Book Image

Data Science for Marketing Analytics - Second Edition

By : Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali
Book Image

Data Science for Marketing Analytics - Second Edition

By: Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali

Overview of this book

Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.

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Table of Contents (11 chapters)
Preface
About the Book
1
1. Data Preparation and Cleaning
1. Data Preparation and Cleaning
Introduction
Data Models and Structured Data
pandas
Data Manipulation
Summary
Free Chapter
2
2. Data Exploration and Visualization
2. Data Exploration and Visualization
Introduction
Identifying and Focusing on the Right Attributes
Fine Tuning Generated Insights
Visualizing Data
Summary
3
3. Unsupervised Learning and Customer Segmentation
3. Unsupervised Learning and Customer Segmentation
Introduction
Segmentation
Approaches to Segmentation
Choosing Relevant Attributes (Segmentation Criteria)
K-Means Clustering
Summary
4
4. Evaluating and Choosing the Best Segmentation Approach
4. Evaluating and Choosing the Best Segmentation Approach
Introduction
Choosing the Number of Clusters
More Clustering Techniques
Evaluating Clustering
Summary
5
5. Predicting Customer Revenue Using Linear Regression
5. Predicting Customer Revenue Using Linear Regression
Introduction
Regression Problems
Feature Engineering for Regression
Performing and Interpreting Linear Regression
Summary
6
6. More Tools and Techniques for Evaluating Regression Models
6. More Tools and Techniques for Evaluating Regression Models
Introduction
Evaluating the Accuracy of a Regression Model
Using Recursive Feature Selection for Feature Elimination
Tree-Based Regression Models
Summary
7
7. Supervised Learning: Predicting Customer Churn
7. Supervised Learning: Predicting Customer Churn
Introduction
Classification Problems
Understanding Logistic Regression
Logistic Regression
Creating a Data Science Pipeline
Churn Prediction Case Study
Modeling the Data
Summary
8
8. Fine-Tuning Classification Algorithms
8. Fine-Tuning Classification Algorithms
Introduction
Support Vector Machines
Decision Trees
Random Forest
Preprocessing Data for Machine Learning Models
Model Evaluation
Performance Metrics
Summary
9
9. Multiclass Classification Algorithms
9. Multiclass Classification Algorithms
Introduction
Understanding Multiclass Classification
Classifiers in Multiclass Classification
Performance Metrics
Class-Imbalanced Data
Summary
Appendix
1. Data Preparation and Cleaning
2. Data Exploration and Visualization
3. Unsupervised Learning and Customer Segmentation
4. Evaluating and Choosing the Best Segmentation Approach
5. Predicting Customer Revenue Using Linear Regression
6. More Tools and Techniques for Evaluating Regression Models
7. Supervised Learning: Predicting Customer Churn
8. Fine-Tuning Classification Algorithms
9. Multiclass Classification Algorithms

Logistic Regression

If a response variable has binary values, the assumptions of linear regression are not valid for the following reasons:

  • The relationship between the independent variable and the predictor variable is not linear.
  • The error terms are heteroscedastic. Recall that heteroscedastic means that the variance of the error terms is not the same throughout the range of x (input data).
  • The error terms are not normally distributed.

If we proceed, considering these violations, the results would be as follows:

  • The predicted probabilities could be greater than 1 or less than 0.
  • The magnitude of the effects of independent variables may be underestimated.

With logistic regression, we are interested in modeling the mean of the response variable, p, in terms of an explanatory variable, x, as a probabilistic model in terms of the odds ratio. The odds ratio is the ratio of two probabilities – the probability of the event occurring, and the probability...

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