Book Image

Hands-On Predictive Analytics with Python

By : Alvaro Fuentes
Book Image

Hands-On Predictive Analytics with Python

By: Alvaro Fuentes

Overview of this book

Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.
Table of Contents (11 chapters)

Summary

In this chapter, we learned some of the most basic and useful techniques for performing EDA. We provided many examples of how to produce visualizations and numerical calculations, and how to interpret them.

We learned about the main techniques for univariate and bivariate analysis, including histograms, bar plots, scatter plots, and boxplots. We also provided some examples of complex multivariate visualizations with Seaborn.

Please bear in mind that the reason for applying all these techniques is to understand the dataset, which will give us a better picture of the relationship between the business problem and the data. It also provides us with valuable information for the next stage of the process: predictive modeling, that will be the subject of our next chapter