BP Neural Network for Predicting Personal Income Information

This project utilizes a Backpropagation (BP) Neural Network to predict personal income information. The BP neural network, a type of artificial neural network, employs the backpropagation algorithm to learn patterns from training data (adult.data) and make predictions on test data (adult.test). The implementation typically involves configuring network parameters such as hidden layers, activation functions (e.g., sigmoid or ReLU), and learning rate through Python libraries like TensorFlow or PyTorch. By inputting individual characteristics including age, education level, occupation, and other demographic features, the model predicts income levels through forward propagation and adjusts weights via gradient descent during training. This approach provides valuable insights into economic conditions, serving as a foundation for decision support systems and data analysis applications. Key functions include data preprocessing, network architecture design, and performance evaluation using metrics like accuracy and F1-score.