Regression Analysis With Python
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Regression Analysis With Python
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Author : Luca Massaron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-02-29
Regression Analysis With Python written by Luca Massaron and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-29 with Computers categories.
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.
Linear Regression With Python
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Author : James V Stone
language : en
Publisher: Sebtel Press
Release Date : 2022-02-15
Linear Regression With Python written by James V Stone and has been published by Sebtel Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-15 with categories.
Linear regression is the first step, and often the only step, required to fit a simple model to data. Supported by a Glossary and tutorial appendices, this is an ideal introduction to regression analysis. Each chapter contains Python code.
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Regression Analysis With R
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Author : Giuseppe Ciaburro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-01-31
Regression Analysis With R written by Giuseppe Ciaburro and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-31 with Computers categories.
Build effective regression models in R to extract valuable insights from real data Key Features Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Book Description Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects. What you will learn Get started with the journey of data science using Simple linear regression Deal with interaction, collinearity and other problems using multiple linear regression Understand diagnostics and what to do if the assumptions fail with proper analysis Load your dataset, treat missing values, and plot relationships with exploratory data analysis Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration Deal with classification problems by applying Logistic regression Explore other regression techniques – Decision trees, Bagging, and Boosting techniques Learn by getting it all in action with the help of a real world case study. Who this book is for This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful
Regression Models With Python For Beginners Theory And Applications Of Linear Models And Logistic Model With Python From Scratch
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Author : Ai Publishing
language : en
Publisher: AI Publishing
Release Date : 2020-02-08
Regression Models With Python For Beginners Theory And Applications Of Linear Models And Logistic Model With Python From Scratch written by Ai Publishing and has been published by AI Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-08 with Computers categories.
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].
Comparative Approaches To Using R And Python For Statistical Data Analysis
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Author : Sarmento, Rui
language : en
Publisher: IGI Global
Release Date : 2017-01-06
Comparative Approaches To Using R And Python For Statistical Data Analysis written by Sarmento, Rui and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-06 with Business & Economics categories.
The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to Using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2023-06-30
An Introduction To Statistical Learning written by Gareth James and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Mathematics categories.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Cognitive Cities
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Author : Jian Shen
language : en
Publisher: Springer Nature
Release Date : 2020-06-19
Cognitive Cities written by Jian Shen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-19 with Computers categories.
This book constitutes refereed proceeding of the Second International Cognitive Cities Conference, IC3 2019, held in Kyoto, Japan, in September 2019. The 37 full papers and 46 short papers were thoroughly reviewed and selected from 206 submissions. The papers are organized according to the topical sections on cognitive city for special needs; cognitive city theory, modeling and simulation; XR and educational innovations for cognitive city; educational technology and strategy in cognitive city; safety, security and privacy in cognitive city; artificial intelligence theory and technology related to cognitive city; Internet of Things for cognitive city; business application and management for cognitive city; big data for cognitive city; engineering technology and applied science for cognitive city; maker, CT and STEAM education for cognitive city.
Machine Learning In Python
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Author : Michael Bowles
language : en
Publisher: John Wiley & Sons
Release Date : 2015-04-27
Machine Learning In Python written by Michael Bowles and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-27 with Computers categories.
Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. Predict outcomes using linear and ensemble algorithm families Build predictive models that solve a range of simple and complex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.