I have a data set in the form:
df occupation class 1 lawyer upper 2 doctor upper 3 unemployed middle 4 plumber lower 5 unemployed upper
The first variable
occupation has only 8 values that it can take, and
class can only take
3. I am trying to predict the
class variable based on the
I have an idea about predicting continuous independent variables with continuous dependent variables (for example, linear regression). And logistic regressions with categorical dependent variables. But what about both sides of the equation being categorical? Does it even involve regressions or are there a simpler set of method to regress
class ~ occupation?
And the opposite situation
categorical_data ~ binary_dummy_variable. If
X is a binary variable. How can I predict a categorical variable on that dummy variable?
I'm thinking that I would need to turn
occupation into a dummy variable and explore the relationship that way. Especially since there is no specific scaling order to the variable. Perhaps turning occupation into a new binary variable "professional" "non-professional". But I still wouldn't know how to compare the new binary output to the