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Answer: Perform data cleaning, handle missing values, remove outliers, and ensure data consistency.
To ensure high-quality data for training an AutoML model in an e-commerce context, one should perform data cleaning to remove inconsistencies and errors, handle missing values by either imputing or removing them, remove outliers that could skew the model, and ensure data consistency across different sources. These steps help address common challenges such as data inconsistency, missing values, and outliers, which can adversely affect model performance.
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In a scenario where you are using AutoML to develop a predictive model for a large-scale e-commerce platform, describe the steps you would take to ensure the data used for training is of high quality. Discuss the potential challenges and how you would address them.
A
Perform data cleaning, handle missing values, remove outliers, and ensure data consistency.
B
Collect more data, merge data from different sources, normalize data, and perform feature selection.
C
Validate data schema, check for data duplication, standardize data formats, and perform data augmentation.
D
Audit data sources, verify data integrity, apply data transformations, and perform data balancing.
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