Outlier Handling & Data Scaling Explained | Step by Step Data Transformation скачать в хорошем качестве

Outlier Handling & Data Scaling Explained | Step by Step Data Transformation

This video demonstrates essential data preprocessing techniques, including how to handle skewed data distributions and perform effective outlier detection using the clipping method. We then explain various data scaling methods, such as MinMaxScaler, StandardScaler, and RobustScaler, along with their mathematical formulas. Understanding these methods is crucial for proper data normalization and standardization in any machine learning project. ⏱️ Timestamps: 00:00 – What is data transformation? 01:11 – Basic feature engineering with pandas 02:17 – Log transformation 02:42 – Clipping outliers 03:20 – MinMaxScaler vs StandardScaler vs RobustScaler 04:50 – Recap checklist 🗒️ Resources mentioned in the video: https://datavisualdesign.substack.com... https://datavisualdesign.substack.com... 🎬 If you want to build video like this using Hera AI, get 15% off using this link: https://hera.cello.so/gpjkXUu6nk2 ☕ Follow us on Substack for weekly, byte-sized data science articles. Support our channel: https://buymeacoffee.com/visualdesign Substack: https://substack.com/@datavisualdesign X: https://x.com/GongDestin Towards Data Science: https://towardsdatascience.com/author... #datascience #datatransformation #featureengineering #pandas #python #scikitlearn