Data Preprocessing in Data Science
Data preprocessing is a foundational step in data science that involves cleaning, transforming, and organizing raw data into a structured and usable format before analysis or model building. It ensures data quality, consistency, and reliability by addressing missing values, removing duplicates, handling outliers, normalizing or scaling features, encoding categorical variables, and correcting inconsistencies. Effective preprocessing improves the accuracy and performance of machine learning algorithms, reduces bias, and helps uncover meaningful patterns. Since real-world data is often noisy and incomplete, data preprocessing plays a critical role in turning raw datasets into valuable insights and enabling robust decision-making across research, business, and artificial intelligence applications.
Data preprocessing, data cleaning, data transformation, feature scaling, normalization, standardization, missing value handling, outlier detection, data wrangling, feature engineering, data integration, encoding categorical data, data quality, data preparation, ETL, machine learning pipeline, data normalization, data reduction, data validation, preprocessing techniques.
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