There are some topics to notice: 1. This is not a business report, so it does not need to include an executive summary, a cover page, table of contents, or even an introduction describing the context of the task. However, it MUST has a CONCLUSION end of the work. 2. If you need any my course’ materials, just let me know and I will send you such as ppt, etc. 3. Please read carefully the ASSESSMENT CRITERIA: 3.1. For both logistic regression, and decision trees discuss different settings you used and why you considered these important. (Consider the choice of variable selection method as part of this question also.) 3.2. For each classification method develop one or a few candidate models that you think are promising before providing a final recommendation of the most appropriate model. You must include the results for what you consider as the important steps in the process that led to your final recommendations. In particular, you must provide a clear and logical explanation of the steps you followed and justify the different decisions you made. . Justify the recommended model(s), using appropriate performance measures. Explain what we learn from this model that is relevant to the problem (dataset) you were asked to analyse. Comment on your findings and the generalisation performance of the model(s) you recommend for each type of classifier. 4. Every idea and choices MUST be justify, do not just put the answer. 5. In this course, I used R STUDIO, but you can choose another software to resolve this coursework. However, the R studio is more preferable.