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| 100 | 1 |
▼a Hwang, Yoon Hyup,
▼e author. |
| 245 | 10 |
▼a Hands-on data science for marketing :
▼b improve your marketing strategies with machine learning using Python and R /
▼c Yoon Hyup Hwang. |
| 260 | |
▼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 |
| 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. |
| 588 | 0 |
▼a Online resource; title from title page (Safari, viewed May 1, 2019). |
| 590 | |
▼a Added to collection customer.56279.3 |
| 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 | 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 |
| 655 | 4 |
▼a Electronic books. |
| 776 | 08 |
▼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, 짤2019
▼z 9781789346343 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2094760 |
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