Master Skewed Datasets: Practical Techniques for Better Insights and Models

Learn practical techniques for handling skewed datasets in this 52-minute tutorial from Data Science Dojo. Discover how to overcome the challenges of data imbalance that lead to biased models and inaccurate insights. Explore essential methods including normalization and transformation techniques to adjust distributions, resampling approaches like SMOTE for oversampling and undersampling, and stratified sampling—all demonstrated through hands-on Python implementations. Gain deep understanding of how skewed datasets impact machine learning models and analytical outcomes. Examine how Large Language Models handle imbalanced text data, techniques for managing rare words and underrepresented topics, and strategies for mitigating bias in NLP models.