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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

K-Means Clustering

K-means clustering is a very common unsupervised learning technique with a wide range of applications. It is powerful because it is conceptually relatively simple, scales to very large datasets, and tends to work well in practice. In this section, you will learn the conceptual foundations of k-means clustering, how to apply k-means clustering to data, and how to deal with high-dimensional data (that is, data with many different variables) in the context of clustering.

K-means clustering is an algorithm that tries to find the best way of grouping data points into k different groups, where k is a parameter given to the algorithm. For now, we will choose k arbitrarily. We will revisit how to choose k in practice in the next chapter. The algorithm then works iteratively to try to find the best grouping. There are two steps to this algorithm:

  1. The algorithm begins by randomly selecting k points in space to be the centroids of the clusters. Each data point is then...

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