Welcome to regression analysis! This chapter covers a different type of supervised learning, where the variable of interest is not categorical but quantitative. We will focus on different modes of linear regression. We will start by learning what linear models do and how they are estimated, using one of the oldest and simplest procedures—ordinary least squares (OLS). Next, we will evaluate how well a model fits data using statsmodels. Then, we will move on to the Bayesian linear regression model and ridge regression; this is a means of regularized linear regression. This is followed by least absolute shrinkage and selection operator (LASSO) regression, which is another regularized regression approach. Finally, we will discuss spline interpolation. While this is technically not considered to be a type of regression, it's still a...
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Table Of Contents
Training Systems Using Python Statistical Modeling
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Training Systems Using Python Statistical Modeling
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Overview of this book
Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics.
You’ll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them.
By the end of this book, you will have 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.
Table of Contents (9 chapters)
Preface
Free Chapter
Classical Statistical Analysis
Introduction to Supervised Learning
Binary Prediction Models
Regression Analysis and How to Use It
Neural Networks
Clustering Techniques
Dimensionality Reduction
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