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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Hwang, Yoon Hyup, |e author. | |
| 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 | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Marketing |x Data processing. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Marketing research. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 6 | |a Marketing |x Informatique. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Marketing |x Recherche. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Marketing |x Data processing. |2 fast |0 (OCoLC)fst01010187 | |
| 650 | 7 | |a Marketing research. |2 fast |0 (OCoLC)fst01010284 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207 | |
| 776 | 0 | 8 | |i Print version: |a Hwang, Yoon Hyup. |t Hands-On Data Science for Marketing : Improve Your Marketing Strategies with Machine Learning Using Python and R. |d Birmingham : Packt Publishing Ltd, ©2019 |z 9781789346343 |
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