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Georgia Institute Of Technology ISYE 6501 Homework 4 Complete Solutions - Introduction To Analytics Modeling - GTX ISYE 6501

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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 m ... odel 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) 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. Question 41. 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. [Show More]

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