Feature Scaling with StandardScaler and MinMaxScaler for distance-Based Algorithms скачать в хорошем качестве

Feature Scaling with StandardScaler and MinMaxScaler for distance-Based Algorithms

In this lecture, I comprehensively review three major clustering algorithms: K-Means, Hierarchical (Agglomerative), and DBSCAN. Their strengths, limitations, and ideal use cases are compared. This is to help you choose the right approach for your data. The discussion then transitions to feature scaling, a major preprocessing step for distance-based machine learning models. Using intuitive examples like predicting house prices, the lecture demonstrates how features with vastly different numerical ranges can bias your results, causing large-scale variables to dominate the clustering process. You will learn two essential scaling techniques: StandardScaler, which centers data at zero using z-score normalization, and MinMaxScaler, which transforms values to a fixed range between zero and one. By the end of this session, you will understand when and why to apply feature scaling to ensure all variables contribute fairly to your model's performance.