced stock data into lagged stock risk data set since 2006.

Second,

-draw a random sample of size n=300 without replacement from your stock return data set. Recall that your stock return data contains a HiLo return column and standardized log lag1 and log lag2 return columns. I will call this your n=300 stock return data set. Show and explain how this is done.

- draw another random sample of size 300 without replacement from your stock return data set. Recall that your stock risk data contains a HiLo risk column and standardized log lag1 and log lag2 columns. I will call this your n=300 stock risk data set.

Next, using your n=300 stock return data set, walk through the steps covered by the ISLR text for your chosen method, SVM or CART, explaining in your own words what you are doing. Put your name into the title of any graphs you show.

Finally, run the program on your n=300 stock risk data set and compare the performance to that of your n=300 stock return data set. Include use of the chi-square test. Discuss the differences, the reasons that these would happen, and the lessons learned about the nature of the stock market.

third,

Select one of the tuning parameters or decision criteria that lie beneath the surface of your chosen methodology, CART or CART. Engage with it by researching beyond the ISLR text. Then experiment with it. Experiment with your data and with other data sets. Try decreasing or increasing n. Look at other sources for help, documenting the sources.

Forth,

Create classification space plots for both of your n=300 data sets, using your chosen methodology, SVM or CART. Be sure to explain how you went about this. Create the plot using the same techniques that we did in our plots for other methods.

] Prepare a comparative study of knn, naive Bayes, logistic regression, and your selected method. Make this comparison on your two n=300 data sets, splitting the data randomly in half to get the training and testing sets.

Explain what you are doing as you go along, explain what you understand about what distinguishes the methods, discuss reasons why the results vary, and why there might be systematic differences in performance between return data and risk data. Show classification space plots for knn, naïve Bayes, and logistic regression. At the end, show a single table in which you summarize the overall correct forecast rate for the stock returns for the four methods; then another table summarizing the performance on the stock risk data