Book Image

Data Science for Marketing Analytics

By : Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar
Book Image

Data Science for Marketing Analytics

By: Tommy Blanchard, Debasish Behera, Pranshu Bhatnagar

Overview of this book

Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions.
Table of Contents (12 chapters)
Data Science for Marketing Analytics
Preface

Preprocessing Data for Machine Learning Models


Preprocessing data before applying any machine learning model can improve the accuracy of the model to a large extent. Therefore, it is important to preprocess data before applying a machine learning algorithm. Preprocessing data consists of the following methods: Standardization, Scaling, and Normalization

Standardization

Most machine learning algorithms assume that all features are centered at zero and have variance in the same order. In the case of linear models such as logistic and linear regression, some of the parameters used in the objective function assume that all the features are centered around zero and have unit variance. If the values of a feature are much higher than some of the other features, then that feature might dominate the objective function and the estimator may not be able to learn from other features. In such cases, standardization can be used to rescale features such that they have a mean of 0 and variance of 1. The following...