What are the effects of missing data in datasets?

What are the effects of missing data in datasets?

Missing data can lead to biased results, reduced statistical power, and can complicate data analysis.

How can missing data affect machine learning models?

It can cause models to perform poorly, as they may not learn from incomplete information, leading to inaccurate predictions.

What techniques can be used to handle missing data?

Common techniques include imputation, deletion, and using algorithms that support missing values.

Are there specific industries more affected by missing data?

Yes, industries like healthcare, finance, and marketing often face significant challenges due to missing data.

Can you give an example of how missing data impacted a real-world scenario?

Sure! In healthcare, missing patient data can lead to incorrect treatment decisions, affecting patient outcomes.