OneHotEncoder Error: cannot convert string to float

Question

I was wondering if someone could help me with this. I'm learning about Multiple Linear Regression and was trying to do some practice but seem to have hit a problem. I was trying to convert payment_type into a categorical variable using onehotencoder. Here I have the error and the first few rows and columns of the data. I tried looking online and doing things that other people suggested but I kept getting errors from those as well. Is there a way to fix this? I've been trying for hours now. Also I'm not sure why it says cannot convert VTS when its the payment_type that I'm trying to convert. Any thoughts and help on the matter is greatly appreciated.

The code prior to what you see on the screen is basically just loading the data, thats about it.

Thanks!

Itachi · Accepted Answer · 2018-05-18 05:27:10Z

3

Documentation of sklearn.preprocessing.OneHotEncoder says:

Encode categorical integer features using a one-hot aka one-of-K scheme.

The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features.

You need to use sklearn.preprocessing.LabelEncoder, to convert your string variables to categorical integer values.

answered May 18, 2018 at 5:27

Itachi

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3 Comments

Itachi Over a year ago

you also need to apply labelencoder to other categorical variables. ie vendor_id, and medalion

ra1993 Over a year ago

ohhh okay got it thanks! so I'm supposed to use onehotencoder on all categorical variables. I see.

Itachi Over a year ago

yes, you need to. Also, please accept the answer, if it solves it.

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