Book Image

Data Science for Marketing Analytics

By : Candas Bilgin
Book Image

Data Science for Marketing Analytics

By: Candas Bilgin

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

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Data Science for Marketing Analytics
Candas Bilgin
Aug, 2019 | 4 hours 35 minutes
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Table of Contents (9 chapters)
Free Chapter
1
Data Preparation and Cleaning
Course Overview
Lesson Overview
Data Models and Structured Data
Pandas
Data Manipulation
Summary
2
Data Exploration and Visualization
Lesson Overview
Identifying the Right Attributes
Generating Targeted Insights
Visualizing Data
Summary
3
Unsupervised Learning: Customer Segmentation
Lesson Overview
Customer Segmentation Methods
Similarity and Data Standardization
k-means Clustering
Summary
4
Choosing the Best Segmentation Approach
Lesson Overview
Choosing the Number of Clusters
Different Methods of Clustering
Evaluation Clustering
Summary
5
Predicting Customer Revenue Using Linear Regression
Lesson Overview
Feature Engineering for Regression
Performing and Interpreting Linear Regression
Summary
6
Other Regression Techniques and Tools for Evaluation
Lesson Overview
Evaluating the Accuracy of a Regression Model
Using Regularization for Feature Selection
Tree Based Regression Models
Summary
7
Supervised Learning - Predicting Customer Churn
Lesson Overview
Understanding Logistic Regression
Creating a Data Science Pipeline
Modeling the Data
Summary
8
Fine-Tuning Classification Algorithms
Lesson Overview
Support Vector Machines
Decision Trees and Random Forests
Pre-processing Data and Model Evaluation
Performance Metrics
Summary
9
Modeling Customer Choice
Lesson Overview
Understanding Multiclass Classification
Class Imbalanced Data
Summary
Chapter 5
Predicting Customer Revenue Using Linear Regression
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Section 2
Feature Engineering for Regression
Feature engineering is the process of taking data and transforming it for use in predictions. The idea is to create features that capture aspects of what's important to the outcome of interest. This process requires both data expertise and domain knowledge—you need to know what can be done with the data that you have, as well as knowledge of what might be predictive of the outcome, you're interested in. We will first look at how to transform data to create features, and then how to clean the data of the resulting features to ensure models are trained on high-quality data.
Previous Section
End of Section 2
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