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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

Performing and Interpreting Linear Regression

In Exercise 5.01, Predicting Sales from Advertising Spend Using Linear Regression, we implemented and saw the output of a linear regression model without discussing the inner workings. Let us understand the technique of linear regression better now. Linear regression is a type of regression model that predicts the outcome using linear relationships between predictors and the outcome. Linear regression models can be thought of as a line running through the feature space that minimizes the distance between the line and the data points.

The model that a linear regression learns is the equation of this line. It is an equation that expresses the dependent variable as a linear function of the independent variables. This is best visualized when there is a single predictor (see Figure 5.28). In such a case, you can draw a line that best fits the data on a scatter plot between the two variables.

Figure 5.28: A visualization of a linear regression...

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