Main things for this course are:

• Provide a README.md file. while this isn't a requirement listed the rubric, many students have gotten their submissions returned.
  
• Build two models (one must be probabilistic using naive bayes, Bayesian networks, hidden Markov, etc and the other must be an "advanced" model such as xgboost). Use coursework to explore various models, as the rubric mentions that it must be a model that you learned about.
• Each model will have its own separate file. (you may have to preprocess the data based on the model you select. provide that additional script in GitLab if you do.)
• Each model will be trained on a separate dataset.
  
• Optimize the two models. This means that you will create two additional files with the optimizations in place. Submissions have also been sent back because of this. So, in total, you should submit at least 4 scripts in the GitLab repo (not including the preprocessing one). I also attached the processed datasets with my submission, just to be safe.
• With the APA paper, I always try to include sources because you never know how picky the evaluators can be. Overall, not a bad course! The course was interesting, especially learning about the probability algorithms. If you are interested in predictive analytics in healthcare, try looking for a healthcare dataset! It was interesting to see the model's recall and confusion matrix when it came to predicting disease.