How will you handle missing data in a dataset?

1. 1Identify missing data using statistical methods
2. 2Apply imputation techniques to fill gaps
3. 3Use data augmentation to increase dataset size
4. 4Test models with and without imputed data
5. 5Evaluate model performance using metrics like accuracy and F1 score

Data scientists and analysts benefit from handling missing data to ensure reliable insights, and software engineers can apply these techniques to improve data quality in their applications

💡 Proper handling of missing data is crucial for reliable analysis and machine learning model performance

💡 Handle missing data to improve model accuracy