Why is data preparation important in data science?
An example of an AI chatbot conversation about the significance of data preparation in data science.
Why is data preparation important in data science?
Data preparation is crucial because it ensures the quality and accuracy of your data, which directly impacts your analysis.
What are the main steps involved in data preparation?
The main steps include data cleaning, data transformation, data integration, and data reduction.
Can you give an example of poor data preparation?
Sure! An example would be using incomplete data without addressing missing values, leading to biased results.
How does data preparation affect machine learning models?
Proper data preparation can enhance model performance, while poor preparation can lead to inaccurate predictions.
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