The goal of this assignment is to practice classification methods and to apply them to a dataset using SPSS data analysis tools. The study aims at determining whether one can predict the severity of heart disease based on a number of factors including age, blood pressure, and many others. Assignment preparation This assignment will be using SPSS data analysis tool. The dataset will be used in this analysis (see Assignment 2 for instructions on how to create this file). It is recommended to replace missing values (character ?) with an empty character. This can be accomplished through a text editor before importing the file into SPSS, or within SPSS. The expected results for this assignment will be based on this assumption. Classification in SPSS The dataset heart-ch.txt is ready to be processed for classification. The target attribute is the last one by default: num. Evaluation of a classifier generally uses a training set first to train the model, then a test set on which predictions by the model are compared with known classes. a. Switch to the Analyze tab. This dataset is going to be analyzed with Binary Logistic Regression. The options by default can be used, however it is recommended to use 95 for CI for exp(B). By selecting num as the dependent variable, and all the other variables as covariates, which result do you obtain (Block 1 Classification Table)? Method Significance R Square Accuracy Logistic regression b. Split the data into 75% for the training set and 25% for the test set, and compare results between Logistic Regression and Nearest-neighbor (both from the Analyze tab). Method Accuracy Logistic regression Nearest-neighbor c. What do you conclude about the predictability of heart disease (from the factors in the dataset)? Explain. d. Which factors are most predictive of heart disease (hint: these are the variables from the Variables in the Equation table having Sig <= 0.05; the higher B, the larger the effect)?