AI Glossary/Data Preprocessing
Data Science

Data Preprocessing

Cleaning and transforming raw data into a format suitable for machine learning.

In-depth explanation

Preprocessing prepares data for modeling by handling missing values, removing duplicates, correcting errors, encoding categories, scaling features, and more. It's often the most time-consuming part of ML projects but is crucial for model performance. Techniques include imputation, normalization, one-hot encoding, and outlier handling.

Examples

Handling missing values
Scaling numerical features
Encoding categories

Related terms

Feature Engineering

More in Data Science

Data Augmentation

Artificially increasing training data by creating modified versions of existing data.

Train-Test Split

Dividing data into separate sets for training and evaluating model performance.

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