Description: This project presents a custom implementation of Principal Component Analysis (PCA) in Python from scratch. PCA is a dimensionality reduction technique widely used in data science and machine learning. While maintaining simplicity, our implementation closely matches the accuracy of the PCA module in scikit-learn, showcasing a comprehensive understanding of the underlying algorithms.
Key Features:
PCA algorithm implemented from scratch in Python. Utilizes a well-known crime dataset for testing and validation. Achieves accuracy comparable to scikit-learn's PCA module with a difference of only 10^-2. Clearly organized code structure and documentation for easy understanding and usage.