Python Feature Engineering Cookbook

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Python Feature Engineering Cookbook Second Edition

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Author : Soledad Galli
language : en
Publisher:
Release Date : 2022-10-31

Python Feature Engineering Cookbook Second Edition written by Soledad Galli and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with categories.

Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries Key Features: Learn and implement feature engineering best practices Reinforce your learning with the help of multiple hands-on recipes Build end-to-end feature engineering pipelines that are performant and reproducible Book Description: Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production. What You Will Learn: Impute missing data using various univariate and multivariate methods Encode categorical variables with one-hot, ordinal, and count encoding Handle highly cardinal categorical variables Transform, discretize, and scale your variables Create variables from date and time with pandas and Feature-engine Combine variables into new features Extract features from text as well as from transactional data with Featuretools Create features from time series data with tsfresh Who this book is for: This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.

Python Feature Engineering Cookbook

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Author : Soledad Galli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-31

Python Feature Engineering Cookbook written by Soledad Galli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with Computers categories.

Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries Key Features Learn and implement feature engineering best practices Reinforce your learning with the help of multiple hands-on recipes Build end-to-end feature engineering pipelines that are performant and reproducible Book DescriptionFeature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.What you will learn Impute missing data using various univariate and multivariate methods Encode categorical variables with one-hot, ordinal, and count encoding Handle highly cardinal categorical variables Transform, discretize, and scale your variables Create variables from date and time with pandas and Feature-engine Combine variables into new features Extract features from text as well as from transactional data with Featuretools Create features from time series data with tsfresh Who this book is for This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.

Python Feature Engineering Cookbook

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Author : Soledad Galli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-30

Python Feature Engineering Cookbook written by Soledad Galli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Computers categories.

Leverage the power of Python to build real-world feature engineering and machine learning pipelines ready to be deployed to production Key Features Learn Craft powerful features from tabular, transactional, and time-series data Develop efficient and reproducible real-world feature engineering pipelines Optimize data transformation and save valuable time Purchase of the print or Kindle book includes a free PDF eBook Book Description Streamline data preprocessing and feature engineering in your machine learning project with this third edition of the Python Feature Engineering Cookbook to make your data preparation more efficient. This guide addresses common challenges, such as imputing missing values and encoding categorical variables using practical solutions and open source Python libraries. You’ll learn advanced techniques for transforming numerical variables, discretizing variables, and dealing with outliers. Each chapter offers step-by-step instructions and real-world examples, helping you understand when and how to apply various transformations for well-prepared data. The book explores feature extraction from complex data types such as dates, times, and text. You’ll see how to create new features through mathematical operations and decision trees and use advanced tools like Featuretools and tsfresh to extract features from relational data and time series. By the end, you’ll be ready to build reproducible feature engineering pipelines that can be easily deployed into production, optimizing data preprocessing workflows and enhancing machine learning model performance. What you will learn Discover multiple methods to impute missing data effectively Encode categorical variables while tackling high cardinality Find out how to properly transform, discretize, and scale your variables Automate feature extraction from date and time data Combine variables strategically to create new and powerful features Extract features from transactional data and time series Learn methods to extract meaningful features from text data Who this book is for If you're a machine learning or data science enthusiast who wants to learn more about feature engineering, data preprocessing, and how to optimize these tasks, this book is for you. If you already know the basics of feature engineering and are looking to learn more advanced methods to craft powerful features, this book will help you. You should have basic knowledge of Python programming and machine learning to get started.

Machine Learning With Python Cookbook

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Author : Chris Albon
language : en
Publisher:
Release Date : 2018

Machine Learning With Python Cookbook written by Chris Albon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Python (Computer program language) categories.

With Early Release ebooks, you get books in their earliest form—the author's raw and unedited content as he or she writes—so you can take advantage of these technologies long before the official release of these titles. You’ll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline—everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work—making it ideal as a learning tool and reference book.

Python Data Science Cookbook

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Author : Taryn Voska
language : en
Publisher: GitforGits
Release Date : 2025-02-10

Python Data Science Cookbook written by Taryn Voska and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-10 with Computers categories.

This book's got a bunch of handy recipes for data science pros to get them through the most common challenges they face when using Python tools and libraries. Each recipe shows you exactly how to do something step-by-step. You can load CSVs directly from a URL, flatten nested JSON, query SQL and NoSQL databases, import Excel sheets, or stream large files in memory-safe batches. Once the data's loaded, you'll find simple ways to spot and fill in missing values, standardize categories that are off, clip outliers, normalize features, get rid of duplicates, and extract the year, month, or weekday from timestamps. You'll learn how to run quick analyses, like generating descriptive statistics, plotting histograms and correlation heatmaps, building pivot tables, creating scatter-matrix plots, and drawing time-series line charts to spot trends. You'll learn how to build polynomial features, compare MinMax, Standard, and Robust scaling, smooth data with rolling averages, apply PCA to reduce dimensions, and encode high-cardinality fields with sparse one-hot encoding using feature engineering recipes. As for machine learning, you'll learn to put together end-to-end pipelines that handle imputation, scaling, feature selection, and modeling in one object, create custom transformers, automate hyperparameter searches with GridSearchCV, save and load your pipelines, and let SelectKBest pick the top features automatically. You'll learn how to test hypotheses with t-tests and chi-square tests, build linear and Ridge regressions, work with decision trees and random forests, segment countries using clustering, and evaluate models using MSE, classification reports, and ROC curves. And you'll finally get a handle on debugging and integration: fixing pandas merge errors, correcting NumPy broadcasting mismatches, and making sure your plots are consistent. Key Learnings You can load remote CSVs directly into pandas using read_csv, so you don't have to deal with manual downloads and file clutter. Use json_normalize to convert nested JSON responses into simple tables, making it a breeze to analyze. You can query relational and NoSQL databases directly from Python, and the results will merge seamlessly into Pandas. Find and fill in missing values using IGNSA(), forward-fill, and median strategies for all of your data over time. You can free up a lot of memory by turning string columns into Pandas' Categorical dtype. You can speed up computations with NumPy vectorization and chunked CSV reading to prevent RAM exhaustion. You can build feature pipelines using custom transformers, scaling, and automated hyperparameter tuning with GridSearchCV. Use regression, tree-based, and clustering algorithms to show linear, nonlinear, and group-specific vaccination patterns. Evaluate models using MSE, R², precision, recall, and ROC curves to assess their performance. Set up automated data retrieval with scheduled API pulls, cloud storage, Kafka streams, and GraphQL queries. Table of Content Data Ingestion from Multiple Sources Preprocessing and Cleaning Complex Datasets Performing Quick Exploratory Analysis Optimizing Data Structures and Performance Feature Engineering and Transformation Building Machine Learning Pipelines Implementing Statistical and Machine Learning Techniques Debugging and Troubleshooting Advanced Data Retrieval and Integration

Scikit Learn Cookbook

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Author : John Sukup
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-12-19

Scikit Learn Cookbook written by John Sukup and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-19 with Computers categories.

Get hands-on with the most widely used Python library in machine learning with over 80 practical recipes that cover core as well as advanced functions Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Solve complex business problems with data-driven approaches Master tools associated with developing predictive and prescriptive models Build robust ML pipelines for real-world applications, avoiding common pitfalls Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader Book DescriptionTrusted by data scientists, ML engineers, and software developers alike, scikit-learn offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling the efficient development and deployment of predictive models in real-world applications. This third edition of scikit-learn Cookbook will help you master ML with real-world examples and scikit-learn 1.5 features. This updated edition takes you on a journey from understanding the fundamentals of ML and data preprocessing, through implementing advanced algorithms and techniques, to deploying and optimizing ML models in production. Along the way, you’ll explore practical, step-by-step recipes that cover everything from feature engineering and model selection to hyperparameter tuning and model evaluation, all using scikit-learn. By the end of this book, you’ll have gained the knowledge and skills needed to confidently build, evaluate, and deploy sophisticated ML models using scikit-learn, ready to tackle a wide range of data-driven challenges. *Email sign-up and proof of purchase requiredWhat you will learn Implement a variety of ML algorithms, from basic classifiers to complex ensemble methods, using scikit-learn Perform data preprocessing, feature engineering, and model selection to prepare datasets for optimal model performance Optimize ML models through hyperparameter tuning and cross-validation techniques to improve accuracy and reliability Deploy ML models for scalable, maintainable real-world applications Evaluate and interpret models with advanced metrics and visualizations in scikit-learn Explore comprehensive, hands-on recipes tailored to scikit-learn version 1.5 Who this book is for This book is for data scientists as well as machine learning and software development professionals looking to deepen their understanding of advanced ML techniques. To get the most out of this book, you should have proficiency in Python programming and familiarity with commonly used ML libraries; e.g., pandas, NumPy, matplotlib, and sciPy. An understanding of basic ML concepts, such as linear regression, decision trees, and model evaluation metrics will be helpful. Familiarity with mathematical concepts such as linear algebra, calculus, and probability will also be invaluable.

Machine Learning With Python Cookbook

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Author : Kyle Gallatin
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-07-27

Machine Learning With Python Cookbook written by Kyle Gallatin and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-27 with Computers categories.

This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Supporting vector machines (SVM), naäve Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks

Forthcoming Books

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Author : Rose Arny
language : en
Publisher:
Release Date : 2002-04

Forthcoming Books written by Rose Arny and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-04 with American literature categories.

Hands On Feature Engineering With Python

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Author : Sahiba Chopra
language : en
Publisher:
Release Date : 2019

Hands On Feature Engineering With Python written by Sahiba Chopra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

A hands-on course to speed up the predicting power of machine learning algorithms About This Video Get expert knowledge on different future engineering techniques on different datasets Explore feature engineering techniques used in numerical datasets Uncover and execute feature extraction popular and useful techniques Build an ensemble model based on a feature engineered dataset In Detail Feature engineering is the most important aspect of machine learning. You know that every day you put off learning the process, you are hurting your model's performance. Studies repeatedly prove that feature engineering can be much more powerful than the choice of algorithms. Yet the field of feature engineering can seem overwhelming and confusing. This course offers you the single best solution. In this course, all of the recommendations have been extensively tested and proven on real-world problems. You'll find everything included: the recommendations, the code, the data sources, and the rationale. You'll get an over-the-shoulder, step-by-step approach for every situation, and each segment can stand alone, allowing you to jump immediately to the topics most important to you. By the end of the course, you'll have a clear, concise path to feature engineering and will enable you to get improved results by applying feature engineering techniques on your datasets Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Hands-On-Feature-Engineering-with-Python . If you require support please email: [email protected].

Small Press Record Of Books In Print

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Author : Len Fulton
language : en
Publisher:
Release Date : 1993

Small Press Record Of Books In Print written by Len Fulton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Books categories.