Why Encode Categorical Data?
Before diving into the encoding techniques, it’s important to understand why encoding is necessary:
- Machine Learning Algorithms: Most machine learning algorithms, such as linear regression, support vector machines, and neural networks, require numerical input. Categorical data needs to be converted into a numerical format to be used effectively.
- Model Performance: Proper encoding can significantly impact the performance of a machine learning model. Incorrect or suboptimal encoding can lead to poor model performance and inaccurate predictions.
- Data Preprocessing: Encoding is a crucial step in the data preprocessing pipeline, ensuring that the data is in a suitable format for training and evaluation.
Encoding Categorical Data in Sklearn
Categorical data is a common occurrence in many datasets, especially in fields like marketing, finance, and social sciences. Unlike numerical data, categorical data represents discrete values or categories, such as gender, country, or product type. Machine learning algorithms, however, require numerical input, making it essential to convert categorical data into a numerical format. This process is known as encoding. In this article, we will explore various methods to encode categorical data using Scikit-learn (Sklearn), a popular machine learning library in Python.
Table of Content
- Why Encode Categorical Data?
- Types of Categorical Data
- Encoding Techniques in Sklearn
- 1. Label Encoding
- 2. One-Hot Encoding
- 3. Ordinal Encoding
- 4. Binary Encoding
- 5. Frequency Encoding
- Advantages and Disadvantages of each Encoding Technique
- Choosing the Right Encoding Method