How can I improve data quality for my ML models?
An example of an AI chatbot conversation about enhancing data quality for machine learning models.
How can I improve data quality for my ML models?
Improving data quality involves several steps, such as data cleaning, validation, and enrichment. What specific issues are you facing?
I have a lot of missing values in my dataset.
You can handle missing values by using techniques like imputation, where you fill in missing data with estimates, or by removing rows with missing values. Which approach do you prefer?
Imputation sounds good. What methods can I use?
Common imputation methods include mean, median, mode, or using predictive models. It depends on your data type and distribution. Do you have a specific method in mind?
I think using the mean would be simplest. Any tips on implementation?
You can use libraries like Pandas in Python to easily calculate the mean and fill missing values. Would you like a code snippet to get started?
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