How to convert this onehotencoder so it can run on scikitlearn 0.22.1

import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score

dataset = pd.read_csv(‘heart.csv’)
X = dataset.iloc[:,:-1].values
y = dataset.iloc[:,-1].values

from sklearn.preprocessing import OneHotEncoder #cp oneHotEncoder = OneHotEncoder(categorical_features=[2], n_values='auto') oneHotEncoder.fit(X) X = oneHotEncoder.transform(X).toarray() X = X[:, 1:] #restecg oneHotEncoder = OneHotEncoder(categorical_features=[8], n_values='auto') oneHotEncoder.fit(X) X = oneHotEncoder.transform(X).toarray() X = X[:, 1:] #slope oneHotEncoder = OneHotEncoder(categorical_features=[13], n_values='auto') oneHotEncoder.fit(X) X = oneHotEncoder.transform(X).toarray() X = X[:, 1:] #ca oneHotEncoder = OneHotEncoder(categorical_features=[15], n_values='auto') oneHotEncoder.fit(X) X = oneHotEncoder.transform(X).toarray() X = X[:, 1:] #thal oneHotEncoder = OneHotEncoder(categorical_features=[19], n_values='auto') oneHotEncoder.fit(X) X = oneHotEncoder.transform(X).toarray() X = X[:, 1:] from sklearn.preprocessing import StandardScaler scalerX = StandardScaler() X = scalerX.fit_transform(X)

because the new version of sklearn onehotencoder doesnt have categorical_features attribute please help

Answer rating: 100% (QA)

In the sklearn 022 the categoricalfeatures were deprecated So ... View the full answer