Purpose of converting continuous data to categorical data I was reading through a notebook tutorial working with the Titanic dataset, linked here, and noticed that they highly favored ordinal data to continuous data.

For example, they converted both the Age and Fare features into ordinal data bins.

I understand that categorizing data like this is helpful when doing data analytics manually, as fewer categories makes data easier to understand from a human perspective. But intuitively, I would think that doing this would cause our data to lose precision, thus leading to our model losing precision or accuracy.

Can someone explain when is converting numerical data to ordinal data is appropriate, and the underlying statistics of why it is effective?
Your intuition is generally correct - in many cases, premature discretization of continuous variables is undesirable. Doing so throws away potentially meaningful data, and the result can be highly dependent on exactly how you bucket the continuous variables, which is usually done rather arbitrarily. Bucketing people by age decade, for example, implies that there is more similarity between a 50-year-old and a 59-year-old than there is between a 59-year-old and a 60-year-old. There can be some advantages in statistical power to doing this, but if your binning doesn't reflect natural cutpoints in the data, you may just be throwing away valuable information.

You can find a very similar question here:

https://stats.stackexchange.com/questions/68834/what-is-the-benefit-of-breaking-up-a-continuous-predictor-variable?noredirect=1&amp;lq=1