You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location…

The correct answer is C. Recurrent Neural Networks (RNN). Recurrent Neural Networks (RNNs) are specifically designed to process sequential and time-series data by maintaining an internal hidden state that captures temporal dependencies across time steps. Inventory prediction relies heavily on temporal patterns - historical demand…

Submitted by klara.se· Apr 18, 2026ML model development

Question

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

Options

AClassification
BReinforcement Learning
CRecurrent Neural Networks (RNN)
DConvolutional Neural Networks (CNN)

How the community answered

(54 responses)

A
6% (3)
B
4% (2)
C
78% (42)
D
13% (7)

Explanation

Recurrent Neural Networks (RNNs) are specifically designed to process sequential and time-series data by maintaining an internal hidden state that captures temporal dependencies across time steps. Inventory prediction relies heavily on temporal patterns - historical demand trends, day-of-week patterns, and seasonal cycles - making RNNs (and their variants like LSTMs and GRUs) the ideal architecture. The requirement to learn from new inventory data on a daily basis also aligns with RNNs' ability to update on streaming sequential data. CNNs (Option D) are optimized for spatial data like images. Reinforcement Learning (Option B) is reward-driven and used for sequential decision-making, not forecasting. Classification (Option A) predicts discrete categories, not continuous inventory quantities.

Topics

#Inventory Prediction#Time-series Forecasting#Recurrent Neural Networks#Sequential Data

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