Data Drift
AI Infrastructure
The process of detecting and correcting corrupt, inaccurate, or irrelevant data from a dataset.
Detailed Explanation
Data Cleaning is the process of identifying and rectifying errors, inconsistencies, or inaccuracies within a dataset to ensure its quality and reliability. It involves handling missing values, removing duplicates, correcting typos, and standardizing formats, which enhances the accuracy of AI models and analysis by providing clean, trustworthy data for training and decision-making.
Use Cases
- •Use case: Improve model accuracy by preprocessing customer data to remove duplicates, correct errors, and standardize formats before training AI algorithms.
Related Terms
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