1. Data Preparation and Cleaning | Data Science for Marketing Analytics [Instructor Edition]

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Table Of Contents

Data Science for Marketing Analytics [Instructor Edition]

By : Pranshu Bhatnagar, Tommy Blanchard, Debasish Behera

4.3 (203)

Buy this Book

Data Science for Marketing Analytics [Instructor Edition]

4.3 (203)

By: Pranshu Bhatnagar, Tommy Blanchard, Debasish Behera

Buy this Book

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 course 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 lessons, 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 lessons, 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 course, you will be able to build your own marketing reporting and interactive dashboard solutions.

Preface

About the Book

Free Chapter

1. Data Preparation and Cleaning

Introduction

Data Models and Structured Data

pandas

Data Manipulation

Summary

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

Data Science for Marketing Analytics [Instructor Edition]

By : Pranshu Bhatnagar, Tommy Blanchard, Debasish Behera

Data Science for Marketing Analytics [Instructor Edition]

By: Pranshu Bhatnagar, Tommy Blanchard, Debasish Behera

Overview of this book

Introduction

Confirmation

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