舉報 
會員
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
推薦閱讀
- Practical Data Analysis
- Machine Learning for Cybersecurity Cookbook
- 一本書玩轉數據分析（雙色圖解版）
- Getting Started with Containerization
- Learning Apache Cassandra（Second Edition）
- MicroPython Projects
- 最后一個人類
- TensorFlow Reinforcement Learning Quick Start Guide
- 人工智能：語言智能處理
- 在實戰中成長：C++開發之路
- MongoDB 4 Quick Start Guide
- PostgreSQL 10 High Performance
- 商務智能
- VMware vSphere 6.5 Cookbook（Third Edition）
- Kibana 7 Quick Start Guide
- 智能機器人：從“深藍”到AlphaGo
- 新手學Photoshop CS6數碼照片處理
- CAD/CAE/CAM技術
- Getting Started with Flurry Analytics
- 數據倉庫結構設計與實施
- Force.com Enterprise Architecture（Second Edition）
- Google Cloud Platform Administration
- Photoshop CS5摳圖與調色圣經
- Pentaho for Big Data Analytics
- Mastering BeagleBone Robotics
- 二維動畫制作（Flash CS3）
- Gitolite Essentials
- 學會提問，駕馭AI：提示詞從入門到精通
- Build Supercomputers with Raspberry Pi 3
- Hands-On Big Data Modeling
