A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.
The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.
Which action will MOST likely improve the performance for the forecasting model?

A. Aggregate sales from stores in the same geographic area.

B. Apply smoothing to correct for seasonal variation.

C. Change the forecast frequency from daily to weekly.

D. Replace missing values in the dataset by using linear interpolation.








 

Suggested Answer: A

Community Answer: D




This question is in MLS-C01 AWS Certified Machine Learning – Specialty Exam
For getting AWS Certified Machine Learning – Specialty Certificate


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