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)

Naive Bayes classifiers

Naive Bayes classifiers is a family of classifiers based on the famous Bayes theorem. This section will take us a bit off topic (which is why I am including it at the end), but it is worth having this family of models in your predictive analytics toolbox. We will briefly review some important probability concepts and will discuss the intuition behind this classifier; then we will use it in our problem.

Conditional probability

This section is by no means a mathematically rigorous discussion of probability concepts: we will focus on the intuition and will make some calculations to make the concepts more concrete.

In this section, we will use the same dataset we have been using and assume that we can accurately...