What types of data quality issues have you encountered?

Data quality issues commonly encountered in machine learning and AI projects include:

• Missing values: absent data can distort model training.
• Noise and Outliers: anomalous data that can degrade model quality.
• Data inconsistency: contradictory or duplicate records.
• Sampling bias: data does not reflect the true distribution, leading to poor generalization.
• Incorrect formats and data types: for example, numerical data represented as strings.
• Low-quality annotations: errors in labeling, critical for supervised learning.

To address these issues, methods such as data cleaning, validation, normalization, and analysis of distribution and correlations are used.