You must run a housing-price prediction model on a single, resource-constrained VM. Which learning algorithm is most appropriate?

Linear regression is the most appropriate choice because it is a simple, computationally inexpensive algorithm. On a single, resource-constrained VM, minimizing computational load is crucial. Linear regression models require fewer resources (CPU, RAM) for training and inference compared to more complex models. Logistic classification is for classification tasks, not regression, and while simple, it doesn't fit the problem type. Recurrent neural networks (RNNs) and feedforward neural networks (FNNs) are generally more resource-intensive, especially for training, and are typically overkill for a straightforward prediction task like housing prices unless there are complex non-linear relationships or sequential data involved that simpler models cannot capture.