Preface

Social media and the Internet of Things have resulted in an avalanche of data. The data is powerful but not in its raw form; it needs to be processed and modelled and Python is one of the most robust tools we have out there to do so. It has an array of packages for predictive modelling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age.

This book is your guide to get started with Predictive Analytics using Python as the tool. You will learn how to process data and make predictive models out of them. A balanced weightage has been given to both the statistical and mathematical concepts and implementing them in Python using libraries, such as pandas, scikit-learn, and NumPy. Starting with understanding the basics of predictive modelling, you will see how to cleanse your data of impurities and make it ready for predictive modelling. You will also learn more about the best predictive modelling algorithms, such as linear regression, decision trees, and logistic regression. Finally, you will see what the best practices in predictive modelling are, as well as the different applications of predictive modelling in the modern world.