Data cleaning is the process of fixing or removing incorrect, incomplete, duplicate, or irrelevant data from a dataset to improve its quality. Clean data is essential for training accurate and reliable AI models.
Key steps:
- Correcting errors (e.g., fixing typos or wrong values).
- Filling in missing data or removing incomplete entries.
- Removing duplicates or irrelevant data points.
Example: Before training an AI model to predict house prices, data cleaning might involve fixing inconsistent entries (e.g., “3 bedrooms” vs. “three bedrooms”) and removing outliers (e.g., a house price listed as $1).
Clean data ensures AI systems make better and more trustworthy predictions.