Abstract: A health outcome recommendation engine trains a machine-learned model using a training set of information that includes health characteristics, actions, environments, and health outcomes associated with training users. The health outcome recommendation engine applies the trained machine-learned model to health characteristics, actions, an environment, and health goals of a user. The machine-learned model identifies actions that, if taken by the user, increase a likelihood of the user achieving the health goals. The health outcome recommendation engine presents the actions to the user via an interface of a client device.

Abstract: A skin condition recommendation engine identifies skin conditions of a user's face and recommends actions and/or products that increase a likelihood that the skin conditions will be remedied. The skin condition recommendation engine trains a machine learned model using a training set of information that includes images and identified skin conditions of training users' faces. The skin condition recommendation engine inputs images of the user's face into the machine learned model, which outputs identified skin conditions of the user. The skin condition recommendation engine accordingly identifies actions that, if performed by the user, would increase a likelihood of the skin conditions being remedied. The skin condition recommendation engine modifies an interface of a device of the user to show the identified actions.