REGRESSION\_ANALYSIS\_WITH\_R

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 1. Get Started With The Journey Of Data Science Using Simple Linear Regression 2. Deal With Interaction, Collinearity And Other Problems Using Multiple Linear Regression 3. Understand Diagnostics And What To Do If The Assumptions Fail With Proper Analysis 4. Load Your Dataset, Treat Missing Values, And Plot Relationships With Exploratory Data Analysis 5. Develop A Perfect Model Keeping Overfitting, Under-fitting, And Cross-validation Into Consideration 6. Deal With Classification Problems By Applying Logistic Regression 7. Explore Other Regression Techniques - Decision Trees, Bagging, And Boosting Techniques 8. 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 Cover......Page 1 Title Page......Page 2 Copyright and Credits......Page 3 Packt Upsell......Page 4 Contributors......Page 5 Table of Contents......Page 7 Preface......Page 11 Chapter 1: Getting Started with Regression......Page 16 Going back to the origin of regression......Page 17 Regression in the real world......Page 21 Understanding regression concepts......Page 23 Regression versus correlation......Page 27 Discovering different types of regression......Page 30 The R environment......Page 33 Installing R......Page 36 Using precompiled binary distribution......Page 38 Installing on Linux......Page 39 RStudio......Page 40 The R stats package......Page 43 The car package......Page 44 The MASS package......Page 46 The caret package......Page 47 The glmnet package......Page 48 The sgd package......Page 49 The Lars package......Page 50 Summary......Page 51 Chapter 2: Basic Concepts – Simple Linear Regression......Page 52 Association between variables – covariance and correlation......Page 53 Searching linear relationships......Page 60 Least squares regression......Page 64 Creating a linear regression model......Page 73 Exploring model results......Page 78 Diagnostic plots......Page 81 Modeling a perfect linear association......Page 87 Summary......Page 91 Chapter 3: More Than Just One Predictor – MLR......Page 92 Multiple linear regression concepts......Page 93 Building a multiple linear regression model......Page 99 Categorical variables......Page 105 Building a model......Page 106 Gradient Descent and linear regression......Page 112 Gradient Descent......Page 114 The sgd package......Page 116 Linear regression with SGD......Page 117 Polynomial regression......Page 121 Summary......Page 128 Chapter 4: When the Response Falls into Two Categories – Logistic Regression......Page 130 Understanding logistic regression......Page 131 The logit model......Page 133 Simple logistic regression......Page 136 Multiple logistic regression......Page 144 Customer satisfaction analysis with the multiple logistic regression......Page 145 Multiple logistic regression with categorical data......Page 152 Multinomial logistic regression......Page 165 Summary......Page 171 Chapter 5: Data Preparation Using R Tools......Page 172 A first look at data......Page 173 Change datatype......Page 176 Removing empty cells......Page 178 Missing values ......Page 179 Treatment of NaN values......Page 182 Finding outliers in data......Page 183 Scale of features......Page 190 Min–max normalization......Page 191 z score standardization......Page 194 Discretization in R......Page 196 Data discretization by binning......Page 197 Data discretization by histogram analysis......Page 200 Principal Component Analysis......Page 204 Summary......Page 215 Chapter 6: Avoiding Overfitting Problems - Achieving Generalization......Page 217 Understanding overfitting......Page 218 Overfitting detection – cross-validation......Page 221 Feature selection......Page 236 Stepwise regression......Page 237 Regression subset selection......Page 245 Ridge regression......Page 253 Lasso regression......Page 262 ElasticNet regression......Page 269 Summary......Page 272 Chapter 7: Going Further with Regression Models......Page 273 Robust linear regression......Page 274 Bayesian linear regression......Page 282 Basic concepts of probability......Page 283 Bayes' theorem......Page 289 Bayesian model using BAS package......Page 291 Count data model......Page 299 Poisson distributions......Page 300 Poisson regression model......Page 302 Modeling the number of warp breaks per loom......Page 304 Summary......Page 310 Chapter 8: Beyond Linearity – When Curving Is Much Better......Page 311 Nonlinear least squares......Page 312 Multivariate Adaptive Regression Splines......Page 318 Generalized Additive Model......Page 330 Regression trees......Page 337 Support Vector Regression......Page 346 Summary......Page 350 Chapter 9: Regression Analysis in Practice......Page 352 Random forest regression with the Boston dataset......Page 353 Exploratory analysis......Page 354 Multiple linear model fitting......Page 364 Random forest regression model......Page 368 Classifying breast cancer using logistic regression......Page 373 Exploratory analysis......Page 375 Model fitting......Page 380 Regression with neural networks......Page 390 Exploratory analysis......Page 392 Neural network model......Page 396 Summary......Page 408 Other Books You May Enjoy......Page 409 Index......Page 412 Build effective regression models in R to extract valuable insights from real data About This Book 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 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 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. In Detail 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 - l ..