Data and content are the foundation of generative artificial intelligence (AI) systems. However, the raw data (data and text) is often not ready to be used by AI systems. Data pre-processing prepares the raw data for AI systems.
Data pre-processing involves transforming raw data into a format that can be understood and analyzed by computers and machine learning algorithms. Without effective data pre-processing, one risks using poor quality data to train machine learning models resulting in models that yield irrelevant or inaccurate results.
Real world data (in the form of text, images, and video), may contain errors, inconsistencies, missing values and otherwise lack regular, uniform structure. By applying data pre-processing operations, data deficiencies are mitigated or removed.
Data pre-processing operations include:
- Cleaning: Removing inconsistencies, errors, and irrelevant data points.
- Transformation: Converting data into a suitable format (e.g., scaling numerical features, encoding categorical variables).
- Integration: Combining data from multiple sources.
- Reduction: Reducing data dimensionality (e.g., feature selection, feature extraction).
- Normalization: Ensuring data falls within a specific range, text standardization, lemmatization, removal of stop words, and punctuation.
- Handling missing values: Imputing or removing missing data.
- Removing duplicates: Eliminating identical records.
