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Hands-on data science for marketing: improve your marketing strategies with machine learning using Python and R/ Yoon Hyup Hwang.
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| dc.contributor.author | Hwang Yoon Hyup | |
| dc.date.accessioned | 2024-01-26T21:32:36Z | |
| dc.date.available | 2024-01-26T21:32:36Z | |
| dc.date.issued | 2019 | |
| dc.identifier.citation | Hwang. Hands-on data science for marketing: improve your marketing strategies with machine learning using Python and R - 1 online resource : - URL: https://libweb.kpfu.ru/ebsco/pdf/2094760.pdf | |
| dc.identifier.isbn | 178934882X | |
| dc.identifier.isbn | 9781789348828 | |
| dc.identifier.uri | https://dspace.kpfu.ru/xmlui/handle/net/178265 | |
| dc.description.abstract | 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 | |
| dc.description.tableofcontents | 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 | |
| dc.description.tableofcontents | 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 | |
| dc.description.tableofcontents | 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 | |
| dc.description.tableofcontents | 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 | |
| dc.language | English | |
| dc.language.iso | en | |
| dc.subject.other | Marketing -- Data processing. | |
| dc.subject.other | Machine learning. | |
| dc.subject.other | Marketing research. | |
| dc.subject.other | Python (Computer program language) | |
| dc.subject.other | R (Computer program language) | |
| dc.subject.other | Machine learning. | |
| dc.subject.other | Marketing -- Data processing. | |
| dc.subject.other | Marketing research. | |
| dc.subject.other | Python (Computer program language) | |
| dc.subject.other | R (Computer program language) | |
| dc.subject.other | Electronic books. | |
| dc.title | Hands-on data science for marketing: improve your marketing strategies with machine learning using Python and R/ Yoon Hyup Hwang. | |
| dc.type | Book | |
| dc.description.pages | 1 online resource : | |
| dc.collection | Электронно-библиотечные системы | |
| dc.source.id | EN05CEBSCO05C975 |
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