How do I handle missing data in exploratory data analysis?
An example of an AI chatbot conversation about handling missing data in EDA.
How do I handle missing data in exploratory data analysis?
Handling missing data is crucial in EDA. You can either remove missing values, impute them, or use algorithms that support missing data.
What are some common imputation techniques?
Common imputation techniques include mean, median, mode imputation, and more advanced methods like K-Nearest Neighbors or regression imputation.
When should I choose to remove missing data instead of imputing?
You should consider removing missing data if the percentage of missing values is small and won't significantly impact your analysis.
Are there any tools that can help with this?
Yes, tools like Pandas in Python, R's mice package, and specialized libraries like fancyimpute can assist with handling missing data.
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