S412/S512/T650 Statistical Learning Project II

• Important: This is not a group project. Please work independently.
• Data proj2data.csv A retrospective sample of 462 males in a heart-disease high-risk region. The data set contains the following variables
– Response: HD: coronary heart disease (Yes) or not (No)
– Predictor variables
∗ adiposity: the accumulation of excessive body fat;
∗ age: age in years;
∗ alcohol: current alcohol consumption;
∗ famhist: family history of heart disease (Yes, No);
∗ ldl: low density lipoprotein cholesterol;
∗ obesity: UN:Underweight, HE: Healthy weight, OV: over weight, OB: obesity
∗ sbp: systolic blood pressure;
∗ tobacco: cumulative tobacco (kg);
∗ typea: score of type-A personality;
• Training data: the first 230 cases.
• Testing data: the last 232 cases.
• Apply all of the following models and find a model that has the lowest test data error rate and can be used to classify whether a patient has the coronary heart disease or not. The best final model should be chosen by comparing the test data error rates.
– 1. LDA
– 2. QDA
– 3. Logistic regression
– 4. Naive Bayes
– 5. KNN classifier
– 6. Tree-based Methods
– 7. Support vector machine

Project Report Format:

• Please typeset your report using R Markdown in RStudio to produce a PDF file. Your report should have the following
– A title
– Abstract
– Introduction
– Section(s) containing details of your data analysis, include only relevant R codes used for data analysis, graphs and explanation of the methods you used, etc. If random data were generated you must specified a random seed so that your results can be repeated.

– Summary of Results and Discussion which include a table to summarize the test error rate of the models trained and give the predicting formula of your final model if possible.
• Limit your report within 15 pages. Only report necessary and relevant R code, graphs, and printout. Make your report neat, clean, readable, not like draft. Do not include the data in your report. Remove all unwanted “warnings” and “messages” using message=FALSE, warning=FALSE in R chunk or fix the issues that relate to the messages.
• Important: Using AI tools or anything like this is prohibited. Use only the R libraries and functions taught or mentioned in the lectures or the textbook or no credit!