Class 11: Data Preprocessing and Feature Engineering
Summary: Master critical data preparation techniques that transform raw data into analysis-ready formats.
Learning Objectives:
- Handle missing data using appropriate strategies
- Transform and normalize data for analysis
- Engineer features that enhance model performance
Key Topics:
- Missing data treatment: Imputation strategies and deletion criteria
- Data transformation: Scaling, normalization, and encoding
- Feature creation and selection methodologies
- Data wrangling best practices
Activities:
- Data cleaning challenges with messy datasets
- Feature engineering competition
Pipeline construction for data preprocessing