cst383 - week 5

    This week covered machine learning topics like handling missing data, data scaling, z-score calculations, knn classification, test sets, cross validation, and evaluating models. These topics, while diverse, worked well together for preparing data and testing to make sure conclusions drawn from it are accurate.

    I found learning about missing data and how it is represented interesting. I had originally not given much thought to the distinction between values like None and NaN so it was fun learning how these values behave and why they are treated differently. Data is often filled with holes and knowing how to handle missing information is an important part of the analysis process.

    Cross validation was another topic that stood out to me. The idea of testing a model against different subsets of data to verify that the results are reliable seems intuitive and clever. It made me think about the people who originally developed these methods and how much thought must have gone into solving these problems. It reminds me of the saying, "We stand on the shoulders of giants". The methods we learn today are the result of decades of innovation by brilliant minds who developed ways to make predictions more accurate. It is fun to think about how much we benefit from the ideas of everyone who came before us and how these methods have become fundamental tools.

    I feel like I learned a lot about what all goes into creating reliable machine learning models. Learning about many of the steps involved like preparing data and evaluating the model highlights how much care is required to produce results that can be trusted. It shows how important accuracy is in fields like medicine and fraud detection where even minor errors have consequences.

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