Data Science Projects with Python
A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition
Seiten
2021
|
2nd Revised edition
Packt Publishing Limited (Verlag)
978-1-80056-448-0 (ISBN)
Packt Publishing Limited (Verlag)
978-1-80056-448-0 (ISBN)
Ideal for anyone who is just getting started with machine learning, this hands-on data science book will give you experience building predictive models using industry-standard tools and techniques. It will help you develop the skills and understanding to generate valuable insights and make data-driven business decisions.
Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost
Key Features
Think critically about data and use it to form and test a hypothesis
Choose an appropriate machine learning model and train it on your data
Communicate data-driven insights with confidence and clarity
Book DescriptionIf data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.
In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.
You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.
Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.
By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.
What you will learn
Load, explore, and process data using the pandas Python package
Use Matplotlib to create compelling data visualizations
Implement predictive machine learning models with scikit-learn
Use lasso and ridge regression to reduce model overfitting
Evaluate random forest and logistic regression model performance
Deliver business insights by presenting clear, convincing conclusions
Who this book is forData Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you’re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.
Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoost
Key Features
Think critically about data and use it to form and test a hypothesis
Choose an appropriate machine learning model and train it on your data
Communicate data-driven insights with confidence and clarity
Book DescriptionIf data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.
In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.
You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.
Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.
By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.
What you will learn
Load, explore, and process data using the pandas Python package
Use Matplotlib to create compelling data visualizations
Implement predictive machine learning models with scikit-learn
Use lasso and ridge regression to reduce model overfitting
Evaluate random forest and logistic regression model performance
Deliver business insights by presenting clear, convincing conclusions
Who this book is forData Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you’re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.
Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.
Table of Contents
Data Exploration and Cleaning
Introduction to Scikit-Learn and Model Evaluation
Details of Logistic Regression and Feature Exploration
The Bias-Variance Trade-off
Decision Trees and Random Forests
Gradient Boosting, XGBoost, and SHAP (SHapley Additive exPlanations) Values
Test Set Analysis, Financial Insights, and Delivery to the Client
| Erscheinungsdatum | 02.08.2021 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Software Entwicklung ► User Interfaces (HCI) | |
| ISBN-10 | 1-80056-448-1 / 1800564481 |
| ISBN-13 | 978-1-80056-448-0 / 9781800564480 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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