When working with categorical variables in machine learning, data leakage can occur if you encode categorical features before properly splitting your data into training and test sets. This is a subtle but crucial issue that can inflate validation accuracy and hurt model performance on real-world unseen data. What Is Categorical Encoding Leakage? How Does Leakage Occur? Why
In our interconnected global economy, where organizations operate across multiple continents and time zones, the seemingly simple task of managing temporal data has become one of the most complex and error-prone challenges in modern data systems. Timezone mismatches in global event data represent a silent but pervasive threat that undermines operational efficiency, corrupts analytical insights, and creates
Introduction Duplicate data is often considered a minor nuisance, but undetected duplicate records have a serious and sometimes hidden impact on data analysis, statistical modeling, and business decision-making. When duplicates go undetected, they can significantly skew probability distributions, introduce bias in models, and compromise the accuracy of insights, reporting, and operational processes. What Are Duplicate