Data Cleaning and Preprocessing in Data Science

Ravendracroma18 Jun, 2025Education

Data cleaning and preprocessing in data science involve detecting and correcting errors, handling missing values, and transforming raw data into a structured format. These steps ensure data quality, consistency, and accuracy, making it suitable for analysis and modeling to derive meaningful insights and build reliable machine learning models.