Step-by-step instructions for data preprocessing and feature engineering to streamline workflows
Advanced techniques for imputing missing values, encoding categorical variables, and transforming numerical and discretized variables for better model performance
Automated feature extraction from date, time, and text data using Python libraries
Hands-on examples and real-world case studies to apply learned techniques in practice
Includes free PDF ebook for added value with print or Kindle book purchase
Summarized by Shop
Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to production Key Features
Craft powerful features from tabular, transactional, and time-series data
Develop efficient and reproducible real-world feature engineering pipelines