Data Cleaning: 9 Ways to Clean Your ML Datasets

Clean data is essential for accurate and reproducible machine learning models. This post details nine crucial data cleaning techniques for 2024, including handling missing values, outlier detection, duplicate removal, and using tools like DagsHub’s Data Engine, Apache Airflow, and scikit-learn. By ensuring datasets are clean and well-prepared, engineers can meaningfully benchmark model performance. Automated pipelines and advanced imputation methods are also discussed to streamline the data cleaning process.