Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Hands-On Predictive Analytics with Python
  • Toc
  • feedback
Hands-On Predictive Analytics with Python

Hands-On Predictive Analytics with Python

By : Alvaro Fuentes
4.4 (8)
close
Hands-On Predictive Analytics with Python

Hands-On Predictive Analytics with Python

4.4 (8)
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)
close

Predicting Categories with Machine Learning

In the previous chapter, we learned the basics of machine learning. In this chapter, we will build models that predict categories. This class of machine learning problems is known as classification tasks. Classification models are the ones that are the most useful in practice, and in this chapter we will talk about some of the most popular and foundational classification models.

We begin the chapter by providing an overview of the classification tasks and some of their applications. Then we bring back our credit card default dataset and start preparing it for modeling. After that, we introduce one of the most popular models for classification—logistic regression, which is similar in spirit to the multiple regression models we discussed in the previous chapter. The next model we present is classification trees. We present this...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete