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Mastering Predictive Analytics with scikit-learn and TensorFlow

By : Alvaro Fuentes

Buy this Book

Mastering Predictive Analytics with scikit-learn and TensorFlow

By: Alvaro Fuentes

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Ensemble Methods for Regression and Classification

Ensemble methods and their working

Ensemble methods for regression

Using ensemble methods for classification

Summary

Cross-validation and Parameter Tuning

Holdout cross-validation

K-fold cross-validation

Comparing models with k-fold cross-validation

Introduction to hyperparameter tuning

Summary

Working with Features

Feature selection methods

Dimensionality reduction and PCA

Feature engineering

Improving models with feature engineering

Reducible and irreducible error

Summary

Introduction to Artificial Neural Networks and TensorFlow

Introduction to ANNs

Elements of a deep neural network model

Introduction to TensorFlow

Core concepts in TensorFlow

Summary

Predictive Analytics with TensorFlow and Deep Neural Networks

Predictions with TensorFlow

Regression with Deep Neural Networks (DNN)

Classification with DNNs

Summary

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Feature selection methods

Feature selection methods are used for selecting features that are likely to help with predictions. The following are the three methods for feature selection:

Removing dummy features with low variance
Identifying important features statistically
Recursive feature elimination

When building predictive analytics models, some features won't be related to the target and this will prove to be less helpful in prediction. Now, the problem is that including irrelevant features in the model can introduce noise and add bias to the model. So, feature selection techniques are a set of techniques used to select the most relevant and useful features that will help either with prediction or with understanding our model.