ML Concepts – Encoding Categorical Data

There are two types of encoding the Categorical Data: One Hot Encoding Label Encoding or Target Encoding Example: One Hot Encoding – 100, 101, 010 Label Encoding – Normal = 0, Malicious = 1 About The Author Dr. Pranay Jha Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in…

There are two types of encoding the Categorical Data:

  1. One Hot Encoding
  2. Label Encoding or Target Encoding

Example:

One Hot Encoding – 100, 101, 010

Label Encoding – Normal = 0, Malicious = 1

# Encoding categorical data
# Encoding the Independent Variable
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')
X = np.array(ct.fit_transform(X))
print(X)
# Encoding the Dependent Variable
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)
print(y)

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Architect’s Toolkit

About the Author

Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.

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