KPrototypes fit_predict error: "could not convert string to float" about kmodes HOT 4 CLOSED

Could you post the full traceback, so I can see where the error occurs exactly?

Also, what does your data look like?

from kmodes.

My data contains both numerical (0.xxx)and categorical of one and two alphabet (A,B,XZ). Here is the snippet:

km = kprototypes.KPrototypes(n_clusters=10, init='Cao', verbose=2)
train1=pd.read_csv('/home/lemma/train.csv')
train1=train1.drop(['id','loss'],1)
train1=train1.values.astype(object)
train1['clusters_KModes'] = km.fit_predict(train1,categorical=[1,2,3])

from kmodes.

With categorical=[1,2,3], you're telling the algorithm that the second, third and fourth columns are categorical. But from your screenshot it looks like there's many more categorical variables. That why kmodes is trying to interpret the 'MJ' string as a float.

from kmodes.

Hi categorical=[1,2,3] is just for snipped only, The actual categorical written in python is ranging from 1-116. Thus I need to do treatment for categorical data because two alphabetic categorical cannot be converted into float?

from kmodes.