What are some common challenges in data cleaning?
An example of an AI chatbot conversation about data cleaning challenges.
What are some common challenges in data cleaning?
Some common challenges include handling missing values, dealing with duplicates, and ensuring data consistency.
How can I handle missing values effectively?
You can handle missing values by using techniques like imputation, deletion, or using algorithms that support missing data.
What tools can help with data cleaning?
Popular tools include OpenRefine, Trifacta, and Python libraries like Pandas.
Are there best practices for data cleaning?
Yes, best practices include documenting your process, validating data, and automating repetitive tasks.
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