Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn

by Stephen Klosterman

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python

Key FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook DescriptionData Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You'll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you'll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.

By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is forIf you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.

Table of ContentsData Exploration and CleaningIntroduction to Scikit-Learn and Model EvaluationDetails of Logistic Regression and Feature ExplorationThe Bias-Variance Trade-offDecision Trees and Random ForestsImputation of Missing Data, Financial Analysis, and Delivery to Client

374 pages, Kindle Edition

Published April 30, 2019

Reviews

[Darin · Rating 5 out of 5]

Let's start with the bad. The title says "Projects" (plural), suggesting many different projects. While this is technically true because of a couple of projects using simulated data, really there is a single dataset used throughout the book. Also, the index needs to be beefed up quite a bit (e.g., where is ROC?).

So why did I give the book 5 stars? The author spends 300+ pages analyzing the data, which consists of features related to default payment history. The machine learning methods he uses consist primarily of logistic regression and decision trees (including random forests).

Along the way he introduces a large variety of relevant topics, including statistical tests (e.g., F-test, tests of normality and correlation) and how to deal with missing values. Numpy, Pandas, and Matplotlib are used for processing the data and he doesn't assume you are already familiar with how to use these libraries. He also teaches some more theoretical topics, such as gradient descent for learning the parameters in the logistic regression model.

While various other books on Data Science cover some of the previously mentioned topics, what really sets this book apart from most of those other books is the depth in which he covers those topics as well as his thought process behind what he chooses to do and how to interpret the results. I really felt like I was sitting with a Data Scientist while he conducted his analysis.

Note that I believe (and follow) the meanings Goodreads gives for what each number of stars means. Therefore, the majority of my ratings are 3 stars ("liked it").

[Yogi Saputro · Rating 4 out of 5]

Pros:
- thorough and comprehensive
Cons:
- contains one big project only

[Andrew Chen · Rating 4 out of 5]

A good introductory book. But not quite advanced. Recommend to newbies.
Also, it’s good to read this book on a computer, rather than read the printed book. Plus, the eBook would be much cheaper.

[Mahad Mahad · Rating 3 out of 5]

Even though it covers concepts comprehensively, it isn't a very beginner-friendly way of learning Data Science. You find yourself racking your brain as a beginner working on just one project.