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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

Modeling the Data

Data modeling, as the name suggests, refers to the process of creating a model that can define the data and can be used to draw conclusions and predictions for new data points. Modeling the data not only includes building your machine learning model but also selecting important features/columns that will go into your model. This section will be divided into two parts: Feature Selection and Model Building. For example, when trying to solve the churn prediction problem, which has a large number of features, feature selection can help in selecting the most relevant features. Those relevant features can then be used to train a model (in the model-building stage) to perform churn prediction.

Feature Selection

Before building our first machine learning model, we have to do some feature selection. Consider a scenario of churn prediction where you have a large number of columns and you want to perform prediction. Not all the features will have an impact on your prediction...

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