This Jupyter notebook presents a comprehensive guide to outlier detection in machine learning. It's designed to provide both theoretical insights and practical coding examples to handle outliers in various datasets.
- Theoretical Background: Begins with a brief introduction to outliers, their impact on machine learning models, and why it's crucial to detect and handle them appropriately.
- Data Exploration: Illustrates methods to explore datasets for potential outliers using statistical summaries and visualization techniques.
- Detection Techniques: Explores multiple methods for detecting outliers, including statistical approaches (like Z-score and IQR method), as well as machine learning-based methods.
- Handling Outliers: Discusses and demonstrates strategies for handling outliers, such as removal, transformation, or using robust models.
- Case Studies: Includes real-world case studies where outlier detection is applied to different datasets, providing a practical perspective.
- Interactive Code Blocks: Contains Python code blocks that can be executed interactively to understand the concepts in real-time.
- Libraries and Tools: Utilizes popular Python libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-Learn.
