Mastering Skewed Datasets: Practical Techniques for Better Insights and Models

Introduction to Skewed Datasets and Handling Techniques

Struggling with skewed datasets that lead to biased models and inaccurate insights? Join us for an insightful live tutorial where we break down essential techniques to handle skewed data effectively. We will dive into the theoretical foundations behind these methods, helping you understand how to transform skewed datasets into meaningful insights and build better-performing models.

We will explore key techniques to address data skewness, including normalization and transformation methods to adjust distributions, resampling approaches like SMOTE for oversampling and under sampling techniques, and stratified sampling to ensure balanced dataset representation, with hands-on Python demonstrations to reinforce each concept.

What You’ll Learn

• Gain a deep understanding of how skewed datasets impact machine learning models and analytical outcomes.
• Step-by-step Python implementations of techniques to address data skewness, including normalization and transformation methods, resampling approaches like SMOTE, and stratified sampling.
• Breakdown of the theoretical foundations behind these methods to understand how to apply them effectively in real-world scenarios and when to use each approach for optimal results.
• Explore how language models deal with imbalanced text data, techniques for managing rare words and underrepresented topics, and bias mitigation strategies in NLP models.