What are the common challenges in data preparation?
An example of an AI chatbot conversation about challenges in data preparation.
What are the common challenges in data preparation?
Some common challenges include data quality issues, missing values, and inconsistent formats.
How can I handle missing values in my dataset?
You can handle missing values by using techniques like imputation, deletion, or using algorithms that support missing data.
What tools can help with data preparation?
Popular tools include Python libraries like Pandas, R, and data preparation platforms like Alteryx and Talend.
Are there best practices for data cleaning?
Yes, best practices include standardizing formats, validating data, and documenting your cleaning process.
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