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Key benefits

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

Description

If 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.

Who is this book for?

Data 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.

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

What do you get with a Packt Subscription?

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Ari W Aug 23, 2021

This book is great for beginners. Not everything is defined in the pages, which fosters the mindset that seeking knowledge is part of data science. Keep asking questions, aspiring data scientists!

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Joseph Shuffield Sep 23, 2021

This book is an invaluable resource for the Python student who has already acquired some proficiency with the structure of Python and the basic methods it provides for analyzing data.Python is a language that has proven to be extremely useful for exploring, analyzing, and visualizing large sets of data—and it can also be used to create predictive models to give stakeholders the detailed information they need to make vital and potentially risky business decisions. For this reason, Python is a skill that businesses increasingly expect their data experts to have—and there are plenty of online courses, boot camps, and other resources for those who want to learn.Most of these courses will be focused on the technical details of how to manipulate data—how to pull summary statistics, joining, sub-setting, and pivoting dataframes (tables), along with creating visuals—charts and graphs. Learning the code — its structure and various functions —- to perform data analysis is a bit like learning a language one word or sentence at a time; working at this level of detail, the student can rarely see the larger picture, and when it comes time to tackle a real world data problem, she’ll have many techniques at her disposal, but she might not have a clear idea of where to start.Data Science Projects With Python provides the Python student with a framework for tackling a real world data problem, from the beginning phase of exploring and cleaning the data, to interrogating it to discover insights from it, and ultimately to use modeling to predict the outcomes of various options, and then packaging and presenting those insights to stakeholders.Because the book is structured around a particular project, and takes the student through all its phases, exploring code and techniques that are relevant at each stage, it provides the context in which various bits of code the Python student may have already encountered can be best used—and in doing so, it gives the student the complete picture of how Python is actually used in a real-world business context.At each stage of the project, the book takes the student through essential code to be used in that stage—and so it provides an excellent reinforcement of skills, functions, and techniques the student may already have encountered, but now within a real-world business context—and this is a perspective that very few Python courses, which are focused on the details of code, will have time for.I would recommend this book for all Python students who want to understand just what using Python “on the job” will entail. By working through the project in the book, which is completed in a Jupyter Notebook, students will find that code they’ve already learned will be enriched by an understanding of exactly when to use it, and what it’s best for, and they will also find they’ve been given a solid framework for tackling other business data problems; they’ll know where to begin, how to conceptualize the problem, how to analyze and make valuable predictive models that can be used again and again, and how to present their work to the stakeholders.Anyone serious about a career in data science or analysis will find this book an invaluable, practical resource.

Ken F Oct 10, 2021

(Note: I'm also a technical editor of data science books, though not for this publisher.)I'm a big fan of several data science and machine learning books, most notably Francois Chollet's excellent Deep Learning with Python and Aurélien Géron's Hands-On Machine Learning -- where these books tend to fail, because their primary focus is on theory, is having enough real-world examples with walkthrough's of "what exactly does that mean and why would I do that."In my opinion, this book is written for those who are getting little stuck between theory and action.This book takes the opposite approach - it's light(ish) on theory, but extremely practical. Running through this book from front to back is like having a great co-worker who's done it all before -- it takes you through what questions you should be asking to get you to the point that you understand what to do next, then shows you the code that makes it happen. It takes you through the full lifecycle of a project, which is helpful in real world situations.Reading the book is similar to a boot camp... you'll come out with a lot more hand-on practical experience than you came in with.

GT Dec 17, 2021

This product has great examples of code to learn with, as well as machine learning algorithms. It is organized and well thought out for a lot of experience levels.

Missy Nov 19, 2021

This book is very well written and covers a wide berth of topics in data science. It is a fantastic resource, especially for programmers/software engineers who are looking to transition into data science (or augment their current code skills). The hands-on walkthroughs really help show how to apply the given concepts to actual data. I will be keeping this textbook as a reference for sure!

Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning , Second Edition

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Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning , Second Edition

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