Topic 1 Question 156
2 つ選択A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3. Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them?
Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data.
The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize.
Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model.
The sales data is missing zero entries for item sales. Request that item sales data from the source database include zero entries to enable building the model.
Only 100 MB of sales data is available in Amazon S3. Request 10 years of sales data, which would provide 200 MB of training data for the model.
解説
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コメント(4)
- 正解だと思う選択肢: AC
AC would be my answer. As half the stores have only been open for 6 months, no seasonality would be captured. The aggregation of the daily also removes trends we see during the week which is also not great when we are looking for the daily predicated sales figure
👍 25cron00012022/04/24 - 正解だと思う選択肢: CD
I go with CD. How could we ignore the days with 0 sales? The model should be trained so that it can predict 0 sales days as well.
👍 3peterfish2022/07/18 - 正解だと思う選択肢: AC
A. Since more than half of the stores have been in business for less than 6 months, it will be challenging to detect seasonality patterns for these new stores. Therefore, one solution is to request categorical data to relate new stores with similar stores that have more historical data. This will help the model to identify common patterns and accurately forecast sales for new stores.
C. Since the sales data is aggregated by week, it may not be possible to identify daily patterns or trends. Hence, one solution is to request daily sales data from the source database to enable building a daily model. This will help the model to identify daily patterns and improve its forecasting accuracy.
👍 2AjoseO2023/02/17
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