Access code files from the following books by Thomas Miller
- Sports Analytics and Data Science: Winning the Game with Methods and Models
- Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
- Web and Network Data Science: Modeling Techniques in Predictive Analytics
- Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science
- Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R
Sports Analytics and Data Science: Winning the Game with Methods and Models
By Thomas W. Miller
Programs and Data to Accompany "Sports Analytics and Data Science: Winning the Game with Methods and Models" Miller (2016)
Note that many R programs contain library commands for bringing in R functions included in packages. To run these programs, the user needs to first install the packages in his/her R environment. Likewise for Python programs, many utilize data structures and methods that require the prior installation and importing of Python packages.
R programs were tested under R 3.1.1 on Mac OS 10.6.8. Python programs were tested under Enthought Canopy and Python 2.7 on Mac OS 10.6.8.
| Book Location | Description of Directory or File | File Name |
| SADS Chapter 1 | Major League Baseball Player Salaries 2015 | mlb_player_salaries_2015.csv |
| National Basketball Association Player Salaries 2015 | nba_player_salaries_2015.csv | |
| National Football Association Player Salaries 2015 | nfl_player_salaries_2015.csv | |
| Major League Baseball Player Salaries and Performance Data | mlb_payroll_performance_2014.csv | |
| MLB, NBA, and NFL Player Salaries (R) | sads_exhibit_1_1.R | |
| Payroll and Performance in Major League Baseball (R) | sads_exhibit_1_2.R | |
| Making a Perceptual Map of Sports (R) | sads_exhibit_1_3.R | |
| SADS Chapter 3 | National Basketball Association Game Data from 2014-2015 Season | basketball_2014_2015_season.csv |
| NBA Team Names and Abbreviations (same as Appendix B Table B4) | nba_team_names_abbreviations.csv | |
| Assessing Team Strength by Unidimensional Scaling | sads_exhibit_3_1.R | |
| SADS Chapter 6 | Consumer Preference Data for Dodger Stadium Seating (Table 6.2) | sporting_event_ranking.csv |
| Mapping Entertainment Events and Activities (R) | sads_exhibit_6_1.R | |
| Mapping Entertainment Events and Activities (Python) | sads_exhibit_6_2.py | |
| Preferences for Sporting Events—Conjoint Analysis (R) | sads_exhibit_6_3.R | |
| Preferences for Sporting Events—Conjoint Analysis (Python) | sads_exhibit_6_4.py | |
| SADS Chapter 7 | Major League Baseball Attendance and Promotion Data for 2012 Season | bobbleheads.csv |
| Dodgers Attendance and Promotion Data for 2012 Season | dodgers.csv | |
| Shaking Our Bobbleheads Yes and No (R) | sads_exhibit_7_1.R | |
| Shaking Our Bobbleheads Yes and No (Python) | sads_exhibit_7_2.py | |
| SADS Chapter 10 | Team Winning Probabilities by Simulation (R) | sads_exhibit_10_1.R |
| Team Winning Probabilities by Simulation (Python) | sads_exhibit_10_2.py | |
| SADS Chapter 11 | Simple One-Site Web Crawler and Scraper (Python) Code Listing | sads_exhibit_11_1.py |
| Simple One-Site Web Crawler and Scraper (Python) Compressed Directory | sads_exhibit_11_1.zip | |
| Gathering Opinion Data from Twitter: Football Injuries (Python) | sads_exhibit_11_2.py | |
| SADS Appendix A | Arizona Diamondbacks Game Day Data from August 2007 | MLB_2007_ARI_data_frame.csv |
| Oklahoma City Thunder Data from 2014-2015 Season | okc_data_2014_2015.csv | |
| Programming the Anscombe Quartet (Python) | sads_exhibit_A_1.py | |
| Programming the Anscombe Quartet (R) | sads_exhibit_A_2.R | |
| Making Differential Runs Plots for Baseball (R) | sads_exhibit_A_3.R | |
| Moving Fraction Plot: A Basketball Example (R) | sads_exhibit_A_4.R | |
| Visualizing Basketball Games (R) | sads_exhibit_A_5.R | |
| Seeing Data Science as an Eclectic Discipline (R) | sads_exhibit_A_6.R | |
| SADS Appendix B | Women’s National Basketball Association (WNBA) | sads_table_B_1.csv |
| Major League Baseball (MLB) | sads_table_B_2.csv | |
| Major League Soccer (MLS) | sads_table_B_3.csv | |
| National Basketball Association (NBA) | sads_table_B_4.csv | |
| National Football League (NFL) | sads_table_B_5.csv |
Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
By Thomas W. Miller
Programs and Data to Accompany "Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python" Miller (2015)
| Book Location | Description of Directory or File | File Name |
| MDS Chapter 1 | Measuring and Modeling Individual Preferences (R) | MDS_Exhibit_1_1.R |
| Measuring and Modeling Individual Preferences (Python) | MDS_Exhibit_1_2.py | |
| "Measuring and Modeling Individual Preferences (data)" | mobile_services_ranking.csv | |
| Questions for Conjoint Survey (documentation) | questions_for_survey.txt | |
| Conjoint Analysis Spine Chart (R binary) | mtpa_spine_chart.Rdata | |
| MDS Chapter 2 | Predicting Commuter Transportation Choices (R) | MDS_Exhibit_2_1.R |
| Predicting Commuter Transportation Choices (Python) | MDS_Exhibit_2_2.py | |
| Predicting Commuter Transportation Choices (data) | sydney.csv | |
| Correlation Heat Map Utility (R binary) | correlation_heat_map.RData | |
| MDS Chapter 3 | Identifying Customer Targets (R) | MDS_Exhibit_3_1.R |
| Identifying Customer Targets (Python) | MDS_Extra_3_1.py | |
| Identifying Customer Targets (data) | bank.csv | |
| Empty Python Directory | __init__.py | |
| Evaluating Predictive Accuracy of a Binary Classifier (Python) | evaluate_classifier.py | |
| MDS Chapter 4 | Identifying Consumer Segments (R) | MDS_Exhibit_4_1.R |
| Identifying Consumer Segments (Python) | MDS_Exhibit_4_2.py | |
| Identifying Consumer Segments (data) | bank.csv | |
| MDS Chapter 5 | Predicting Customer Retention (R) | MDS_Exhibit_5_1.R |
| Predicting Customer Retention (Python) | MDS_Extra_5_1.py | |
| Predicting Customer Retention (data) | att.csv | |
| Empty Python Directory | __init__.py | |
| Evaluating Predictive Accuracy of a Binary Classifier (Python) | evaluate_classifier.py | |
| MDS Chapter 6 | Product Positioning of Movies (R) | MDS_Exhibit_6_1.R |
| Product Positioning of Movies (Python) | MDS_Exhibit_6_2.py | |
| Multidimensional Scaling Demonstration: US Cities (R) | MDS_Exhibit_6_3.R | |
| Multidimensional Scaling Demonstration: US Cities (Python) | MDS_Exhibit_6_4.py | |
| Using Activities Market Baskets for Product Positioning (R) | MDS_Exhibit_6_5.R | |
| Using Activities Market Baskets for Product Positioning (Python) | MDS_Exhibit_6_6.py | |
| Hierarchical Clustering of Activities (R) | MDS_Exhibit_6_7.R | |
| Hierarchical Clustering of Activities (Python) | MDS_Extra_6_7.py | |
| Hierarchical Clustering of Activities (data) | wisconsin_dells.csv | |
| MDS Chapter 7 | Analysis for a Field Test of Laundry Soaps (R) | MDS_Exhibit_7_1.R |
| Analysis for a Field Test of Laundry Soaps (Python) | MDS_Extra_7_1.py | |
| Analysis for a Field Test of Laundry Soaps (grouped data) | gsoaps.csv | |
| Analysis for a Field Test of Laundry Soaps (individual data) | soaps.csv | |
| MDS Chapter 8 | Shaking Our Bobbleheads Yes and No (R) | MDS_Exhibit_8_1.R |
| Shaking Our Bobbleheads Yes and No (Python) | MDS_Exhibit_8_2.py | |
| Shaking Our Bobbleheads Yes and No (data) | dodgers.csv | |
| MDS Chapter 9 | Market Basket Analysis of Grocery Store Data (R) | MDS_Exhibit_9_1.R |
| Market Basket Analysis of Grocery Store Data (Python to R) | MDS_Exhibit_9_2.py | |
| MDS Chapter 10 | Training and Testing a Hierarchical Bayes Model (R) | MDS_Exhibit_10_1.R |
| Analyzing Consumer Preferences and Building a Market Simulation (R) | MDS_Exhibit_10_2.R | |
| Training and Testing a Hierarchical Bayes Model (data) | computer_choice_study.csv | |
| Market Simulation Utilities (R binary) | mtpa_market_simulation_utilities.Rdata | |
| Split-plotting Utilities (R binary) | mtpa_split_plotting_utilities.Rdata | |
| MDS Chapter 11 | Network Models and Measures (R) | MDS_Exhibit_11_1.R |
| Analysis of Agent-Based Simulation (R) | MDS_Exhibit_11_2.R | |
| Defining and Visualizing a Small-World Network (Python) | MDS_Exhibit_11_3.py | |
| Analysis of Agent-Based Simulation (Python) | MDS_Exhibit_11_4.py | |
| Analysis of Agent-Based Simulation (data trials) | NetLogo_results | |
| Analysis of Agent-Based Simulation (summary data) | virus_results.csv | |
| MDS Chapter 12 | Competitive Intelligence: Spirit Airlines Financial Dossier (R) | MDS_Exhibit_12_1.R |
| MDS Chapter 13 | Restaurant Site Selection (R) | MDS_Exhibit_13_1.R |
| Restaurant Site Selection (Python) | MDS_Exhibit_13_2.py | |
| Restaurant Site Selection (data) | studenmunds_restaurants.csv | |
| Correlation Heat Map Utility (R binary) | correlation_heat_map.RData | |
| MDS Appendix C | AT&T Choice Study | MDS_Appendix_C_1 |
| Anonymous Microsoft Web Data | MDS_Appendix_C_2 | |
| Bank Marketing Study | MDS_Appendix_C_3 | |
| Boston Housing Study | MDS_Appendix_C_4 | |
| Computer Choice Study | MDS_Appendix_C_5 | |
| DriveTime Sedans | MDS_Appendix_C_6 | |
| Lydia E. Pinkham Medicine Company | MDS_Appendix_C_7 | |
| Procter & Gamble Laundry Soaps | MDS_Appendix_C_8 | |
| Return of the Bobbleheads | MDS_Appendix_C_9 | |
| Studenmund’s Restaurants | MDS_Appendix_C_10 | |
| Sydney Transportation Study | MDS_Appendix_C_11 | |
| ToutBay Begins Again | MDS_Appendix_C_12 | |
| Two Month’s Salary | MDS_Appendix_C_13 | |
| Wisconsin Dells | MDS_Appendix_C_14 | |
| Wikipedia Votes | MDS_Appendix_C_16 | |
| MDS Appendix D | Conjoint Analysis Spine Chart (R) | MDS_Exhibit_D1.R |
| Market Simulation Utilities (R) | MDS_Exhibit_D2.R | |
| Split-plotting Utilities (R) | MDS_Exhibit_D3.R | |
| Utilities for Spatial Data Analysis (R) | MDS_Exhibit_D4.R | |
| Correlation Heat Map Utility (R) | MDS_Exhibit_D5.R | |
| Evaluating Predictive Accuracy of a Binary Classifier (Python) | MDS_Exhibit_D6.py |
Web and Network Data Science: Modeling Techniques in Predictive Analytics
By Thomas W. Miller
Programs and Data to Accompany "Web and Network Data Science: Modeling Techniques in Predictive Analytics" Miller (2015)
Note that many R programs contain library commands for bringing in R functions included in packages. To run these programs, the user needs to first install the packages in his/her R environment. Likewise for Python programs, many utilize data structures and methods that require the prior installation and importing of Python packages.
R programs were tested under R 3.1.1 on Mac OS 10.6.8. Python programs were tested under Enthought Canopy and Python 2.7 on Mac OS 10.6.8.
| Book Location | Description of Directory or File | File Name |
| WNDS Chapter 1 | Browser Usage Data | browser_usage_2008_2014.csv |
| Analysis of Browser Usage (Python) | wnds_chapter_1.py | |
| Analysis of Browser Usage (R) | wnds_chapter_1.R | |
| WNDS Chapter 2 | ToutBay Website Traffic Data | toutbay_begins.csv |
| Website Traffic Analysis (R) | wnds_chapter_2.R | |
| WNDS Chapter 3 | Extracting and Parsing Web Site Data (Python) | wnds_chapter_3a.py |
| Extracting and Parsing Web Site Data (R) | wnds_chapter_3a.R | |
| Directory for Simple One-Page Web Scraper (Python) | wnds_chapter_3b | |
| Directory for Crawling and Scraping while Napping (Python) | wnds_chapter_3c | |
| WNDS Chapter 4 | Identifying Keywords for Testing Performance in Search (R) | wnds_chapter_4.R |
| Directory of Keywords Data for the Angels | tickets_angels | |
| Directory of Keywords Data for the Dodgers | tickets_dodgers | |
| WNDS Chapter 5 | Competitive Intelligence: Spirit Airlines Financial Dossier (R) | wnds_chapter_5.R |
| WNDS Chapter 6 | Enron E-Mail Network Data | enron_email_links.txt |
| Defining and Visualizing Simple Networks (Python) | wnds_chapter_6a.py | |
| Defining and Visualizing Simple Networks (R) | wnds_chapter_6a.R | |
| Visualizing Networks-Understanding Organizations (R) | wnds_chapter_6b.R | |
| WNDS Chapter 7 | Correlation Heat Map Utility (R) | correlation_heat_map_utility.R |
| Wikipedia Votes Data | wiki_edges.txt | |
| Networks Models and Measures (R) | wnds_chapter_7a.R | |
| Methods of Sampling from Large Networks (R) | wnds_chapter_7b.R | |
| WNDS Chapter 8 | Sentiment Analysis Negative Word List (text data) | Hu_Liu_negative_word_list.txt |
| Sentiment Analysis Positive Word List (text data) | Hu_Liu_positive_word_list.txt | |
| Directories and Subdiretories of Movie Reviews (text data) | ||
Training Data - Unsupervised/Unrated Reviews |
reviews/train/unsup | |
Training Data - Positive Reviews |
reviews/train/pos | |
Training Data - Negative Reviews |
reviews/train/neg | |
Test Data - Positive Reviews |
reviews/test/pos | |
Test Data - Negative Reviews |
reviews/test/neg | |
Test Data - Tom's Reviews |
reviews/test/tom | |
| Split-plotting Utilities (R) | R_utility_program_3.R | |
| Text Scoring Script for Sentiment Analysis (R) | R_utility_program_5.R | |
| Initializer Module (Python) | __init__.py | |
| Utility Functions (Python) | python_utilities.py | |
Evaluating the Predictive Accuracy of a Binary Classifier |
||
Text Measures for Sentiment Analysis |
||
Summative Scoring of Sentiment |
||
| Sentiment Analysis and Classification of Movie Ratings (Python) | wnds_chapter_8_program.py | |
| Sentiment Analysis and Classification of Movie Ratings (R) | wnds_chapter_8_program.R | |
| WNDS Chapter 9 | Directory of POTUS Speeches Data Organized by President Name (Oral Addresses Kennedy through Obama) | ALL_POTUS |
| Directory of PUTUS Speeches Data (Oral Addresses Kennedy through Obama) | POTUS | |
| Discovering Common Themes: POTUS Speeches (Python) | wnds_chapter_9a.py | |
| Multidimensional Scaling Results | POTUS_mds.csv | |
| Making Word Clouds: POTUS Speeches (R) | wnds_chapter_9b.R | |
| From Text Measures to Text Maps: POTUS Speeches (R) | wnds_chapter_9c.R | |
| WNDS Chapter 10 | Anonymous Microsoft Web Attribute Data | microsoft_attribute_data.csv |
| Anonymous Microsoft Web Test Data | microsoft_test_data.csv | |
| Anonymous Microsoft Web Training Data | microsoft_training_data.csv | |
| From Rules to Recommendations: The Microsoft Case (R) | wnds_chapter_10.R | |
| Anonymous Microsoft Web Data Organized as Transactions (partial output from wnds_chapter10.R) | microsoft_training_transactions.csv | |
| WNDS Chapter 11 | Directory of NetLogo Simulation Results | NetLogo_results |
| NetLogo Results Data | virus_results.csv | |
| Analysis of Agent-Based Simulation Results (Python) | wnds_chapter_11.py | |
| Analysis of Agent-Based Simulation Results (R) | wnds_chapter_11.R | |
| WNDS Appendix C | E-Mail or Spam Case Study Data | email_or_spam.csv |
| ToutBay Website Traffic Data | toutbay_begins.csv | |
| Enron E-Mail Network Data | enron_email_links.txt | |
| Directory of POTUS State of the Union Addresses (Oral and written, all Presidents) | POTUS_COMPLETE | |
| Directory of POTUS Speeches Data Organized by President Name (Oral Addresses Kennedy through Obama) | ALL_POTUS | |
| Directory of PUTUS Speeches Data (Oral Addresses Kennedy through Obama) | POTUS | |
| Directory of Keywords Data for the Angels | tickets_angels | |
| Directory of Keywords Data for the Dodgers | tickets_dodgers | |
| Wikipedia Votes Case Study Data | wiki_edges.txt | |
| Anonymous Microsoft Web Attribute Data | microsoft_attribute_data.csv | |
| Anonymous Microsoft Web Test Data | microsoft_test_data.csv | |
| Anonymous Microsoft Web Training Data | microsoft_training_data.csv | |
| WNDS Appendix D | D Utility Functions (Python) | python_utilities.py |
Evaluating the Predictive Accuracy of a Binary Classifier |
||
Text Measures for Sentiment Analysis |
||
Summative Scoring of Sentiment |
||
| Split-plotting Utilities (R) | R_utility_program_3.R | |
| Text Scoring Script for Sentiment Analysis (R) | R_utility_program_5.R | |
| Correlation Heat Map Utility (R) | correlation_heat_map_utility.R |
Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science
By Thomas W. Miller
Programs and Data to Accompany "Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R (Revised and Expanded Edition)" Miller (2015) and "Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science" Miller (2015)
Note that many R programs contain library commands for bringing in R functions included in packages. To run these programs, the user needs to first install the packages in his/her R environment. Likewise for Python programs, many utilize data structures and methods that require the prior installation and importing of Python packages.
R programs were tested under R 3.1.1 on Mac OS 10.6.8. Python programs were tested under Enthought Canopy and Python 2.7 on Mac OS 10.6.8.
| Book Location | Description of Directory or File | File Name |
| Chapter 1 | Programming the Anscombe Quartet (Python) | chapter_1_program.py |
| Programming the Anscombe Quartet (R) | chapter_1_program.R | |
| Chapter 2 | Shaking Our Bobbleheads Yes and No (data) | dodgers.csv |
| Shaking Our Bobbleheads Yes and No (Python) | chapter_2_program.py | |
| Shaking Our Bobbleheads Yes and No (R) | chapter_2_program.R | |
| Chapter 3 | Questions for Conjoint Survey (documentation) | questions_for_survey.txt |
| Measuring and Modeling Individual Preferences (data) | mobile_services_ranking.csv | |
| Conjoint Analysis Spine Chart (R) | R_utility_program_1.R | |
| Measuring and Modeling Individual Preferences (Python) | chapter_3_program.py | |
| Measuring and Modeling Individual Preferences (R) | chapter_3_program.R | |
| Chapter 4 | Market Basket Analysis of Grocery Store Data (Python) | chapter_4_program.py |
| Market Basket Analysis of Grocery Store Data (R) | chapter_4_program.R | |
| Chapter 5 | New Orders for Durable Goods (data) | FRED_DGO_data.csv |
| Employment Rate (data) | FRED_ER_data.csv | |
| Index of Consumer Sentiment (data) | FRED_ICS_data.csv | |
| New Homes Sold (data) | FRED_NHS_data.csv | |
| Working with Economic Data (Python) | chapter_5_program.py | |
| Working with Economic Data (R) | chapter_5_program.R | |
| Chapter 6 | Call Center Shifts and Needs for Wednesdays (data) | data_anonymous_bank_shifts.csv |
| Call Center Traffic for February (data) | data_anonymous_bank_february.txt | |
| Split-plotting Utilities (R) | R_utility_program_3.R | |
| Wait-time Ribbon Plot (R) | R_utility_program_4.R | |
| Call Center Scheduling (Python) | chapter_6_program.py | |
| Call Center Scheduling (R) | chapter_6_program.R | |
| Chapter 7 | Movie Taglines Original Data (text data) | taglines_copy_data.txt |
| Movie Tagline Data Preparation Script for Text Analysis (R) | R_utility_program_7.R | |
| Movie Taglines Parsed Data (text data) | movie_tagline_data_parsed.csv | |
| Split-plotting Utilities (R) | R_utility_program_3.R | |
| Text Analysis of Movie Taglines (Python) | chapter_7_program.py | |
| Text Analysis of Movie Taglines (R) | chapter_7_program.R | |
| Chapter 8 | Sentiment Analysis Negative Word List (text data) | Hu_Liu_negative_word_list.txt |
| Sentiment Analysis Positive Word List (text data) | Hu_Liu_positive_word_list.txt | |
| Directories and Subdiretories of Movie Reviews (text data) | ||
Training Data - Unsupervised/Unrated Reviews |
reviews/train/unsup | |
Training Data - Positive Reviews |
reviews/train/pos | |
Training Data - Negative Reviews |
reviews/train/neg | |
Test Data - Positive Reviews |
reviews/test/pos | |
Test Data - Negative Reviews |
reviews/test/neg | |
Test Data - Tom's Reviews |
reviews/test/tom | |
| Split-plotting Utilities (R) | R_utility_program_3.R | |
| Initializer Module (Python) | __init__.py | |
| Utility Functions (Python) | python_utilities.py | |
Evaluating the Predictive Accuracy of a Binary Classifier |
||
Text Measures for Sentiment Analysis |
||
Summative Scoring of Sentiment |
||
| Sentiment Analysis and Classification of Movie Ratings (Python) | chapter_8_program.py | |
| Sentiment Analysis and Classification of Movie Ratings (R) | chapter_8_program.R | |
| Chapter 9 | Team Winning Probabilities by Simulation (Python) | chapter_9_program.py |
| Team Winning Probabilities by Simulation (R) | chapter_9_program.R | |
| Chapter 10 | California Housing Values (data) | houses_data.txt |
| Regression Models for Spatial Data (Python) | chapter_10_program.py | |
| Regression Models for Spatial Data (R) | chapter_10_program.R | |
| Chapter 11 | Computer Choice Study (data) | computer_choice_study.csv |
| Market Simulation Utilities (R) | R_utility_program_2.R | |
| Training and Testing a Hierarchical Bayes Model (R) | chapter_11a_program.R | |
| Preference - Choice - and Market Simulation (R) | chapter_11b_program.R | |
| Appendix C | Return of the Bobbleheads (data) | bobbleheads.csv |
| DriveTime Sedans (data) | drive_time_sedans.csv | |
| Two Month's Salary (data) | two_months_salary.csv | |
| Wisconsin Dells (data) | wisconsin_dells.csv | |
| Computer Choice Study (data) | computer_choice_study.csv | |
| Appendix D | Utility Functions (Python) | python_utilities.py |
Evaluating the Predictive Accuracy of a Binary Classifier |
||
Text Measures for Sentiment Analysis |
||
Summative Scoring of Sentiment |
||
| Conjoint Analysis Spine Chart (R) | R_utility_program_1.R | |
| Market Simulation Utilities (R) | R_utility_program_2.R | |
| Split-plotting Utilities (R) | R_utility_program_3.R | |
| Wait-time Ribbon Plot (R) | R_utility_program_4.R | |
| Text Scoring Script for Sentiment Analysis (R) | R_utility_program_5.R | |
| Utilities for Spatial Data Analysis (R) | R_utility_program_6.R | |
| Movie Tagline Data Preparation Script for Text Analysis (R) | R_utility_program_7.R | |
| Python Code from Book (text data) | mtpa_Python_code.txt | |
| R Code from Book (text data) | mtpa_R_code.txt | |
| Making Word Clouds (R) | R_utility_program_8.R |
Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R
By Thomas W. Miller
Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. All example code is presented in R, today's #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).
| Overview | Overview of Data and Prgram Files | overview.pdf |
| Chapter 1 | R Program for the Anscombe Quartet (program) | chapter_1_program.R |
| Chapter 2 | Shaking Our Bobbleheads Yes and No (data) | dodgers.csv |
| Shaking Our Bobbleheads Yes and No (program) | chapter_2_program.R | |
| Chapter 3 | Measuring and Modeling Individual Preferences (data) | mobile_services_ranking.csv |
| Measuring and Modeling Individual Preferences (program) | chapter_3_program.R | |
| Chapter 4 | Market Basket Analysis of Grocery Store Data (program) | chapter_4_program.R |
| Chapter 5 | Working with Economic Data (program) | chapter_5_program.R |
| Chapter 6 | Call Center Scheduling Problem and Solution (shift data) | data_anonymous_bank_shifts.csv |
| Call Center Scheduling Problem and Solution (call center data) | data_anonymous_bank_february.txt | |
| Call Center Scheduling Problem and Solution (program) | chapter_6_program.R | |
| Chapter 7 | Text Analytics of Movie Taglines (data) | taglines_copy_data.txt |
| Text Analytics Book R Code (data for world cloud) | MTPA_R_code.txt | |
| Text Analytics of Movie Taglines (program) | chapter_7_program.R | |
| Chapter 8 | Sentiment Analysis and Classification of Movie Ratings (Hu and Liu negative word list) | Hu_Liu_negative_word_list.txt |
| Sentiment Analysis and Classification of Movie Ratings (Hu and Liu positive word list) | Hu_Liu_positive_word_list.txt | |
| Sentiment Analysis and Classification of Movie Ratings (directory of text files reviews) | train/unsup | |
| Sentiment Analysis and Classification of Movie Ratings (directory of text files reviews) | train/pos | |
| Sentiment Analysis and Classification of Movie Ratings (directory of text files reviews) | train/neg | |
| Sentiment Analysis and Classification of Movie Ratings (directory of text files reviews) | test/pos | |
| Sentiment Analysis and Classification of Movie Ratings (directory of text files reviews) | test/neg | |
| Sentiment Analysis and Classification of Movie Ratings (directory of text files reviews) | test/tom | |
| Sentiment Analysis and Classification of Movie Ratings (program) | chapter_8_program.R | |
| Word Scoring Code for Sentiment Analysis (program, same as in Appendix C) | appendix_c5_program.R | |
| Chapter 9 | Winning Probabilities by Simulation (Negative Binomial Model) (program) | chapter_9_program.R |
| Chapter 10 | Computer Choice Study: Training and Testing with Hierarchical Bayes (data) | computer_choice_study.csv |
| Computer Choice Study: Training and Testing with Hierarchical Bayes (program) | chapter_10a_program.R | |
| Preference, Choice, and Market Simulation (program) | chapter_10b_program.R | |
| Chapter 11 | California Housing Values: Regression and Spatial Regression Models (data) | houses_data.txt |
| California Housing Values: Regression and Spatial Regression Models (program) | chapter_11_program.R | |
| Appendix C | Conjoint Analysis Spine Chart (program) | appendix_c1_program.R |
| Market Simulation Utilities (program) | appendix_c2_program.R | |
| Split-plotting Utilities (program) | appendix_c3_program.R | |
| Wait-time Ribbon Plot (program) | appendix_c4_program.R | |
| Word Scoring Code for Sentiment Analysis (program) | appendix_c5_program.R | |
| Utilities for Spatial Data Analysis (program) | appendix_c6_program.R | |