What are the best practices for data preparation?
An example of an AI chatbot conversation about data preparation best practices.
What are the best practices for data preparation?
Data preparation involves cleaning, transforming, and organizing data. Key practices include handling missing values, normalizing data, and ensuring data consistency.
How do I handle missing values in my dataset?
You can handle missing values by removing them, imputing with mean/median/mode, or using algorithms that support missing values.
What tools can I use for data preparation?
Popular tools include Python libraries like Pandas, R, and data preparation platforms like Trifacta and Alteryx.
Can you explain data normalization?
Data normalization is the process of scaling data to a specific range, often 0 to 1, to ensure that no variable dominates others.
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