Abstract: A system may perform adaptive target presence probability to predict a location of a user (e.g., target) at a given time based on accumulated observations. For example, the system may track a location of the user over time and generate observation data associated with a user profile. The observation data may include a plurality of observations, with a single observation corresponding to a location and time at which the user was detected. The system may apply a clustering algorithm to the observation data to generate probability distributions (e.g., clusters) associated with discrete locations. For example, the system may group observations that are in proximity to each other and separate the groups based on location. Using the probability distribution for an individual cluster, the system may determine a likelihood that the user is present at a location corresponding to the cluster.