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Encoding techniques are essential in data preprocessing, especially when dealing with categorical data in machine learning. These methods convert non-numerical data into numerical formats, enabling machine learning models to process them effectively. Below are some widely used encoding techniques in Python, along with their use cases and implementation.
Label Encoding
Label Encoding assigns a unique integer to each category. It is suitable for ordinal data, where the order of categories is meaningful.
from sklearn.preprocessing import LabelEncoderdata = ['red', 'blue', 'green', 'blue', 'red']label_encoder = LabelEncoder()encoded_data = label_encoder.fit_transform(data)print(encoded_data)# Output: [2 0 1 0 2]Copied!✕CopyThis method is simple but may introduce ordinal relationships for nominal data, which can mislead models.
One-Hot Encoding
Feature Encoding Techniques - Machine Learning - GeeksforGeeks
Jul 12, 2025 · Ordinal Encoding: We can use Ordinal Encoding provided in Scikit learn class to encode Ordinal features. It ensures that ordinal nature of the variables is sustained.
See results only from geeksforgeeks.orgLabel Encoding in Python
Since most ML algorithms work only with numeric data, categorical features must be encoded before model training. In Label Encoding, each unique categor…
Feature Encoding Techniques in Machine Learning with Python ...
- Label / Ordinal Encoder. Label Encoder and Ordinal Encoder encode categories …
- One Hot / Dummy Encoding. In One-Hot Encoding and Dummy Encoding, the …
- Target Encoding. Target Encoding uses Bayesian posterior probability to …
- Count / Frequency Encoding. Count and Frequency Encoding encodes …
- Binary / BaseN Encoding. Binary Encoding encodes categorical variables into …
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