Data Science for Marketing Analytics - Second Edition

Spis treści książki

• Data Science for Marketing Analytics
• second edition
• Preface
  • About the Book
    • About the Authors
    • Who This Book Is For
    • About the Chapters
    • Conventions
    • Code Presentation
    • Minimum Hardware Requirements
    • Downloading the Code Bundle
    • Setting Up Your Environment
      • Installing Anaconda on Your System
      • Launching Jupyter Notebook
      • Installing the ds-marketing Virtual Environment
    • Running the Code Online Using Binder
    • Get in Touch
    • Please Leave a Review
• 1. Data Preparation and Cleaning
  • Introduction
  • Data Models and Structured Data
  • pandas
    • Importing and Exporting Data with pandas DataFrames
    • Viewing and Inspecting Data in DataFrames
    • Exercise 1.01: Loading Data Stored in a JSON File
    • Exercise 1.02: Loading Data from Multiple Sources
    • Structure of a pandas DataFrame and Series
  • Data Manipulation
    • Selecting and Filtering in pandas
    • Creating DataFrames in Python
    • Adding and Removing Attributes and Observations
    • Combining Data
    • Handling Missing Data
    • Exercise 1.03: Combining DataFrames and Handling Missing Values
    • Applying Functions and Operations on DataFrames
    • Grouping Data
    • Exercise 1.04: Applying Data Transformations
    • Activity 1.01: Addressing Data Spilling
  • Summary
• 2. Data Exploration and Visualization
  • Introduction
  • Identifying and Focusing on the Right Attributes
    • The groupby( ) Function
    • The unique( ) function
    • The value_counts( ) function
    • Exercise 2.01: Exploring the Attributes in Sales Data
  • Fine Tuning Generated Insights
    • Selecting and Renaming Attributes
    • Reshaping the Data
    • Exercise 2.02: Calculating Conversion Ratios for Website Ads.
    • Pivot Tables
  • Visualizing Data
    • Exercise 2.03: Visualizing Data With pandas
    • Visualization through Seaborn
    • Visualization with Matplotlib
    • Activity 2.01: Analyzing Advertisements
  • Summary
• 3. Unsupervised Learning and Customer Segmentation
  • Introduction
  • Segmentation
    • Exercise 3.01: Mall Customer Segmentation Understanding the Data
  • Approaches to Segmentation
    • Traditional Segmentation Methods
    • Exercise 3.02: Traditional Segmentation of Mall Customers
    • Unsupervised Learning (Clustering) for Customer Segmentation
  • Choosing Relevant Attributes (Segmentation Criteria)
    • Standardizing Data
    • Exercise 3.03: Standardizing Customer Data
    • Calculating Distance
    • Exercise 3.04: Calculating the Distance between Customers
  • K-Means Clustering
    • Exercise 3.05: K-Means Clustering on Mall Customers
    • Understanding and Describing the Clusters
    • Activity 3.01: Bank Customer Segmentation for Loan Campaign
    • Clustering with High-Dimensional Data
    • Exercise 3.06: Dealing with High-Dimensional Data
    • Activity 3.02: Bank Customer Segmentation with Multiple Features
  • Summary
• 4. Evaluating and Choosing the Best Segmentation Approach
  • Introduction
  • Choosing the Number of Clusters
    • Exercise 4.01: Data Staging and Visualization
    • Simple Visual Inspection to Choose the Optimal Number of Clusters
    • Exercise 4.02: Choosing the Number of Clusters Based on Visual Inspection
    • The Elbow Method with Sum of Squared Errors
    • Exercise 4.03: Determining the Number of Clusters Using the Elbow Method
    • Activity 4.01: Optimizing a Luxury Clothing Brands Marketing Campaign Using Clustering
  • More Clustering Techniques
    • Mean-Shift Clustering
    • Exercise 4.04: Mean-Shift Clustering on Mall Customers
    • Benefits and Drawbacks of the Mean-Shift Technique
    • k-modes and k-prototypes Clustering
    • Exercise 4.05: Clustering Data Using the k-prototypes Method
  • Evaluating Clustering
    • Silhouette Score
    • Exercise 4.06: Using Silhouette Score to Pick Optimal Number of Clusters
    • Train and Test Split
    • Exercise 4.07: Using a Train-Test Split to Evaluate Clustering Performance
    • Activity 4.02: Evaluating Clustering on Customer Data
    • The Role of Business in Cluster Evaluation
  • Summary
• 5. Predicting Customer Revenue Using Linear Regression
  • Introduction
  • Regression Problems
    • Exercise 5.01: Predicting Sales from Advertising Spend Using Linear Regression
  • Feature Engineering for Regression
    • Feature Creation
    • Data Cleaning
    • Exercise 5.02: Creating Features for Customer Revenue Prediction
    • Assessing Features Using Visualizations and Correlations
    • Exercise 5.03: Examining Relationships between Predictors and the Outcome
    • Activity 5.01: Examining the Relationship between Store Location and Revenue
  • Performing and Interpreting Linear Regression
    • Exercise 5.04: Building a Linear Model Predicting Customer Spend
    • Activity 5.02: Predicting Store Revenue Using Linear Regression
  • Summary
• 6. More Tools and Techniques for Evaluating Regression Models
  • Introduction
  • Evaluating the Accuracy of a Regression Model
    • Residuals and Errors
    • Mean Absolute Error
    • Root Mean Squared Error
    • Exercise 6.01: Evaluating Regression Models of Location Revenue Using the MAE and RMSE
    • Activity 6.01: Finding Important Variables for Predicting Responses to a Marketing Offer
  • Using Recursive Feature Selection for Feature Elimination
    • Exercise 6.02: Using RFE for Feature Selection
    • Activity 6.02: Using RFE to Choose Features for Predicting Customer Spend
  • Tree-Based Regression Models
    • Random Forests
    • Exercise 6.03: Using Tree-Based Regression Models to Capture Non-Linear Trends
    • Activity 6.03: Building the Best Regression Model for Customer Spend Based on Demographic Data
  • Summary
• 7. Supervised Learning: Predicting Customer Churn
  • Introduction
  • Classification Problems
  • Understanding Logistic Regression
    • Revisiting Linear Regression
  • Logistic Regression
    • Cost Function for Logistic Regression
    • Assumptions of Logistic Regression
    • Exercise 7.01: Comparing Predictions by Linear and Logistic Regression on the Shill Bidding Dataset
  • Creating a Data Science Pipeline
  • Churn Prediction Case Study
    • Obtaining the Data
    • Exercise 7.02: Obtaining the Data
    • Scrubbing the Data
    • Exercise 7.03: Imputing Missing Values
    • Exercise 7.04: Renaming Columns and Changing the Data Type
    • Exploring the Data
    • Exercise 7.05: Obtaining the Statistical Overview and Correlation Plot
    • Visualizing the Data
    • Exercise 7.06: Performing Exploratory Data Analysis (EDA)
    • Activity 7.01: Performing the OSE technique from OSEMN
  • Modeling the Data
    • Feature Selection
    • Exercise 7.07: Performing Feature Selection
    • Model Building
    • Exercise 7.08: Building a Logistic Regression Model
    • Interpreting the Data
    • Activity 7.02: Performing the MN technique from OSEMN
  • Summary
• 8. Fine-Tuning Classification Algorithms
  • Introduction
  • Support Vector Machines
    • Intuition behind Maximum Margin
    • Linearly Inseparable Cases
    • Linearly Inseparable Cases Using the Kernel
    • Exercise 8.01: Training an SVM Algorithm Over a Dataset
  • Decision Trees
    • Exercise 8.02: Implementing a Decision Tree Algorithm over a Dataset
    • Important Terminology for Decision Trees
    • Decision Tree Algorithm Formulation
  • Random Forest
    • Exercise 8.03: Implementing a Random Forest Model over a Dataset
    • Classical Algorithms Accuracy Compared
    • Activity 8.01: Implementing Different Classification Algorithms
  • Preprocessing Data for Machine Learning Models
    • Standardization
    • Exercise 8.04: Standardizing Data
    • Scaling
    • Exercise 8.05: Scaling Data After Feature Selection
    • Normalization
    • Exercise 8.06: Performing Normalization on Data
  • Model Evaluation
    • Exercise 8.07: Stratified K-fold
    • Fine-Tuning of the Model
    • Exercise 8.08: Fine-Tuning a Model
    • Activity 8.02: Tuning and Optimizing the Model
  • Performance Metrics
    • Precision
    • Recall
    • F1 Score
    • Exercise 8.09: Evaluating the Performance Metrics for a Model
    • ROC Curve
    • Exercise 8.10: Plotting the ROC Curve
    • Activity 8.03: Comparison of the Models
  • Summary
• 9. Multiclass Classification Algorithms
  • Introduction
  • Understanding Multiclass Classification
  • Classifiers in Multiclass Classification
    • Exercise 9.01: Implementing a Multiclass Classification Algorithm on a Dataset
  • Performance Metrics
    • Exercise 9.02: Evaluating Performance Using Multiclass Performance Metrics
    • Activity 9.01: Performing Multiclass Classification and Evaluating Performance
  • Class-Imbalanced Data
    • Exercise 9.03: Performing Classification on Imbalanced Data
    • Dealing with Class-Imbalanced Data
    • Exercise 9.04: Fixing the Imbalance of a Dataset Using SMOTE
    • Activity 9.02: Dealing with Imbalanced Data Using scikit-learn
  • Summary
• Appendix
  • 1. Data Preparation and Cleaning
    • Activity 1.01: Addressing Data Spilling
  • 2. Data Exploration and Visualization
    • Activity 2.01: Analyzing Advertisements
  • 3. Unsupervised Learning and Customer Segmentation
    • Activity 3.01: Bank Customer Segmentation for Loan Campaign
    • Activity 3.02: Bank Customer Segmentation with Multiple Features
  • 4. Evaluating and Choosing the Best Segmentation Approach
    • Activity 4.01: Optimizing a Luxury Clothing Brand's Marketing Campaign Using Clustering
    • Activity 4.02: Evaluating Clustering on Customer Data
  • 5. Predicting Customer Revenue Using Linear Regression
    • Activity 5.01: Examining the Relationship between Store Location and Revenue
    • Activity 5.02: Predicting Store Revenue Using Linear Regression
  • 6. More Tools and Techniques for Evaluating Regression Models
    • Activity 6.01: Finding Important Variables for Predicting Responses to a Marketing Offer
    • Activity 6.02: Using RFE to Choose Features for Predicting Customer Spend
    • Activity 6.03: Building the Best Regression Model for Customer Spend Based on Demographic Data
  • 7. Supervised Learning: Predicting Customer Churn
    • Activity 7.01: Performing the OSE technique from OSEMN
    • Activity 7.02: Performing the MN technique from OSEMN
  • 8. Fine-Tuning Classification Algorithms
    • Activity 8.01: Implementing Different Classification Algorithms
    • Activity 8.02: Tuning and Optimizing the Model
    • Activity 8.03: Comparison of the Models
  • 9. Multiclass Classification Algorithms
    • Activity 9.01: Performing Multiclass Classification and Evaluating Performance
    • Activity 9.02: Dealing with Imbalanced Data Using scikit-learn
    • Hey!

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