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
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??
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.
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
df= pd.get_dummies(df, columns=['var1','var2','var3',....'varn'],
prefix='', prefix_sep='')
df.head()
did you solved it ?
well and simply done!
SO helpful
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)
Thank you so much!
Last code is not running
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??
even the ols giving error saying variable required
thanks man
Isn't male and female binary data and wouldn't be a logistic regression model be better?
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.