Book Image

Data Science for Marketing Analytics

By : Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar
Book Image

Data Science for Marketing Analytics

By: Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Overview of this book

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.

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Table of Contents (12 chapters)
Data Science for Marketing Analytics
Data Science for Marketing Analytics
Preface
Preface
Free Chapter
1
Data Preparation and Cleaning
Data Preparation and Cleaning
Introduction
Data Models and Structured Data
pandas
Data Manipulation
Summary
2
Data Exploration and Visualization
Data Exploration and Visualization
Introduction
Identifying the Right Attributes
Generating Targeted Insights
Visualizing Data
Summary
3
Unsupervised Learning: Customer Segmentation
Unsupervised Learning: Customer Segmentation
Introduction
Customer Segmentation Methods
Similarity and Data Standardization
k-means Clustering
Summary
4
Choosing the Best Segmentation Approach
Choosing the Best Segmentation Approach
Introduction
Choosing the Number of Clusters
Different Methods of Clustering
Evaluating Clustering
Summary
5
Predicting Customer Revenue Using Linear Regression
Predicting Customer Revenue Using Linear Regression
Introduction
Understanding Regression
Feature Engineering for Regression
Performing and Interpreting Linear Regression
Summary
6
Other Regression Techniques and Tools for Evaluation
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
7
Supervised Learning: Predicting Customer Churn
Supervised Learning: Predicting Customer Churn
Introduction
Classification Problems
Understanding Logistic Regression
Creating a Data Science Pipeline
Modeling the Data
Summary
8
Fine-Tuning Classification Algorithms
Fine-Tuning Classification Algorithms
Introduction
Support Vector Machines
Decision Trees
Random Forest
Preprocessing Data for Machine Learning Models
Model Evaluation
Performance Metrics
Summary
9
Modeling Customer Choice
Modeling Customer Choice
Introduction
Understanding Multiclass Classification
Class Imbalanced Data
Summary
Appendix
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

Understanding Logistic Regression

Logistic regression is one of the most widely used classification methods, and it works well when data is linearly separable. The objective of logistic regression is to squash the output of linear regression to classes 0 and 1.

Revisiting Linear Regression

In the case of linear regression, our function would be as follows:

Figure 7.2: Equation of linear regression

Here, x refers to the input data, y is the target variable, and θ0 and θ1 are parameters that are learned from the training data.

Also, the cost function in case of linear regression, which is to be minimized is as follows:

Figure 7.3: Linear regression cost function

This works well for continuous data, but the problem arises when we have a target variable that is categorical, such as, 0 or 1. When we try to use linear regression to predict the target variable, we can get a value anywhere between −∞ to +∞, which is not what we need.

Logistic Regression

If a response variable has binary values, the assumptions...

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