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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

Understanding Logistic Regression

Logistic regression is one of the most widely used classification methods, and it works well when data is linearly separable. The objective of logistic regression is to squash the output of linear regression to classes 0 and 1. Let's first understand the "regression" part of the name and why, despite its name, logistic regression is a classification model.

Revisiting Linear Regression

In the case of linear regression, our mapping function would be as follows:

Figure 7.2: Equation of linear regression

Here, x refers to the input data and θ0 and θ1 are parameters that are learned from the training data.

Also, the cost function in the case of linear regression, which is to be minimized, is the root mean squared error (RMSE), which we discussed in the previous chapter.

This works well for continuous data, but the problem arises when we have a categorical target variable, such as 0 or 1. When we try to use linear regression...

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