CST 383 - Week 5

 Something I would like to analyze and talk about is the section on Missing Data. Before reading about it, I did not really consider how important it is to properly deal with missing data. I used to think that you could just remove rows or ignore the problem, but now I understand that handling missing values is a big part of data science. The way pandas provides built-in functions to detect, remove, and replace missing data is very helpful. In the examples from the book and slides, the datasets were small, so it looked simple to manage. However, I imagine that when working with much larger datasets, these tools become even more important and powerful.

I am still thinking about when it is better to delete rows or columns versus when it is better to replace the missing values. It seems like deleting data could remove useful information, especially if many rows contain missing values. On the other hand, replacing values with the mean, median, or mode could introduce bias if it is not done carefully. I think the decision depends on how much data is missing and whether the missing values are random or not. This is something I would like to understand more deeply, especially how it affects model performance.

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