You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?
A.
Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
B.
Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
C.
Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
D.
Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKEGive the report to the logistics team each month so they can fine-tune inventory levels.
This type of model is well-suited to predicting inventory levels because it can take into account trends and patterns in the data over time, such as seasonal fluctuations in demand or changes in customer behavior.
https://cloud.google.com/learn/what-is-time-series
"For example, a large retail store may have millions of items to forecast so that inventory is available when demand is high, and not overstocked when demand is low."
C (by experience)
Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
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