What are the best practices for data preprocessing in Python?
An example of an AI chatbot conversation about data preprocessing techniques in Python.
What are the best practices for data preprocessing in Python?
Best practices include handling missing values, normalizing data, and encoding categorical variables.
How do I handle missing values in my dataset?
You can handle missing values by using techniques like imputation, dropping rows, or filling with a specific value.
What libraries should I use for data preprocessing?
Popular libraries include Pandas for data manipulation, NumPy for numerical operations, and Scikit-learn for preprocessing functions.
Can you give an example of normalizing data?
Sure! You can use MinMaxScaler from Scikit-learn to scale your data to a range between 0 and 1.
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