Data Science for Marketing Analytics: A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition

Cover
FM
Copyright
Table of Contents
Preface
Chapter 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
Chapter 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
Chapter 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
Chapter 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 Brand's 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Index