• Make a hypothesis about a correlation in a dataset and test the hypothesis using a statistical inference technique (such as the t-test).
• Build a predictive model using one of the techniques covered in the course, i.e., ordinary least squares regression or Nave Bayes

Your report should follow this outline:

1. Objectives: What are you setting out to prove or predict? What is your rationale for there being a correlation in the data that you're looking to confirm and/or exploit?
2. Data Preparation: What was your data source (e.g., web scraping, corporate data, a standard machine learning dataset, open data, etc.)? How good was the data quality? What did you need to do to procure it? What tools or code did you need to use to prepare it for analysis? What challenges did you face?
3. Analysis or model: If you are conducting an inference test, explain the analysis you performed clearly and include well-labelled diagrams to make your points. If you chose to do a predictive model, explain the model, how you trained and tested it, and how well it works. How did you confirm that the data met the requirements for the test or modelling technique to be valid?
4. Conclusions: Did you prove/disprove your hypothesis or create a useful model? What did you learn about your data set?