scaling data for a dataset with numerical ( both continuous and discrete) and categorical variables [closed]

Question

I am practicing regression and classification techniques on different datasets. Now I came to this dataset and I am going to practice regression algorithms.
I want to try algorithms on raw data, normalized data and standardized data ( just as a practice )

Now I know for categorical variables it is enough to define them as categories and no scaling is needed.
I learnt, dependent variable will not be scaled.
I learnt if continuous numeric variables have different units or large difference in values, I can try scaling on them.
but what about discrete numerical variables? should I scale them? I read in some content that if there is a small range of values I d better define that discrete variable as a categorical variable and do not scale them. is this a commen approach in machine learning?
I would appreciate any help understanding how to treat discrete variables.

Answer 1

Suppose, you have a feature that is the number of students in a university and you want to use that feature to predict the cost per year to study at that university (a simple linear regression task). In this case, it makes sense to take the feature as a continuous variable, and not a categorical one.

Again, suppose, you have a feature, which is the pH of an acid, and you want to use that feature to predict the color it will produce when it is added to universal indicator solution (a solution that shows different color for different pH values), (Now it's a classification problem: classify between colors). In this case, it does not make any sense to take the feature as a continuous variable, but a categorical one as different pH values have different colors, and no gradual change.

So, when should you define a discrete feature as a categorical variable and when to define it as a continuous variable? It actually depends on what the feature represents.