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.

Related Content you might be interested in

Current Title:

Small book image
Data Science for Marketing Analytics - Second Edition
Mirza Rahim Baig | Gururajan Govindan | Vishwesh Ravi Shrimali
Sep, 2021 | 636 pages
Small book image
The Data Science Workshop
Robert Thas John | Anthony So | Thomas V Joseph | Andrew Worsley | Samuel Asare
Jan, 2020 | 818 pages
Small book image
The Data Science Workshop
Robert Thas John | Anthony So | Thomas V Joseph | Andrew Worsley | Samuel Asare
Aug, 2020 | 824 pages
Small book image
Machine Learning with scikit-learn Quick Start Guide
Kevin Jolly
Oct, 2018 | 172 pages
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

Tree-Based Regression Models

In the preceding activity, you were able to identify the three most important features that could be used to predict customer spend. Now, imagine doing the same by removing each feature one at a time and finding out the RMSE. RFE aims to remove the redundant task of going over each feature by doing it internally, without forcing the user to put in the effort to do it manually.

So far, we have covered linear regression models. Now it's time to take it up a notch by discussing some tree-based regression models.

Linear models are not the only type of regression models. Another powerful technique is the use of regression trees. Regression trees are based on the idea of a decision tree. A decision tree is a bit like a flowchart, where, at each step, you ask whether a variable is greater than or less than some value. After flowing through several of these steps, you reach the end of the tree and receive an answer for what value the prediction should be...

Previous Section
End of Section 5
Next Section