Categorical Data and Encoding Techniques

Categorical Data & Encoding

Machine learning models require numeric input, so we must convert categorical variables.

One‑Hot Encoding with get_dummies

df_onehot = pd.get_dummies(df, columns=['city', 'gender'], drop_first=True)
print(df_onehot.head())

Label Encoding with scikit‑learn

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['dept_code'] = le.fit_transform(df['department'])

Frequency Encoding

freq = df['category'].value_counts() / len(df)
df['category_freq'] = df['category'].map(freq)

Target Encoding (mean encoding)

target_means = df.groupby('city')['target'].mean()
df['city_target_enc'] = df['city'].map(target_means)

When to use which?

• Low cardinality (<10) → One‑Hot.
• Medium cardinality (10‑100) → Frequency / Target.
• High cardinality (>100) → Hashing or embeddings.