Introduction
In this project, you will learn how to perform one-hot encoding on label data for a single-label classification task. One-hot encoding is a common technique used to transform categorical variables into a format that can be used by machine learning algorithms.
🎯 Tasks
In this project, you will learn:
- How to understand the concept of one-hot encoding and its importance in machine learning.
- How to implement a function to perform one-hot encoding on a list of sample labels.
- How to test the label encoding function with sample data.
🏆 Achievements
After completing this project, you will be able to:
- Transform categorical labels into a numerical format suitable for machine learning models.
- Understand the importance of data preprocessing and feature engineering in the machine learning pipeline.
- Demonstrate practical coding skills in Python to manipulate and transform data for machine learning tasks.
