using python and R Larose, Chantal D. creator Larose, Daniel T. Shahee, Shaukat Ali. text New Delhi Wiley 2025 1st ed. monographic eng

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xx, 256p. ; PB 24.9 cm PREFACE TO THE ADAPTED EDITION PREFACE TO THE US EDITION ACKNOWLEDGMENTS ABOUT THE AUTHORS CHAPTER 1 INTRODUCTION TO DATA SCIENCE 1.1 Why Data Science? 1.2 What Is Data Science? 1.3 The Data Science Methodology 1.4 Data Science Tasks 1.4.1 Description 1.4.2 Estimation 1.4.3 Classification 1.4.4 Clustering 1.4.5 Prediction 1.4.6 Association Exercises CHAPTER 2 THE BASICS OF PYTHON AND R 2.1 Downloading Python 2.2 Basics of Coding in Python 2.2.1 Using Comments in Python 2.2.2 Executing Commands in Python 2.2.3 Importing Packages in Python 2.2.4 Getting Data into Python 2.2.5 Saving Output in Python 2.2.6 Accessing Records and Variables in Python 2.2.7 Setting Up Graphics in Python 2.3 Downloading R and Rstudio 2.4 Basics of Coding in R 2.4.1 Using Comments in R 2.4.2 Executing Commands in R 2.4.3 Importing Packages in R 2.4.4 Getting Data into R 2.4.5 Saving Output in R 2.4.6 Accessing Records and Variables in R References Exercises CHAPTER 3 DATA PREPARATION 3.1 The Bank Marketing Data Set 3.2 The Problem Understanding Phase 3.2.1 Clearly Enunciate the Project Objectives 3.2.2 Translate These Objectives into a Data Science Problem 3.3 Data Preparation Phase 3.4 Adding an Index Field 3.4.1 How to Add an Index Field Using Python 3.4.2 How to Add an Index Field Using R 3.5 Changing Misleading Field Values 3.5.1 How to Change Misleading Field Values Using Python 3.5.2 How to Change Misleading Field Values Using R 3.6 Reexpression of Categorical Data as Numeric 3.6.1 How to Reexpress Categorical Field Values Using Python 3.6.2 How to Reexpress Categorical Field Values Using R 3.7 Standardizing the Numeric Fields 3.7.1 How to Standardize Numeric Fields Using Python 3.7.2 How to Standardize Numeric Fields Using R 3.8 Identifying Outliers 3.8.1 How to Identify Outliers Using Python 3.8.2 How to Identify Outliers Using R References Exercises 45 CHAPTER 4 EXPLORATOR Y DATA ANALYSIS 4.1 Eda Versus HT 4.2 Bar Graphs with Response Overlay 4.2.1 How to Construct a Bar Graph with Overlay Using Python 4.2.2 How to Construct a Bar Graph with Overlay Using R 4.3 Contingency Tables 4.3.1 How to Construct Contingency Tables Using Python 4.3.2 How to Construct Contingency Tables Using R 4.4 Histograms with Response Overlay 4.4.1 How to Construct Histograms with Overlay Using Python 4.4.2 How to Construct Histograms with Overlay Using R 4.5 Binning Based on Predictive Value 4.5.1 How to Perform Binning Based on Predictive Value Using Python 4.5.2 How to Perform Binning Based on Predictive Value Using R References Exercises CHAPTER 5 PREPARING TO MODEL THE DATA 5.1 The Story So Far 5.2 Partitioning the Data 5.2.1 How to Partition the Data in Python 5.2.2 How to Partition the Data in R 5.3 Validating Your Partition 5.4 Balancing the Training Data Set 5.4.1 How to Balance the Training Data Set in Python 5.4.2 How to Balance the Training Data Set in R 5.5 Establishing Baseline Model Performance References Exercises CHAPTER 6 DECISION TREES 6.1 Introduction to Decision Trees 6.2 Classification and Regression Trees 6.2.1 How to Build CART Decision Trees Using Python 6.2.2 How to Build CART Decision Trees Using R 6.3 The C5.0 Algorithm for Building Decision Trees 6.3.1 How to Build C5.0 Decision Trees Using Python 6.3.2 How to Build C5.0 Decision Trees Using R 6.4 Random Forests 6.4.1 How to Build Random Forests in Python 6.4.2 How to Build Random Forests in R References Exercises CHAPTER 7 MODEL EVALUATION 7.1 Introduction to Model Evaluation 7.2 Classification Evaluation Measures 7.3 Sensitivity and Specificity 7.4 Precision, Recall, and Fβ Scores 7.5 Method for Model Evaluation 7.6 An Application of Model Evaluation 7.6.1 How to Perform Model Evaluation Using R 7.7 Accounting for Unequal Error Costs 7.7.1 Accounting for Unequal Error Costs Using R 7.8 Comparing Models with and Without Unequal Error Costs 7.9 Data-Driven Error Costs Exercises CHAPTER 8 NAÏVE BAYES CLASSIFICATION 8.1 Introduction to Naïve Bayes 8.2 Bayes Theorem 8.3 Maximum a Posteriori Hypothesis 8.4 Class Conditional Independence 8.5 Application of Naïve Bayes Classification 8.5.1 Naïve Bayes in Python 8.5.2 Naïve Bayes in R References Exercises CHAPTER 9 NEURAL NETWORKS 9.1 Introduction to Neural Networks 9.2 The Neural Network Structure 9.3 Connection Weights and the Combination Function 9.4 The Sigmoid Activation Function 9.5 Backpropagation 9.6 An Application of a Neural Network Model 9.7 Interpreting the Weights in a Neural Network Model 9.8 How to Use Neural Networks in R 9.9 How to Use Neural Networks in Python References Exercises CHAPTER 10 CLUSTERING 10.1 What Is Clustering? 10.2 Introduction to the k-Means Clustering Algorithm 10.3 An Application of k-Means Clustering 10.4 Cluster Validation 10.5 How to Perform k-Means Clustering Using Python 10.5.1 k-Means Python Example Using Sklearn 10.6 How to Perform k-Means Clustering Using R Exercises CHAPTER 11 REGRESSION MODELING 11.1 The Estimation Task 11.2 Descriptive Regression Modeling 11.3 An Application of Multiple Regression Modeling 11.4 How to Perform Multiple Regression Modeling Using Python 11.5 How to Perform Multiple Regression Modeling Using Sklearn Python 11.6 How to Perform Multiple Regression Modeling Using R 11.7 Model Evaluation for Estimation 11.7.1 How to Perform Stepwise Regression Using Python 11.7.2 How to Perform Estimation Model Evaluation Using Python 11.7.3 How to Perform Estimation Model Evaluation Using R 11.8 Stepwise Regression 11.8.1 How to Perform Stepwise Regression Using R 11.9 Baseline Models for Regression References Exercises CHAPTER 12 DIMENSION REDUCTION 12.1 The Need for Dimension Reduction 12.2 Multicollinearity 12.3 Identifying Multicollinearity Using Variance Inflation Factors 12.3.1 How to Identify Multicollinearity Using Python 12.3.2 How to Identify Multicollinearity in R 12.4 Principal Components Analysis 12.5 An Application of Principal Components Analysis 12.6 How Many Components Should We Extract? 12.6.1 The Eigenvalue Criterion 12.6.2 The Proportion of Variance Explained Criterion 12.7 Performing PCA with k = 4 12.8 Validation of the Principal Components 12.9 How to Perform Principal Components Analysis Using Python 12.10 How to Perform Principal Components Analysis Using R 12.11 When Is Multicollinearity Not a Problem? References Exercises CHAPTER 13 GENERALIZED LINEAR MODELS 13.1 An Overview of General Linear Models 13.2 Linear Regression As a General Linear Model 13.3 Logistic Regression As a General Linear Model 13.4 An Application of Logistic Regression Modeling 13.4.1 How to Perform Logistic Regression Using Python 13.4.2 How to Perform Logistic Regression Using R 13.5 Poisson Regression 13.6 An Application of Poisson Regression Modeling 13.6.1 How to Perform Poisson Regression Using Python 13.6.2 How to Perform Poisson Regression Using R Reference Exercises CHAPTER 14 ASSOCIATION RULES 14.1 Introduction to Association Rules 14.2 A Simple Example of Association Rule Mining 14.3 Support, Confidence, and Lift 14.4 Mining Association Rules 14.4.1 How to Mine Association Rules Using R 14.5 Confirming Our Metrics 14.6 The Confidence Difference Criterion 14.6.1 How to Apply the Confidence Difference Criterion Using R 14.7 The Confidence Quotient Criterion 14.7.1 How to Apply the Confidence Quotient Criterion Using R Valediction References Exercises APPENDIX DATA SUMMARIZATION AND VISUALIZATION Part 1 Summarization 1: Building Blocks of Data Analysis Part 2 Visualization: Graphs and Tables for Summarizing and Organizing Data A.1 Categorical Variables A.2 Quantitative Variables Part 3 Summarization 2: Measures of Center, Variability, and Position Part 4 Summarization and Visualization of Bivariate Relationships INDEX Chantal D. Larose Chantal D. Larose earned her PhD in Statistics from the University of Connecticut in 2015, focusing her dissertation on Model-Based Clustering of Incomplete Data. As an Assistant Professor of Decision Science at SUNY New Paltz, she played a pivotal role in developing the Bachelor of Science in Business Analytics program. Currently, she serves as an Assistant Professor of Statistics and Data Science at Eastern Connecticut State University, contributing to the design of the Mathematical Sciences Department’s data science curriculum. Daniel T. Larose Daniel T. Larose completed his PhD in Statistics from the University of Connecticut in 1996, with his dissertation titled Bayesian Approaches to Meta-Analysis. A Professor of Statistics and Data Science at Central Connecticut State University, he pioneered the world’s first online Master of Science in Data Mining in 2001. As the author or coauthor of 12 textbooks, Daniel also directs the online Master of Data Science program at CCSU and operates a consulting business. Shaukat Ali Shahee Shaukat Ali Shahee received his PhD from the SJM School of Management at the Indian Institute of Technology Bombay, where his research addressed challenges in analyzing imbalanced data with diverse intrinsic characteristics. His work has been featured in renowned journals such as International Journal of Artificial Intelligence and Soft Computing, Applied Intelligence, and Data Mining and Knowledge Discovery, as well as in the prestigious Advances in Data Mining book series. With 5.5 years of industry experience, he has served as a Quantitative Research Analyst at AlphaCrest Capital Management, a Deputy Manager at Bank of Maharashtra, and a Research Engineer at the IIT Bombay CSE department. By Chantal D Larose, Daniel T Larose and Shaukat Ali Shahee. Fundamentals of Data Science Using Python and R is an essential resource for students and professionals eager to explore data science with Python and R, two of the most popular open-source tools. The book covers the entire Data Science Methodology—from problem understanding to model deployment—and has been widely praised for its clarity and practicality. This adapted edition retains the core structure of the original, while enhancing end-of-chapter questions to suit the Indian academic environment. New examples and exercises focus on India-specific datasets, encouraging students to apply their knowledge to real-world scenarios relevant to India’s socio-economic and technological contexts. This hands-on approach ensures students gain both theoretical understanding and practical skills for a data-driven world. The basics of python and R Exploratory data analysis Naive bayes classification 005.76 LARC 9789363860759 260210 20260210150604.0