This project presents an analysis and implementation of an Artificial Neural Network (ANN) model using the Haberman dataset, which contains data on breast cancer patients. The primary goal is to predict patient survival based on features such as age, year of operation, and the number of positive axillary nodes detected. The ANN model is built and trained using R, with steps including data normalization, splitting into training and testing sets, and evaluating the model's accuracy. Visualizations of the neural network structure are also provided to illustrate the model's architecture.