pritomsh / encoding-techniques

Encoding: converting categorical data into a numerical data

Encoding

In machine learning, encoding refers to the process of converting categorical data into a numerical representation that can be easily processed by machine learning models.

Enable processing of categorical data: Machine learning models typically work with numerical data, so categorical data must be encoded into a numerical representation before it can be processed by the model.

Improve model performance: Encoding categorical data can improve the performance of machine learning models. For example, one-hot encoding can help prevent the model from assuming a false order or relationship between categories, while ordinal encoding can help preserve the relationship between categories. Choosing the appropriate encoding method can make a significant difference in the accuracy of a machine learning model.

Overall, encoding categorical data is an important step in machine learning that helps enable processing of categorical data, preserves information, avoids bias, and improves model performance.

Types of Encoding

• Without Use Any Encoding Techniques
• Label Encoding
• One-Hot Encoding
• Ordinal Encoding

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