Interpretable Entity Resolution with CatBoost and SHAP

In today's data-driven world, connecting disparate datasets and identifying duplicate records is critical, but black-box matching algorithms can leave you guessing why records were merged. This text-based course guides you through building transparent, highly accurate entity resolution models. You will learn how to prepare record pairs, engineer similarity features, train a powerful CatBoost classifier, and apply SHAP (SHapley Additive exPlanations) to explain exactly why records match or don't match. What you'll learn: - Understand the foundational concepts of entity resolution, record linkage, and data deduplication. - Engineer robust similarity features using text distance metrics and modern data preprocessing techniques. - Train a CatBoost classification model optimized for identifying matching and non-matching record pairs. - Apply SHAP values to explain individual matching decisions and debug model predictions. - Evaluate matching model performance using key metrics like precision, recall, and F1-score. - Implement best practices for maintaining model interpretability and transparency in production data pipelines. The course starts with essential terminology and data matching fundamentals before walking you through feature engineering, model training, and interpretability techniques through clear written explanations and code snippets. It is designed for beginner data analysts, data scientists, and software engineers who want to master record matching without needing prior experience in complex entity resolution frameworks. Start reading today to unlock clear, explainable insights from your data matching workflows.