Chapter:1-Label Encoder vs One Hot Encoder in Machine Learning

Label Encoding:

Data format

data format
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
#import datasetdataset=pd.read_csv('Data.csv')
x= dataset.iloc[:,:-1].values
# Taking care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer =[:, 1:3])
x[:, 1:3] = imputer.transform(x[:, 1:3])
#encoding categorical data
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
x[:, 0] = labelencoder.fit_transform(x[:, 0])
Taking care of missing data
Label encoded country values.

One Hot Encoder

If you’re interested in checking out the documentation, you can find it here. Now, as we already discussed, depending on the data we have, we might run into situations where, after label encoding, we might confuse our model into thinking that a column has data with some kind of order or hierarchy when we clearly don’t have it. To avoid this, we ‘OneHotEncode’ that column.

from sklearn.preprocessing import OneHotEncoder
onehotencoder = OneHotEncoder(categorical_features = [0])
x = onehotencoder.fit_transform(x).toarray()
OneHot encoded country values.



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