| 000 -LEADER |
|---|
| fixed length control field |
01923nam a22002297a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
210302b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9788126567935 |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
English |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
658.4034 BAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bari, Anasse |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Predictive analytics for dummies / |
| Statement of responsibility, etc. |
Anasse Bari; Mohamed Chaouchi; Tommy Jung |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd edition |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
Hoboken, NJ : |
| Name of publisher, distributor, etc. |
Wiley, |
| Date of publication, distribution, etc. |
2017. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
viii, 443 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
--For dummies. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index.<br/>"Learn to: Analyze structured and unstructured data ; Use algorithms and data analysis techniques ; Build clustering, classificaion and statistical models ; Apply predictive analytics to your website and marketing efforts"--Cover. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Introduction. --<br/>pt. 1. Getting started with predictive analytics. Entering the arena ; Predictive analytics in the wild ; Exploring your data types and associated techniques ; Complexities of data --<br/>pt. 2. Incorporating algorithms in your models. Applying models ; Identifying similarities in data ; Predicting the future using data classification --<br/>pt. 3. Developing a roadmap. Convincing your management to adopt predictive analytics ; Preparing data ; Building a predictive model ; Visualization of analytical results --<br/>pt. 4. Programming predictive analytics. Creating basic prediction examples ; Creating basic examples of unsupervised predictions ; Predictive modeling with R ; Avoiding analysis traps ; Targeting big data --<br/>pt. 5. The part of tens. Ten reasons to implement predictive analytics ; Ten steps to build a predictive analytic model. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Decision making -- Data processing. |
| -- |
Decision making -- Mathematical models. |
| -- |
Management -- Data processing. |
| -- |
Management -- Mathematical models. |
| -- |
Data mining. |
| -- |
Big data. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Chaouchi, Mohamed -- author |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Jung, Tommy -- author |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
