Think of the difference between supervised and unsupervised as having and not having a supervising entity letting you know whether you are making the right decisions or not, respectively. Supervised leaming may benefit from label classifications of data, such as flowers (e.g., roses, tulips, carnations, and so on). On the other hand, unsupervised learning may not have a classification to benefit from because the answer to the question may be the aim. Read more on these topics here: Supervised and Unsupervised Learning. Using the provided dataset that represents the Titanic disaster, create both an unsupervised clustering algorithm to describe the data and a simple supervised classification prediction to determine who might survive. Implement your algorithms in Python. Submit 2 Python files with roughly 50-80 lines of code each and 1 MS Word document (or Jupyter Notebook). The code file must include a file header that includes the following information at a minimum: Your name, date, course, and description of the code. ⚫ Code must be well commented and in your own words. Explain your decisions and what the code is doing, and provide a rationale as to why you selected the given algorithm. Code should adhere to best practice code standards. • Capture and record results and screenshots in a Word document. The following resources can be used to help you complete this assignment: ⚫ ICU Staffing Feature Phenotypes and Their Relationship With Patients' Outcomes: An Unsupervised Machine Learning Analysis • Identifying Topological Order Through Unsupervised Machine Learning Machine Learning - Clustering Introduction Machine Learning - Supervised Learning Decision Trees

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