Regression analysis on categorical variable in Python

Python is an exceptional tool in determining the statistical relationship between independent categorical variable in multiple regression analysis

[@chisomanagbakwu2507]

Thank you for this. How can one handle multiple categorical variables? For e.g. If you have more than one column of categorical variable with some of them having more than 2 possible results e.g size: large, medium and small, how do I handle this? I used the get.dummies function to convert to binary but I'm now stuck

[@obiriyeboah7515]

df= pd.get_dummies(df, columns=['var1','var2','var3',....'varn'],
prefix='', prefix_sep='')

df.head()

[@pushpendrakushwaha604]

did you solved it ?

[@papaorgen4224]

well and simply done!

[@heartbrokendra]

SO helpful

[@sofiiatatosh4648]

P-value for female variable indicates that it is not significant for a model (since it is 0.39, but the significance level is usually 0.05)

[@_peeyush]

Thank you so much!

[@anvekshakatiyar1507]

Last code is not running

[@ShubhamSingh-tu9nk]

Hey, I have more than two variables such as material_con, material_timber, material_others, Design_slab,Design_flat,Design_rec but there is an error after dropping the data can you help here do you have any video on more than 2 variable??

[@ShubhamSingh-tu9nk]

even the ols giving error saying variable required

[@ochoonce9882]

thanks man

[@nitinkaura4417]

Isn't male and female binary data and wouldn't be a logistic regression model be better?

[@marcelkruger5187]

Don't think so since the output of a logistic model would be a categorical variable. So since you are predicting price this can't use logistic regression.