Book Image

Data Science for Marketing Analytics - Second Edition

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

Data Science for Marketing Analytics - Second Edition

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

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.
Table of Contents (11 chapters)
Preface

8. Fine-Tuning Classification Algorithms

Overview

This chapter will help you optimize predictive analytics using classification algorithms such as support vector machines, decision trees, and random forests, which are some of the most common classification algorithms from the scikit-learn machine learning library. Moreover, you will learn how to implement tree-based classification models, which you have used previously for regression. Next, you will learn how to choose appropriate performance metrics for evaluating the performance of a classification model. Finally, you will put all these skills to use in solving a customer churn prediction problem where you will optimize and evaluate the best classification algorithm for predicting whether a given customer will churn or not.