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Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science (Paperback)

Author:  Thomas W. Miller

Thomas W. Miller (Author)
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Thomas W. Miller’s balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers and students alike. This important reference addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modelling, pricing research, finance, sports, Web and text analytics and social network analysis. He illuminates the use of cross-sectional data, time series, spatial and even spatio-temporal data. For each problem, Miller explains:

Today's definitive, comprehensive guide to using predictive analytics to overcome business challenges – now updated and reorganized for more effective learning.

1 Analytics and Data Science 1
2 Advertising and Promotion 16
3 Preference and Choice 33
4 Market Basket Analysis 43
5 Economic Data Analysis 61
6 Operations Management 81
7 Text Analytics 103
8 Sentiment Analysis 1 35
9 Sports Analytics 187
10 Spatial Data Analysis 211
11 Brand and Price 239
12 The Big Little Data Game 273
A Data Science Methods 277
A.1 Databases and Data Preparation 279
A.2 Classical and Bayesian Statistics 281
A.3 Regression and Classification 284
A.4 Machine Learning 289
A.5 Web and Social Network Analysis 291
A.6 Recommender Systems 293
A.7 Product Positioning 295
A.8 Market Segmentation 297
A.9 Site Selection 299
A.10 Financial Data Science 300
B Measurement 301
C Case Studies 315
C.1 Return of the Bubbleheads 315
C.2 Drive Time Sedans 316
C.3 Two Month’s Salary 321
C.4 Wisconsin Dells 325
C.5 Computer Choice Study 33
D Code and Utilities 335
Bibliography 379
Index 413.


Thomas W. Miller
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Pearson Education



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