Book Image

Mastering Predictive Analytics with scikit-learn and TensorFlow

By : Alvaro Fuentes
Book Image

Mastering Predictive Analytics with scikit-learn and TensorFlow

By: Alvaro Fuentes

Overview of this book

Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems. This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics. By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.
Table of Contents (7 chapters)

Working with Features

In this chapter, we are going to take a close look at how features play an important role in the feature engineering technique. We'll learn some techniques that will allow us to improve our predictive analytics models in two ways: in terms of the performance metrics of our models and to understand the relationship between the features and the target variables that we are trying to predict.

In this chapter, we are going to cover the following topics:

  • Feature selection methods
  • Dimensionality reduction and PCA
  • Creating new features
  • Improving models with feature engineering