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using one hot encoder with logistic regression

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Ayoni02 answered on June 24, 2024 Popularity 1/10 Helpfulness 8/10

answer using one hot encoder with logistic regression

using one hot encoder with logistic regression

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lebelencoder

onehotencoder

from sklearn.preprocessing import LabelEncoder

x = df.select_dtypes(exclude=['number']) \
      .apply(LabelEncoder().fit_transform) \
      .join(df.select_dtypes(include=['number']))

x
""" output
        status  country  city      datetime  amount
601766       0        0     1  1.453916e+09     4.5
669244       0        1     0  1.454109e+09     6.9
"""
classifier.fit(x.drop('status',1), x['status'])
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

Popularity 1/10 Helpfulness 8/10 Language python

Source: Grepper

Tags: encoder logistic-regression python using

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Contributed on Jun 24 2024

Ayoni02

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