In this project, I explored the concept of mean normalization, a feature scaling technique used in machine learning. Some algorithms require data to be normalized to work correctly. Mean normalization scales the data such that values are distributed evenly around zero. This project also covers data separation techniques to prepare datasets for training and testing machine learning models.
- Explanation of mean normalization and its importance in machine learning.
- Step-by-step implementation of mean normalization.
- Data separation techniques to prepare datasets for model training and testing.
- Practical examples and visualizations to demonstrate the effects of normalization and data separation.