Binary Classification Implementation Using Backpropagation Neural Network

This program implements binary classification through a Backpropagation (BP) neural network, featuring exceptionally clear code structure that even novice programmers can readily comprehend. The implementation follows a standard machine learning workflow: first training the model with input data using forward propagation and error calculation, then applying the trained network to classify new data samples. The core algorithm employs gradient descent optimization with backpropagation to iteratively adjust synaptic weights and biases, minimizing classification errors through multiple epochs. Key functions include data normalization, activation function implementation (typically sigmoid/tanh for hidden layers), and convergence checking. Additional features incorporate training progress visualization through loss-curve plotting and confusion matrix generation for performance analysis, enabling users to better understand and optimize the classification algorithm. The modular code structure separates data preprocessing, network initialization, training loop, and prediction phases, making it adaptable for various applications including pattern recognition and predictive modeling.