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.
Table of Contents (9 chapters)
Chapter 8
Fine-Tuning Classification Algorithms
Content Locked
Section 3
Decision Trees and Random Forests
There Decision trees are mostly used for classification tasks. They are a non-parametric form of supervised learning method. Decision trees work on the concept of finding out the target variable by learning simple decision rules from data. They can be used for both classification and regression tasks.