Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R
Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate convers...
| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing,
2019.
|
| Subjects: | |
| Online Access: | EBSCOhost Перейти в каталог НБ ТГУ |
| LEADER | 05329cam a2200577Ii 4500 | ||
|---|---|---|---|
| 001 | koha001014796 | ||
| 003 | OCoLC | ||
| 005 | 20250222070047.0 | ||
| 006 | m d | ||
| 007 | cr unu|||||||| | ||
| 008 | 190509s2019 enka o 000 0 eng d | ||
| 035 | |a koha001014796 | ||
| 040 | |a UMI |b eng |e rda |e pn |c UMI |d TEFOD |d EBLCP |d UKAHL |d MERUC |d UKMGB |d OCLCF |d YDX |d OCLCQ |d N$T | ||
| 015 | |a GBB995004 |2 bnb | ||
| 016 | 7 | |a 019365457 |2 Uk | |
| 019 | |a 1091659201 |a 1096523152 | ||
| 020 | |a 178934882X | ||
| 020 | |a 9781789348828 |q (electronic bk.) | ||
| 020 | |z 9781789346343 | ||
| 037 | |a CL0501000047 |b Safari Books Online | ||
| 050 | 4 | |a HF5415.125 | |
| 082 | 0 | 4 | |a 658.834 |2 23 |
| 049 | |a MAIN | ||
| 100 | 1 | |a Hwang, Yoon Hyup, |9 914524 | |
| 245 | 1 | 0 | |a Hands-on data science for marketing |b improve your marketing strategies with machine learning using Python and R |c Yoon Hyup Hwang. |
| 264 | 1 | |a Birmingham, UK |b Packt Publishing, |c 2019. | |
| 300 | |a 1 online resource |b illustrations | ||
| 588 | 0 | |a Online resource; title from title page (Safari, viewed May 1, 2019). | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Section 1: Introduction and Environment Setup; Chapter 1: Data Science and Marketing; Technical requirements; Trends in marketing; Applications of data science in marketing; Descriptive versus explanatory versus predictive analyses; Types of learning algorithms; Data science workflow; Setting up the Python environment; Installing the Anaconda distribution; A simple logistic regression model in Python; Setting up the R environment; Installing R and RStudio; A simple logistic regression model in R | |
| 505 | 8 | |a Chapter 3: Drivers behind Marketing EngagementUsing regression analysis for explanatory analysis; Explanatory analysis and regression analysis; Logistic regression; Regression analysis with Python; Data analysis and visualizations; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables; Combining continuous and categorical variables; Regression analysis with R; Data analysis and visualization; Engagement rate; Sales channels; Total claim amounts; Regression analysis; Continuous variables; Categorical variables | |
| 505 | 8 | |a Combining continuous and categorical variablesSummary; Chapter 4: From Engagement to Conversion; Decision trees; Logistic regression versus decision trees; Growing decision trees; Decision trees and interpretations with Python; Data analysis and visualization; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balances by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding months; Encoding jobs; Encoding marital; Encoding the housing and loan variables; Building decision trees; Interpreting decision trees | |
| 505 | 8 | |a Decision trees and interpretations with RData analysis and visualizations; Conversion rate; Conversion rates by job; Default rates by conversions; Bank balance by conversions; Conversion rates by number of contacts; Encoding categorical variables; Encoding the month; Encoding the job, housing, and marital variables; Building decision trees; Interpreting decision trees; Summary; Section 3: Product Visibility and Marketing; Chapter 5: Product Analytics; The importance of product analytics; Product analytics using Python; Time series trends; Repeat customers; Trending items over time | |
| 520 | |a Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Computing and visualizing KPIs using R; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Summary | ||
| 520 | |a This book will be an excellent resource for both Python and R developers and will help them apply data science and machine learning to marketing with real-world data sets. By the end of this book, you will be well equipped with the required knowledge and expertise to draw insights from data and improve your marketing strategies. | ||
| 653 | 0 | |a Marketing |x Data processing. | |
| 653 | 0 | |a Machine learning. | |
| 653 | 0 | |a Marketing research. | |
| 653 | 0 | |a Python (Computer program language) | |
| 653 | 0 | |a R (Computer program language) | |
| 653 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 653 | 7 | |a Marketing |x Data processing. |2 fast |0 (OCoLC)fst01010187 | |
| 653 | 7 | |a Marketing research. |2 fast |0 (OCoLC)fst01010284 | |
| 653 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 653 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207 | |
| 655 | 0 | |a EBSCO eBooks |9 905790 | |
| 655 | 4 | |a Electronic books. |9 899821 | |
| 856 | 4 | 0 | |3 EBSCOhost |u https://www.lib.tsu.ru/limit/2023/EBSCO/2094760.pdf |
| 856 | |y Перейти в каталог НБ ТГУ |u https://koha.lib.tsu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=1014796 | ||
| 910 | |a EBSCO eBooks | ||
| 999 | |c 1014796 |d 1014796 | ||
| 039 | |||