Why is data preprocessing important in AI projects?

Why is data preprocessing important in AI projects?

Data preprocessing is crucial because it helps clean and organize raw data, making it suitable for analysis and model training.

What are some common data preprocessing techniques?

Common techniques include normalization, handling missing values, encoding categorical variables, and feature scaling.

How does data quality affect AI model performance?

Poor data quality can lead to inaccurate models, as the AI learns from flawed information, resulting in unreliable predictions.

Can you give an example of a preprocessing step?

Sure! One example is removing duplicates from a dataset, which ensures that the model doesn't learn from repeated entries.