
Feature engineering is a crucial step in the machinelearning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machinelearning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.
Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a realworld, structured dataset with several featureengineering techniques. Python packages including numpy, Pandas, Scikitlearn, and Matplotlib are used in code examples.
You’ll examine:

Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms

Natural text techniques: bagofwords, ngrams, and phrase detection

Frequencybased filtering and feature scaling for eliminating uninformative features

Encoding techniques of categorical variables, including feature hashing and bincounting

Modelbased feature engineering with principal component analysis

The concept of model stacking, using kmeans as a featurization technique

Image feature extraction with manual and deeplearning techniques



Python Data Analysis
Key Features

Find, manipulate, and analyze your data using the Python 3.5 libraries

Perform advanced, highperformance linear algebra and mathematical calculations with clean and efficient Python code

An easytofollow guide with realistic examples that are frequently used in realworld data...
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Think Python
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