Linear Models With Python
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Linear Models with Python
Praise for Linear Models with R: This book is a must-have tool for anyone interested in understanding and applying linear models. The logical ordering of the chapters is well thought out and portrays Faraway’s wealth of experience in teaching and using linear models. ... It lays down the material in a logical and intricate manner and makes linear modeling appealing to researchers from virtually all fields of study. -Biometrical Journal Throughout, it gives plenty of insight ... with comments that even the seasoned practitioner will appreciate. Interspersed with R code and the output that it produces one can find many little gems of what I think is sound statistical advice, well epitomized with the examples chosen...I read it with delight and think that the same will be true with anyone who is engaged in the use or teaching of linear models. -Journal of the Royal Statistical Society Like its widely praised, best-selling companion version, Linear Models with R, this book replaces R with Python to seamlessly give a coherent exposition of the practice of linear modeling. Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference and prediction to missing data, factorial models and block designs. Numerous examples illustrate how to apply the different methods using Python. Features: Python is a powerful, open source programming language increasingly being used in data science, machine learning and computer science. Python and R are similar, but R was designed for statistics, while Python is multi-talented. This version replaces R with Python to make it accessible to a greater number of users outside of statistics, including those from Machine Learning. A reader coming to this book from an ML background will learn new statistical perspectives on learning from data. Topics include Model Selection, Shrinkage, Experiments with Blocks and Missing Data. Includes an Appendix on Python for beginners. Linear Models with Python explains how to use linear models in physical science, engineering, social science and business applications. It is ideal as a textbook for linear models or linear regression courses.
Regression Models With Python For Beginners: Theory and Applications of Linear Models and Logistic Model with Python from Scratch
Linear and Logistic Regressions with Python for Beginners with Hands-On ProjectsAre you looking for a hands-on approach to learn Regression fast? Or perhaps you have just completed a Data Science or Python course and are looking for data science models?Do you need to start learning Logistic and Linear Regression from Scratch?This book is for you. This book will give you the chance to have a fundamental understanding of regression analysis, which is needed for any data scientist or machine learning engineer. The book will achieve this by not only having an in-depth theoretical and analytical explanation of all concepts but also including dozens of hands-on, real-life projects that will help you understand the concepts better. We will start by digging into Python programming as all the projects are developed using it, and it is currently the most used programming language in the world. We will also explore the most-famous libraries for data science such as Pandas, SciPy, Sklearn, and Statsmodel. Then, we will start seeing how we can preprocess, prepare, and visualize the data, as these steps are crucial for any data science project and can take up to 80 percent of the project time. While we will focus more on the techniques normally used in regression analysis, we will also explain, in-details, all the techniques used in any data science project.What this book offers... You will learn all about regression analysis in three modules, one for simple linear regression, one for multiple regression, and a final one for logistic regression. All three modules will contain many hands-on projects using real-world datasets.Clear and Easy to Understand SolutionsAll solutions in this book are extensively tested by a group of beta readers. The solutions provided are simplified as much as possible so that they can serve as examples for you to refer to when you are learning a new skills.What this book aims to do... This book is written with one goal in mind - to help beginners overcome their initial obstacles to learning data science and Artificial Intelligence. A lot of times, newbies tend to feel intimidated by Data Science and AI. The goal of this book is to isolate the different concepts so that beginners can gradually gain competency in the fundamentals of regression before working on a project at the end of the chapter. Beginners in Data Science does not have to be scary or frustrating when you take one step at a time.Ready to start practicing and building your Regression Models? Click the BUY button now to download this bookTopics Covered: What is Regression and When to Use It? Using Python for Regression Analysis Data Preparation Simple Linear Regression Correlation Analysis Multiple Linear Regression Hands-On Project ..and more... Click the BUY button and download the book now to start learning and practicing Regression with Python.** MONEY BACK GUARANTEE BY AMAZON **If you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform or contact us by sending an email at [email protected].
Regression Analysis with Python
Author: Luca Massaron
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-02-29
Learn the art of regression analysis with Python About This Book Become competent at implementing regression analysis in Python Solve some of the complex data science problems related to predicting outcomes Get to grips with various types of regression for effective data analysis Who This Book Is For The book targets Python developers, with a basic understanding of data science, statistics, and math, who want to learn how to do regression analysis on a dataset. It is beneficial if you have some knowledge of statistics and data science. What You Will Learn Format a dataset for regression and evaluate its performance Apply multiple linear regression to real-world problems Learn to classify training points Create an observation matrix, using different techniques of data analysis and cleaning Apply several techniques to decrease (and eventually fix) any overfitting problem Learn to scale linear models to a big dataset and deal with incremental data In Detail Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer. Style and approach This is a practical tutorial-based book. You will be given an example problem and then supplied with the relevant code and how to walk through it. The details are provided in a step by step manner, followed by a thorough explanation of the math underlying the solution. This approach will help you leverage your own data using the same techniques.