Data preparation is essential for transforming raw data into a valuable asset, similar to refining unrefined iron ore into a usable component for manufacturing.
Before raw data can be processed and analyzed, it must be cleaned, formatted, standardized, and organized. This foundational work is essential in data preparation.
Data sourcing involves collecting raw data from various sources, verifying its quality, and ensuring compliance with privacy regulations and security requirements.
Preparing data for machine learning involves normalization and encoding, ensuring compatibility with algorithms for efficient processing and effective outcomes.
Collection
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