This project provides a simple and lightweight way to convert categorical data into one-hot encoded format. It is designed to be easy to use and suitable for machine-learning preprocessing workflows.
- Converts categorical values into one-hot vectors
- Stores category mappings for consistent encoding and decoding
- Handles unseen categories smoothly
- Fit once and transform multiple datasets
One-hot encoding is a common step in preparing data for machine-learning models. This encoder helps transform non-numerical categories into numeric vectors so that models can process them effectively. It can be integrated into any data pipeline and works with various types of datasets.