Читать книгу Sharing Economy and Big Data Analytics - Soraya Sedkaoui - Страница 18

This last part presents a range of advanced data analysis algorithms, including regression, classification and cluster analysis. It provides a set of techniques to anyone who wants to generate value from data based on the data analysis process. Using practical examples, we introduce fundamental principles of the Big Data analytics process.

Chapter 9, “Linear Regression”, discusses the essential techniques for modelling the linear relationships between explanatory variables and the outcome variable in order to make predictions on a continuous scale. After introducing the linear regression model, there is also a discussion of logistic regression, and Ridge and Lasso regressions. Based on an Airbnb database’s example, this chapter provides a practical guide to explore the data and build a predictive model more efficiently, using Python.

Chapter 10, “Classification Algorithms” , revisits the origin of supervised learning and introduces the most common classification algorithms. This chapter is an introduction to the fundamental principles of different techniques that help to better classify data. Using the same Airbnb database, this chapter will illustrate the most significant features in the model definition.

Chapter 11, “Cluster Analysis”, moves the focus to another sub-domain of Machine Learning: unsupervised learning. This chapter will examine clustering algorithms, mainly k-means and hierarchical classification. To better understand the principles discussed throughout this chapter, a practical example will be introduced in order to show how to find groups of objects that share a certain degree of similarity.

In conclusion, our ambition is to make this book one of the first basic references of the sharing economy practices boosted by Big Data. We hope that this book will open up new horizons for you, by presenting new approaches that you may not have known before. We also hope that this will help you sharpen your curiosity and stimulate your desire to learn more about it.

1 1 Jean-Louis-Auguste Commerson.