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Answer: Recurrent Neural Networks (RNN)
The correct answer is C. Recurrent Neural Networks (RNN). RNNs are particularly well-suited for time series data, as they can remember important information from past inputs to help predict future data. In this context, since the model needs to learn from daily updates to inventory data, an RNN is ideal because it can handle sequences and temporal dependencies effectively, making it well-suited for forecasting based on historical demand and seasonal popularity.
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As an ML engineer at a large grocery retailer operating in multiple regions, you are tasked with creating an inventory prediction model. This model should consider features such as region, location, historical demand, and seasonal popularity to accurately forecast inventory needs. Additionally, it should be capable of learning from new inventory data on a daily basis to improve its predictions over time. Which algorithm would be most suitable for building this model?
A
Classification
B
Reinforcement Learning
C
Recurrent Neural Networks (RNN)
D
Convolutional Neural Networks (CNN)
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