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What is the most appropriate technique to handle missing values for a categorical column in a dataset before fitting it to a model?
You have a dataset with 15 features and you want to visualize it on a 2D plot for exploratory data analysis. What would be the first step in using PCA (Principal Component Analysis) to achieve this?
You are working on a machine learning project where you need to build a model for image classification. The project also requires heavy numerical computations and data preprocessing. Which of the following Python libraries would be most suitable to complete your project successfully?
You have a Pandas DataFrame that contains missing values. Which of the following methods will replace all NaNs with zeros?
You have a dataset of a retail company's sales transactions, which includes details like customer ID, transaction amount, transaction date, and store ID. You're tasked with summarizing monthly revenue for each store. What would be the most appropriate data aggregation technique?
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