This is the code repository for Training Systems Using Python Statistical Modeling, published by Packt.
Explore popular techniques for modeling your data in Python
Python's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics.
You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them.
By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.
This book has the following features:
- Understand the importance of statistical modeling
- Learn about the various Python packages for statistical analysis
- Build predictive models from scratch using Python's scikit-learn library
- Implement regression analysis and clustering
- Learn how to train a neural network in Python
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
With the following software and hardware list you can run all code files present in the book (Chapter 1-16).
| Chapter | Software required | OS required |
|---|---|---|
| All | Python 3.6 and above | Windows, Mac OS X, and Linux (Any) |
| All | Jupyter Notebook | Windows, Mac OS X, and Linux (Any) |
| All | Anaconda | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
-
Hands-On Data Analysis with NumPy and Pandas [PACKT] [Amazon]
-
Statistical Application Development with R and Python - Second Edition [PACKT] [Amazon]
Curtis Miller is a doctoral candidate at the University of Utah studying mathematical statistics. He writes software for both research and personal interest, including the R package (CPAT) available on the Comprehensive R Archive Network (CRAN). Among Curtis Miller's publications are academic papers along with books and video courses all published by Packt Publishing. Curtis Miller's video courses include Unpacking NumPy and Pandas, Data Acquisition and Manipulation with Python, Training Your Systems with Python Statistical Modelling, and Applications of Statistical Learning with Python. His books include Hands-On Data Analysis with NumPy and Pandas.
Click here if you have any feedback or suggestions.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.