Modeling Techniques in Predictive Analytics with Python and R A Guide to Data Science |
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| Author:
| Miller, Thomas |
| Series title: | FT Press Analytics Ser. |
| ISBN: | 978-0-13-389206-2 |
| Publication Date: | Oct 2014 |
| Publisher: | Pearson Higher Education & Professional Group
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| Book Format: | Hardback |
| List Price: | AUD $100.95 |
Book Description:
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Today, successful firms win by understanding their data more deeply than competitors do. They compete based on analytics. In Modeling Techniques in Predictive Analytics, the Python edition, the leader of Northwestern University's prestigious analytics program brings together all the up-to-date concepts, techniques, and Python code you need to excel in analytics.
Thomas...
More Description
Today, successful firms win by understanding their data more deeply than competitors do. They compete based on analytics. In Modeling Techniques in Predictive Analytics, the Python edition, the leader of Northwestern University's prestigious analytics program brings together all the up-to-date concepts, techniques, and Python code you need to excel in analytics.
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 modeling, 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:
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Why the problem is significant
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What data is relevant
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How to explore your data
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How to model your data - first conceptually, with words and figures; and then with mathematics and programs
Miller walks through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. Extensive example code is presented in Python, a new and extremely popular language for applied statistics, statistical research, and predictive modeling; all 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).