How can I handle missing data in my dataset?

How can I handle missing data in my dataset?

There are several strategies to handle missing data, such as imputation, deletion, or using algorithms that support missing values. What type of data are you working with?

I'm working with a customer dataset that has missing values in age and income.

For numerical data like age and income, you could use mean or median imputation. Alternatively, you could use predictive modeling to estimate the missing values. Would you like to know more about any specific method?

Yes, can you explain mean imputation?

Mean imputation involves replacing missing values with the mean of the available values for that variable. It's simple but can reduce variability. Would you like to see an example?

That would be great! How do I implement it in Python?

You can use the pandas library. Here's a quick example: df['age'].fillna(df['age'].mean(), inplace=True). This replaces missing age values with the mean age.