Data Preprocessing and Cleaning with KNIME Analytics Platform focuses on preparing raw datasets for analysis and machine learning by improving data quality and consistency. It enables users to design visual workflows to clean, transform, and standardize data efficiently without extensive coding. This training explains key preprocessing tasks such as handling missing values, removing duplicates, outlier detection, normalization, encoding categorical variables, and data transformation. It also covers data integration from multiple sources, feature engineering, and workflow automation techniques within KNIME. You will learn how organizations use preprocessing to improve model accuracy, reliability, and performance in analytics projects. The course also highlights best practices for building scalable and efficient data cleaning pipelines.
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