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Data Science for Marketing Analytics
DataScienceforMarketingAnalyticscoverseverystageofdataanalytics,fromworkingwitharawdatasettosegmentingapopulationandmodelingdifferentpartsofthepopulationbasedonthesegments.ThebookstartsbyteachingyouhowtousePythonlibraries,suchaspandasandMatplotlib,toreaddatafromPython,manipulateit,andcreateplots,usingbothcategoricalandcontinuousvariables.Then,you'lllearnhowtosegmentapopulationintogroupsandusedifferentclusteringtechniquestoevaluatecustomersegmentation.Asyoumakeyourwaythroughthechapters,you'llexplorewaystoevaluateandselectthebestsegmentationapproach,andgoontocreatealinearregressionmodeloncustomervaluedatatopredictlifetimevalue.Intheconcludingchapters,you'llgainanunderstandingofregressiontechniquesandtoolsforevaluatingregressionmodels,andexplorewaystopredictcustomerchoiceusingclassificationalgorithms.Finally,you'llapplythesetechniquestocreateachurnmodelformodelingcustomerproductchoices.Bytheendofthisbook,youwillbeabletobuildyourownmarketingreportingandinteractivedashboardsolutions.
目錄(71章)
倒序
- 封面
- 版權頁
- Preface
- About the Book
- Chapter 1 Data Preparation and Cleaning
- Introduction
- Data Models and Structured Data
- pandas
- Data Manipulation
- Summary
- Chapter 2 Data Exploration and Visualization
- Introduction
- Identifying the Right Attributes
- Generating Targeted Insights
- Visualizing Data
- Summary
- Chapter 3 Unsupervised Learning: Customer Segmentation
- Introduction
- Customer Segmentation Methods
- Similarity and Data Standardization
- k-means Clustering
- Summary
- Chapter 4 Choosing the Best Segmentation Approach
- Introduction
- Choosing the Number of Clusters
- Different Methods of Clustering
- Evaluating Clustering
- Summary
- Chapter 5 Predicting Customer Revenue Using Linear Regression
- Introduction
- Understanding Regression
- Feature Engineering for Regression
- Performing and Interpreting Linear Regression
- Summary
- Chapter 6 Other Regression Techniques and Tools for Evaluation
- Introduction
- Evaluating the Accuracy of a Regression Model
- Using Regularization for Feature Selection
- Tree-Based Regression Models
- Summary
- Chapter 7 Supervised Learning: Predicting Customer Churn
- Introduction
- Classification Problems
- Understanding Logistic Regression
- Creating a Data Science Pipeline
- Modeling the Data
- Summary
- Chapter 8 Fine-Tuning Classification Algorithms
- Introduction
- Support Vector Machines
- Decision Trees
- Random Forest
- Preprocessing Data for Machine Learning Models
- Model Evaluation
- Performance Metrics
- Summary
- Chapter 9 Modeling Customer Choice
- Introduction
- Understanding Multiclass Classification
- Class Imbalanced Data
- Summary
- Appendix
- Chapter 1: Data Preparation and Cleaning
- Chapter 2: Data Exploration and Visualization
- Chapter 3: Unsupervised Learning: Customer Segmentation
- Chapter 4: Choosing the Best Segmentation Approach
- Chapter 5: Predicting Customer Revenue Using Linear Regression
- Chapter 6: Other Regression Techniques and Tools for Evaluation
- Chapter 7: Supervised Learning: Predicting Customer Churn
- Chapter 8: Fine-Tuning Classification Algorithms
- Chapter 9: Modeling Customer Choice 更新時間：2021-06-11 13:46:13
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