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Regression Analysis with R - Design and develop statistical nodes to identify unique relationships within data at scale (Paperback)
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Regression Analysis with R - Design and develop statistical nodes to identify unique relationships within data at scale (Paperback)
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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 DescriptionRegression
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
forThis 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
General
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