5 - Feature Scaling in Python
Learn how to apply feature scaling in Python to prepare your data for machine learning. This tutorial covers the basics of scaling, why it is important, and step-by-step methods to scale features using built-in Python, numpy, pandas, and scikit-learn. You will see practical examples, learn about min-max scaling and standardization, and discover how to handle outliers and errors.
Follow along with hands-on exercises, mini projects, and challenges to build your skills. By the end, you will know how to scale different types of data and choose the right approach for your analysis or model.
00:00 Introduction to feature scaling
00:18 Why feature scaling is important
00:53 Exploring a simple data set
01:19 Measuring feature ranges
01:56 Understanding min-max scaling
02:24 Scaling heights and weights
03:27 Practicing with user input
03:53 Handling outliers in scaling
04:39 Introduction to standardization
05:22 When to scale features
05:49 Scaling with numpy
06:17 Using scikit-learn for scaling
06:52 Scaling multiple features
07:20 Inverse transforming scaled data
07:46 Scaling the Iris data set
08:17 Scaling custom test scores
08:52 Scaling survey ages
09:37 Writing a reusable scaling function
10:12 Handling errors in scaling
10:51 Scaling data with pandas
11:23 Challenge: scaling temperatures
11:50 Recap and next steps
12:24 Final thoughts and encouragement
#Python #MachineLearning #DataScience
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