What are some common data quality issues in AI?
An example of an AI chatbot conversation about common data quality issues in AI.
What are some common data quality issues in AI?
Common data quality issues in AI include missing values, duplicates, inconsistencies, and outliers.
How do missing values affect AI models?
Missing values can lead to biased results, reduced accuracy, and can complicate the training process.
What strategies can I use to handle duplicates?
You can use techniques like data deduplication, clustering, and fuzzy matching to identify and remove duplicates.
Can you explain how outliers impact data analysis?
Outliers can skew results, affect statistical analyses, and lead to misleading conclusions if not handled properly.
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