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Data Science for Marketing Analytics

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

4.3 (203)

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Data Science for Marketing Analytics

4.3 (203)

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

Buy this Book

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.

Preface

About the Book

1. Data Preparation and Cleaning

Introduction

Data Models and Structured Data

pandas

Data Manipulation

Summary

Free Chapter

2. Data Exploration and Visualization

Introduction

Identifying and Focusing on the Right Attributes

Fine Tuning Generated Insights

Visualizing Data

Summary

3. Unsupervised Learning and Customer Segmentation

Introduction

Segmentation

Approaches to Segmentation

Choosing Relevant Attributes (Segmentation Criteria)

K-Means Clustering

Summary

4. Evaluating and Choosing the Best Segmentation Approach

Introduction

Choosing the Number of Clusters

More Clustering Techniques

Evaluating Clustering

Summary

5. Predicting Customer Revenue Using Linear Regression

Introduction

Regression Problems

Feature Engineering for Regression

Performing and Interpreting Linear Regression

Summary

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. 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. Fine-Tuning Classification Algorithms

Introduction

Support Vector Machines

Decision Trees

Random Forest

Preprocessing Data for Machine Learning Models

Model Evaluation

Performance Metrics

Summary

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

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4.3 (203)

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Data Science for Marketing Analytics

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

Data Science for Marketing Analytics

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

Overview of this book

Introduction

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