KNIME 10: How to Handle Missing Data in KNIME | Data Cleaning Step-by-Step | KNIME Tutorial скачать в хорошем качестве

KNIME 10: How to Handle Missing Data in KNIME | Data Cleaning Step-by-Step | KNIME Tutorial

In this video, you will learn how to handle missing values in KNIME Analytics Platform, one of the most important steps in data cleaning and data preparation for business analytics. Missing data is a common problem in real-world datasets, including customer records, sales data, HR databases, and financial reports. If not handled properly, missing values can lead to biased analysis and incorrect business decisions. This tutorial explains: • How to use the Missing Value node in KNIME • Methods to handle missing values in numeric data • Techniques to treat missing values in categorical data • How to verify cleaned data in KNIME • Best practices for missing value treatment from a business analytics perspective This video is part of the playlist Data Analytics using KNIME for Business Decision Making and is highly useful for MBA students, management learners, researchers, and beginners in data analytics. By the end of this session, you will understand how proper handling of missing data improves data quality, model accuracy, and data-driven decision making.