Entity Resolution in Python: Similarity Features and Record Linkage

Duplicate data can compromise the integrity of any analysis, but finding matching records across messy datasets is a major challenge. This written guide introduces you to the core principles of entity resolution and data deduplication using Python. You will transition from manual data cleaning to designing automated, programmatic record linkage pipelines. By reading through clear, step-by-step text explanations and analyzing practical Python code snippets, you will learn how to preprocess text, compute similarity scores, and evaluate matches with precision. What you'll learn: - Understand the fundamental concepts of entity resolution, record linkage, and blocking. - Apply text preprocessing techniques to clean and standardize messy string data. - Compute similarity metrics including Levenshtein distance, Jaro-Winkler, and cosine similarity. - Configure and run matching pipelines using the Python recordlinkage library. - Integrate modern Python practices like type hints and pandas workflows into your deduplication scripts. - Evaluate matching performance using precision, recall, and F-score metrics. The course starts with foundational definitions of entity resolution before guiding you through data preparation, similarity calculation, and decision-making workflows. You will study practical code examples that demonstrate how to link datasets with confidence. This course is designed for beginner data analysts, database administrators, and Python developers who want to master data deduplication. No prior experience with entity resolution is required, though a basic familiarity with Python is helpful. Start reading today to unlock cleaner, more reliable data pipelines.