Data mining for business analytics : concepts, techniques, and applications with XLMiner®.
Shmueli, Galit
Data mining for business analytics : concepts, techniques, and applications with XLMiner®. - 3. - 514 páginas : : figuras ; tables.
Contents.
CHAPTER 1 Introduction -- CHAPTER 2 Overview of the Data Mining Process. -- CHAPTER 3 Data Visualization. -- CHAPTER 4 Dimension Reduction. -- CHAPTER 5 Evaluating Predictive Performance. -- 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. -- CHAPTER 14 Association Rules and Collaborative Filtering. -- CHAPTER 15 Cluster Analysis. -- CHAPTER 16 Handling Time Series. -- CHAPTER 17 Regression-Based Forecasting. -- CHAPTER 18 Smoothing Methods. -- CHAPTER 19 Social Network Analytics. -- CHAPTER 20 Text Mining. -- CHAPTER 21 Cases.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
9781118729274
Hojas de cálculo electrónicas
Electronic spreadsheets
Hojas de cálculo--programas informáticos
Spreadsheets--Computer programs
Hojas de cálculo--software informático
Spreadsheets--Computer software
005.54 / S558
Data mining for business analytics : concepts, techniques, and applications with XLMiner®. - 3. - 514 páginas : : figuras ; tables.
Contents.
CHAPTER 1 Introduction -- CHAPTER 2 Overview of the Data Mining Process. -- CHAPTER 3 Data Visualization. -- CHAPTER 4 Dimension Reduction. -- CHAPTER 5 Evaluating Predictive Performance. -- 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. -- CHAPTER 14 Association Rules and Collaborative Filtering. -- CHAPTER 15 Cluster Analysis. -- CHAPTER 16 Handling Time Series. -- CHAPTER 17 Regression-Based Forecasting. -- CHAPTER 18 Smoothing Methods. -- CHAPTER 19 Social Network Analytics. -- CHAPTER 20 Text Mining. -- CHAPTER 21 Cases.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
9781118729274
Hojas de cálculo electrónicas
Electronic spreadsheets
Hojas de cálculo--programas informáticos
Spreadsheets--Computer programs
Hojas de cálculo--software informático
Spreadsheets--Computer software
005.54 / S558