Complete eTextbook Content:
Part I Preliminaries
Chapter 1 Introduction
Chapter 2 Overview of the Data Mining Process
Part II Data Exploration and Dimension Reduction
Chapter 3 Data Visualization
Chapter 4 Dimension Reduction
Part III Performance Evaluation
Chapter 5 Evaluating Predictive Performance
Part IV Prediction and Classification Methods
Chapter 6 Multiple Linear Regression
Chapter 7 k-Nearest Neighbors (kNN)
Chapter 8 The Naive Bayes Classifier
Chapter 9 Classification and Regression Trees
Chapter 10 Logistic Regression
Chapter 11 Neural Nets
Chapter 12 Discriminant Analysis
Chapter 13 Combining Methods: Ensembles and Uplift Modeling
Part V Mining Relationships among Records
Chapter 14 Association Rules and Collaborative Filtering
Chapter 15 Cluster Analysis
Part VI Forecasting Time Series
Chapter 16 Handling Time Series
Chapter 17 Regression-Based Forecasting
Chapter 18 Smoothing Methods
Part VII Data Analytics
Chapter 19 Social Network Analytics
Chapter 20 Text Mining
Part VIII Cases
Chapter 21 Cases
Name: Data Mining for Business Analytics: Concepts, Techniques and Applications in Python
Author: Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel
This is a eBook for the actual textbook of Data Mining for Business Analytics: Concepts, Techniques and Applications in Python, by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel.
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