Book description
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language
Key Features
- Use data analytics and machine learning in a sales and marketing context
- Gain insights from data to make better business decisions
- Build your experience and confidence with realistic hands-on practice
Book Description
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.
What you will learn
- Load, clean, and explore sales and marketing data using pandas
- Form and test hypotheses using real data sets and analytics tools
- Visualize patterns in customer behavior using Matplotlib
- Use advanced machine learning models like random forest and SVM
- Use various unsupervised learning algorithms for customer segmentation
- Use supervised learning techniques for sales prediction
- Evaluate and compare different models to get the best outcomes
- Optimize models with hyperparameter tuning and SMOTE
Who this book is for
This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path.
Basic prior knowledge of Python and experience working with data will help you access this book more easily.
Table of contents
- Data Science for Marketing Analytics
- second edition
- Preface
-
1. Data Preparation and Cleaning
- Introduction
- Data Models and Structured Data
- pandas
-
Data Manipulation
- Selecting and Filtering in pandas
- Creating DataFrames in Python
- Adding and Removing Attributes and Observations
- Combining Data
- Handling Missing Data
- Exercise 1.03: Combining DataFrames and Handling Missing Values
- Applying Functions and Operations on DataFrames
- Grouping Data
- Exercise 1.04: Applying Data Transformations
- Activity 1.01: Addressing Data Spilling
- Summary
- 2. Data Exploration and Visualization
-
3. Unsupervised Learning and Customer Segmentation
- Introduction
- Segmentation
- Approaches to Segmentation
- Choosing Relevant Attributes (Segmentation Criteria)
-
K-Means Clustering
- Exercise 3.05: K-Means Clustering on Mall Customers
- Understanding and Describing the Clusters
- Activity 3.01: Bank Customer Segmentation for Loan Campaign
- Clustering with High-Dimensional Data
- Exercise 3.06: Dealing with High-Dimensional Data
- Activity 3.02: Bank Customer Segmentation with Multiple Features
- Summary
-
4. Evaluating and Choosing the Best Segmentation Approach
- Introduction
-
Choosing the Number of Clusters
- Exercise 4.01: Data Staging and Visualization
- Simple Visual Inspection to Choose the Optimal Number of Clusters
- Exercise 4.02: Choosing the Number of Clusters Based on Visual Inspection
- The Elbow Method with Sum of Squared Errors
- Exercise 4.03: Determining the Number of Clusters Using the Elbow Method
- Activity 4.01: Optimizing a Luxury Clothing Brand's Marketing Campaign Using Clustering
- More Clustering Techniques
- Evaluating Clustering
- Summary
-
5. Predicting Customer Revenue Using Linear Regression
- Introduction
- Regression Problems
-
Feature Engineering for Regression
- Feature Creation
- Data Cleaning
- Exercise 5.02: Creating Features for Customer Revenue Prediction
- Assessing Features Using Visualizations and Correlations
- Exercise 5.03: Examining Relationships between Predictors and the Outcome
- Activity 5.01: Examining the Relationship between Store Location and Revenue
- Performing and Interpreting Linear Regression
- Summary
- 6. More Tools and Techniques for Evaluating Regression Models
-
7. Supervised Learning: Predicting Customer Churn
- Introduction
- Classification Problems
- Understanding Logistic Regression
- Logistic Regression
- Creating a Data Science Pipeline
-
Churn Prediction Case Study
- Obtaining the Data
- Exercise 7.02: Obtaining the Data
- Scrubbing the Data
- Exercise 7.03: Imputing Missing Values
- Exercise 7.04: Renaming Columns and Changing the Data Type
- Exploring the Data
- Exercise 7.05: Obtaining the Statistical Overview and Correlation Plot
- Visualizing the Data
- Exercise 7.06: Performing Exploratory Data Analysis (EDA)
- Activity 7.01: Performing the OSE technique from OSEMN
- Modeling the Data
- Summary
- 8. Fine-Tuning Classification Algorithms
- 9. Multiclass Classification Algorithms
-
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
Product information
- Title: Data Science for Marketing Analytics
- Author(s):
- Release date: September 2021
- Publisher(s): Packt Publishing
- ISBN: 9781800560475
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