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| 001 133 |
| 003 ULIBM |
| 005 20200318065709. |
| 006 m d |
| 007 cr unu||||||| |
| 008 190509s2019 enka o 000 0 eng |
| 015 ^aGBB995004^2bn |
| 016 7 ^a019365457^2U |
| 020 ^z978178934634 |
| 020 ^a9781789348828^q(electronic bk. |
| 020 ^a178934882 |
| 035 ^a(OCoLC)1100643331^z(OCoLC)1091659201^z(OCoLC)109652315 |
| 035 ^a2094760^b(N$T |
| 040 ^aUMI^beng^erda^epn^cUMI^dTEFOD^dEBLCP^dUKAHL^dMERUC^dUKMGB^dOCLCF^dYDX^dOCLCQ^dN$ |
| 049 ^aMAI |
| 050 4 ^aHF5415.12 |
| 082 04 ^a658.834^22 |
| 100 1 ^aHwang, Yoon Hyup,^eauthor |
| 245 10 ^aHands-on data science for marketing :^bimprove your marketing strategies with machine learning using Python and R /^cYoon Hyup Hwang |
| 300 ^a1 online resource :^billustration |
| 505 8 ^aDecision 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 tim |
| 505 8 ^aCombining 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 tree |
| 505 8 ^aChapter 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 variable |
| 505 0 ^aCover; 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 |
| 520 ^aThis 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 |
| 520 ^aSection 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; Summar |
| 590 ^aAdded to collection customer.56279. |
| 650 7 ^aR (Computer program language)^2fast^0(OCoLC)fst0108620 |
| 650 7 ^aPython (Computer program language)^2fast^0(OCoLC)fst0108473 |
| 650 7 ^aMarketing research.^2fast^0(OCoLC)fst0101028 |
| 650 7 ^aMarketing^xData processing.^2fast^0(OCoLC)fst0101018 |
| 650 7 ^aMachine learning.^2fast^0(OCoLC)fst0100479 |
| 650 0 ^aR (Computer program language |
| 650 0 ^aPython (Computer program language |
| 650 0 ^aMarketing research |
| 650 0 ^aMachine learning |
| 650 0 ^aMarketing^xData processing |
| 655 4 ^aElectronic books |
| 856 0^ 3EBSCOhost^uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2094760 |
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