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Data Science for Marketing Analytics

Data Science for Marketing Analytics

By : Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali
4.3 (203)
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Data Science for Marketing Analytics

Data Science for Marketing Analytics

4.3 (203)
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)
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Preface

Introduction

You are working in a marketing company that takes projects from various clients. Your team has been given a project where you have to predict the percentage of conversions for a Black Friday sale that the team is going to plan. The percentage of conversion as per the client refers to the number of people who actually buy products vis-à-vis the number of people who initially signed up for updates regarding the sale by visiting the website. Your first instinct is to go for a regression model for predicting the percentage conversion. However, you have millions of rows of data with hundreds of columns. In scenarios like these, it's very common to encounter issues of multi-collinearity where two or more features effectively convey the same information. This can then end up affecting the robustness of the model. This is where solutions such as Recursive Feature Selection (RFE) can be of help.

In the previous chapter, you learned how to prepare data for regression...

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