Book Image

Building Statistical Models in Python

By : Huy Hoang Nguyen, Paul N Adams, Stuart J Miller
Book Image

Building Statistical Models in Python

By: Huy Hoang Nguyen, Paul N Adams, Stuart J Miller

Overview of this book

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.
Table of Contents (22 chapters)
1
Part 1:Introduction to Statistics
7
Part 2:Regression Models
10
Part 3:Classification Models
13
Part 4:Time Series Models
17
Part 5:Survival Analysis

To get the most out of this book

You will need access to download and install open-source code packages implemented in the Python programming language and accessible through PyPi.org or the Anaconda Python distribution. While a background in statistics is helpful, but not necessary, this book assumes you have a decent background in basic algebra. Each unit of this book is independent of the other units, but the chapters within each unit build upon each other. Thus, we advise you to begin each unit with that unit’s first chapter to understand the content.

Software/hardware covered in the book

Operating system requirements

Python version ≥ 3.8

Windows, macOS, or Linux

Statsmodels 0.13.2

SciPy 1.8.1

lifelines 0.27.4

scikit-learn 1.1.1

pmdarima 2.02

Sktime 0.15.0

Pandas 1.4.3

Matplotlib 3.5.2

Numpy 1.23.0

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.