What are some common data issues in AI projects and their solutions?

What are some common data issues in AI projects?

Common data issues include missing values, data bias, and data quality problems.

How can I handle missing values in my dataset?

You can handle missing values by using techniques like imputation, removing missing data, or using algorithms that support missing values.

What is data bias and how can I mitigate it?

Data bias occurs when the training data is not representative of the real-world scenario. You can mitigate it by ensuring diverse data collection and using bias detection tools.

Can you suggest some tools for data quality assessment?

Sure! Tools like Talend, Apache Griffin, and DataCleaner are great for assessing data quality.