What are the best practices for using EDA tools?
When using EDA tools, it's important to start with a clear understanding of your data and objectives. Always clean and preprocess your data before analysis.
Can you elaborate on data cleaning techniques?
Sure! Data cleaning techniques include handling missing values, removing duplicates, and correcting inconsistencies in your dataset.
What visualization techniques should I use?
Common visualization techniques include scatter plots for relationships, histograms for distributions, and box plots for outliers.
How do I choose the right EDA tool for my project?
Consider factors like the size of your dataset, the complexity of your analysis, and your familiarity with the tool. Popular options include Python libraries like Pandas and visualization tools like Tableau.
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