Georgia Institute Of Technology - ISYE 650114-week_4_hw_solutions

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WEEK 4 HOMEWORK – SAMPLE SOLUTIONS IMPORTANT NOTE These homework solutions show multiple approaches and some optional extensions for most of the questions in the assignment. You don’t need to s... ubmit all this in your assignments; they’re included here just to help you learn more – because remember, the main goal of the homework assignments, and of the entire course, is to help you learn as much as you can, and develop your analytics skills as much as possible! Question 1 Using the same crime data set as in Homework 3 Question 4, apply Principal Component Analysis and then create a regression model using the first 4 principal components. Specify your new model in terms of the original variables (not the principal components), and compare its quality to that of your solution to Homework 3 Question 4. You can use the R function prcomp for PCA. (Note that to first scale the data, you can include scale. = TRUE to scale as part of the PCA function.) Question 2 Using the same crime data set as in Homework 3 Question 4, find the best model you can using (a) a regression tree model, and (b) a random forest model. In R, you can use the tree package or the rpart package, and the randomForest package. For each model, describe one or two qualitative takeaways you get from analyzing the results (i.e., don’t just stop when you have a good model, but interpret it too). (a) Regression Tree Here’s one possible solution. Question 3 Describe a situation or problem from your job, everyday life, current events, etc., for which a logistic regression model would be appropriate. List some (up to 5) predictors that you might use. Here’s one potential suggestion. In business-to-business sales and marketing, buying-propensity models can be useful in ranking customers as high value or low value. A logistic regression model could be used to predict the probability of a customer buying or not, or (using a threshold) to classify into yes or no categories. Some of the predictors that could be used to develop such a model are: 1 Question 4 1. Using the GermanCredit data set at http://archive.ics.uci.edu/ml/machine-learningdatabases/statlog/german / (description at http://archive.ics.uci.edu/ml/datasets/Statlog+ %28German+Credit+Data%29 ), use logistic regression to find a good predictive model for whether credit applicants are good credit risks or not. Show your model (factors used and their coefficients), the software output, and the quality of fit. You can use the glm function in R. To get a logistic regression (logit) model on data where the response is either zero or one, use family=binomial(link=”logit”) in your glm function call. Variable | Value | Coefficient ROC curve Threshold | Accuracy | AUC Loss vs Threshold [Show More]

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