In this project, I developed a machine learning and deep learning model to predict patient attendance for hospital appointments. The model was trained on a large dataset of patient demographic and appointment information and used various algorithms such as decision trees, logistic regression, and neural networks to predict whether a patient is likely to attend their scheduled appointment or not. I implemented the model using a full-stack approach, including data preprocessing, feature engineering, model training and testing, and deploying the model as a web application. To improve the accuracy of the model, I explored various feature engineering techniques such as one-hot encoding and normalization, and fine-tuned the hyperparameters of the algorithms using cross-validation. I evaluated the performance of the model using various metrics such as accuracy, precision, recall, and F1-score. I also provided visualizations and explanations of the model's predictions to improve transparency and user understanding. Finally, I deployed the model as a web application that allows users to input patient demographic and appointment information and receive a prediction of whether the patient is likely to attend their appointment or not. This application can help hospitals and clinics optimize their scheduling and resource allocation and improve patient care. Overall, this project demonstrates my expertise in machine learning, deep learning, and full-stack development and shows how these skills can be applied to real-world problems in healthcare.