Learn how to prepare raw data for machine learning by mastering key data transformation techniques. This lesson covers essential steps such as handling missing values, feature normalization, standardization, and encoding categorical variables. You will see practical examples using the Titanic dataset, ensuring your data is ready for accurate and efficient modeling.
Follow along as we demonstrate hands-on methods for scaling and encoding features, building preprocessing pipelines, and splitting data for training and testing. By the end, you will understand the importance of clean, well-transformed data and be able to apply these skills to your own projects.
00:00 Introduction to Data Transformation
00:18 Setting Up a Clean Environment
00:46 What is Data Transformation
01:51 Loading and Exploring the Titanic Dataset
02:55 Why Transform Features
03:21 Identifying and Handling Missing Values
04:47 Normalization vs Standardization Explained
05:18 Selecting Columns for Scaling
06:06 Applying Normalization to Features
07:12 Standardizing Numeric Data
08:13 Encoding Text Columns
08:29 Label Encoding Example
09:40 One Hot Encoding Example
10:36 Automating Preprocessing with Pipelines
13:08 Importance of Feature Scaling
13:35 Mini Project: Full Preprocessing Workflow
15:22 Manual Normalization Practice
16:24 User Input Normalization Demo
17:32 Tips for Feature Scaling
18:00 Identifying Columns for Encoding
18:45 Recap and Key Takeaways
19:13 Next Steps and Call to Action
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