Dataset Preparation: Merging Data for Machine Learning Models

Raw data is rarely ready for machine learning right out of the box. To build accurate predictive models, you must first learn how to combine disparate data sources—such as sales transactions, store details, and holiday calendars—into a single, cohesive dataset. This text-only course guides you through the fundamental principles of data merging and preprocessing. You will learn how to align features, handle mismatched keys, and validate your merges to prevent data leakage and ensure your machine learning models receive clean, structured training data. What you will learn: Understand the foundational concepts of relational database joins and data alignment; Merge transactional training data with descriptive feature sets using key identifiers; Apply modern merge validation techniques to prevent duplicate rows and data corruption; Handle missing values, mismatched keys, and temporal data like holiday calendars effectively; Prepare a final, cleaned dataset specifically structured for retail sales forecasting; Practice data preprocessing workflows using clear, step-by-step written exercises and code examples. You will start by exploring core data alignment terminology and join types before moving on to practical merging strategies. Through written explanations and realistic code snippets, you will master the step-by-step workflow of assembling a complete dataset for predictive modeling. This course is designed for beginner data analysts and aspiring machine learning engineers. No advanced programming or prior machine learning experience is required. Start reading today to master the essential data preparation skills that power successful machine learning models.