Python Feature Engineering Cookbook
A complete guide to crafting powerful features for your machine learning models
Created by Soledad Galli
Unlock the power of your data by learning how to create, transform, and optimize features for machine learning models. Explore practical techniques for handling missing values, encoding categories, and extracting insights from complex data types. Build efficient workflows that help your models perform better and save you time.
Packt | Aug 2024 | 396 min
What You Will Learn
You will work through hands-on examples that show how to tackle common data challenges using Python libraries. By applying practical methods to real-world datasets, you will learn when and why to use specific feature engineering techniques. Each step helps you build confidence in preparing data for robust machine learning models.
Key Features
- Impute missing values and encode categories to prepare data for modeling
- Extract features from dates, text, and time series to boost model accuracy
- Build reproducible pipelines that streamline data preprocessing tasks
Target Audience
Ideal for data scientists and machine learning practitioners with basic Python skills who want to deepen their knowledge of feature engineering. If you already understand the fundamentals and are ready to explore advanced techniques for creating powerful features and efficient pipelines, you will find valuable solutions and insights here.
