Data Preprocessing
Data Preprocessing is the initial database and coding phase of cleaning, transforming, and formatting raw input datasets to prepare them for machine learning algorithms.
Frequently Asked Questions
What are the typical steps in data preprocessing?▼
Handling missing values (imputation or removal), removing duplicates, encoding categorical text, scaling numerical values, and tokenizing text sequences.
Why is data preprocessing critical?▼
Raw real-world data is often incomplete, noisy, inconsistent, and formatted in ways that algorithms cannot process. Proper preprocessing ensures computational stability and model accuracy.
Quick Facts
- CategoryModel Training
- Key ApplicationDatabase extract-transform-load (ETL) pipelines, tokenizing raw text documents, and normalizing image pixels.
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Data Preprocessing Media Coverage & Intelligence
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