Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data
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Table of contents:Introduction. Two Simple Data Mining Methods for Variable Assessment. Logistic Regression: The Workhorse of Database Response Modeling. Ordinary Regression: The Workhorse of Database Profit Modeling. CHAID for Interpreting a Logistic Regression Model. The Importance of the Regression Coefficient. The Predictive Contribution Coefficient: A Measure of Predictive Importance. CHAID for Specifying a Model with Interaction Variables. Market Segment Classification Modeling With LogisticThis expert compilation delivers a collection of successful database marketing methodologies for b...
Table of contents:
Introduction. Two Simple Data Mining Methods for Variable Assessment. Logistic Regression: The Workhorse of Database Response Modeling. Ordinary Regression: The Workhorse of Database Profit Modeling. CHAID for Interpreting a Logistic Regression Model. The Importance of the Regression Coefficient. The Predictive Contribution Coefficient: A Measure of Predictive Importance. CHAID for Specifying a Model with Interaction Variables. Market Segment Classification Modeling With Logistic
This expert compilation delivers a collection of successful database marketing methodologies for big data. It offers solutions to common problems in the database marketing industry, focusing on the needs of data analysts and data miners.
The quantitative techniques described marry traditional statistical methodologies with new machine learning methods, and the book examines three concepts in model assessment: traditional decile analysis, precision, and separability. This fascinating book also explores cutting-edge techniques including genetic intelligent hybrid models.
By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge.
This expert compilation delivers a collection of successful database marketing methodologies and offers solutions to common problems in the database marketing industry, focusing on the needs of data analysts and data miners.
Introduction. Two Simple Data Mining Methods for Variable Assessment. Logistic Regression: The Workhorse of Database Response Modeling. Ordinary Regression: The Workhorse of Database Profit Modeling. CHAID for Interpreting a Logistic Regression Model. The Importance of the Regression Coefficient. The Predictive Contribution Coefficient: A Measure of Predictive Importance. CHAID for Specifying a Model with Interaction Variables. Market Segment Classification Modeling With Logistic
This expert compilation delivers a collection of successful database marketing methodologies for big data. It offers solutions to common problems in the database marketing industry, focusing on the needs of data analysts and data miners.
The quantitative techniques described marry traditional statistical methodologies with new machine learning methods, and the book examines three concepts in model assessment: traditional decile analysis, precision, and separability. This fascinating book also explores cutting-edge techniques including genetic intelligent hybrid models.
By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge.
This expert compilation delivers a collection of successful database marketing methodologies and offers solutions to common problems in the database marketing industry, focusing on the needs of data analysts and data miners.