Logistic Regression (LR) Classifier Implementation

This implementation presents a logistic regression classifier designed for binary classification tasks. The code includes detailed usage documentation to facilitate easy understanding and operation. For those interested in logistic regression algorithms or seeking a reference classification implementation, this program offers practical functionality and learning resources. Key features include: - Sigmoid activation function implementation for probability mapping - Gradient descent optimization for parameter learning - Regularization options to prevent overfitting - Model evaluation metrics including accuracy and confusion matrix - Sample dataset preprocessing and feature scaling examples The implementation follows standard logistic regression methodology where the hypothesis function uses the sigmoid function to output probabilities between 0 and 1. The cost function is minimized using optimization techniques to find optimal parameters for classification decision boundaries.