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    Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python
    Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python
    Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python
    Ebook230 pages1 hour

    Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python

    By Alan Fontaine

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    About this ebook

    Learn advanced techniques to improve the performance and quality of your predictive models




    Key Features



    • Use ensemble methods to improve the performance of predictive analytics models


    • Implement feature selection, dimensionality reduction, and cross-validation techniques


    • Develop neural network models and master the basics of deep learning



    Book Description



    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.




    What you will learn



    • Use ensemble algorithms to obtain accurate predictions


    • Apply dimensionality reduction techniques to combine features and build better models


    • Choose the optimal hyperparameters using cross-validation


    • Implement different techniques to solve current challenges in the predictive analytics domain


    • Understand various elements of deep neural network (DNN) models


    • Implement neural networks to solve both classification and regression problems



    Who this book is for



    Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

    LanguageEnglish
    PublisherPackt Publishing
    Release dateSep 29, 2018
    ISBN9781789612240
    Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python

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      Book preview

      Mastering Predictive Analytics with scikit-learn and TensorFlow - Alan Fontaine

      Mastering Predictive Analytics with scikit-learn and TensorFlow

      Mastering Predictive Analytics with scikit-learn and TensorFlow

      Implement machine learning techniques to build advanced predictive models using Python

      Alan Fontaine

      BIRMINGHAM - MUMBAI

      Mastering Predictive Analytics with scikit-learn and TensorFlow

      Copyright © 2018 Packt Publishing

      All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

      Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

      Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

      Commissioning Editor: Sunith Shetty

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      First published: September 2018

      Production reference: 1280918

      Published by Packt Publishing Ltd.

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      ISBN 978-1-78961-774-0

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      Contributor

      About the author

      Alan Fontaine is a data scientist with more than 12 years of experience in analytical roles. He has been a consultant for many projects in fields such as: business, education, medicine, mass media, among others. He is a big Python fan and has been using it routinely for five years for analyzing data, building models, producing reports, making predictions, and building interactive applications that transform data into intelligence.

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      Table of Contents

      Title Page

      Copyright and Credits

      Mastering Predictive Analytics with scikit-learn and TensorFlow

      Packt Upsell

      Why subscribe?

      Packt.com

      Contributor

      About the author

      Packt is searching for authors like you

      Preface

      Who this book is for

      What this book covers

      To get the most out of this book

      Download the example code files

      Download the color images

      Conventions used

      Get in touch

      Reviews

      Ensemble Methods for Regression and Classification

      Ensemble methods and their working

      Bootstrap sampling

      Bagging

      Random forests

      Boosting

      Ensemble methods for regression

      The diamond dataset

      Training different regression models

      KNN model

      Bagging model

      Random forests model

      Boosting model

      Using ensemble methods for classification

      Predicting a credit card dataset 

      Training different regression models

      Logistic regression model

      Bagging model

      Random forest model

      Boosting model

      Summary

      Cross-validation and Parameter Tuning

      Holdout cross-validation

      K-fold cross-validation

      Implementing k-fold cross-validation

      Comparing models with k-fold cross-validation

      Introduction to hyperparameter tuning

      Exhaustive grid search

      Hyperparameter tuning in scikit-learn

      Comparing tuned and untuned models

      Summary

      Working with Features

      Feature selection methods 

      Removing dummy features with low variance

      Identifying important features statistically

      Recursive feature elimination

      Dimensionality reduction and PCA

      Feature engineering

      Creating new features

      Improving models with feature engineering

      Training your model

      Reducible and irreducible error

      Summary

      Introduction to Artificial Neural Networks and TensorFlow

      Introduction to ANNs

      Perceptrons

      Multilayer perceptron

      Elements of a deep neural network model

      Deep learning

      Elements of an MLP model

      Introduction to TensorFlow

      TensorFlow installation

      Core concepts in TensorFlow

      Tensors

      Computational graph

      Summary

      Predictive Analytics with TensorFlow and Deep Neural Networks

      Predictions with TensorFlow

      Introduction to the MNIST dataset

      Building classification models using MNIST dataset

      Elements of the DNN model

      Building the DNN

      Reading the data

      Defining the architecture

      Placeholders for inputs and labels

      Building the neural network

      The loss function

      Defining optimizer and training operations

      Training strategy and valuation of accuracy of the classification

      Running the computational graph

      Regression with Deep Neural Networks (DNN)

      Elements of the DNN model

      Building the DNN

      Reading the data

      Objects for modeling

      Training strategy

      Input pipeline for the DNN

      Defining the architecture

      Placeholders for input values and labels

      Building the DNN

      The loss function

      Defining optimizer and training operations

      Running the computational graph

      Classification with DNNs

      Exponential linear unit activation function

      Classification with DNNs

      Elements of the DNN model

      Building the DNN

      Reading the data

      Producing the objects for modeling

      Training strategy

      Input pipeline for DNN

      Defining the architecture

      Placeholders for inputs and labels

      Building the neural network

      The loss function

      Evaluation nodes

      Optimizer and the training operation

      Run the computational graph

      Evaluating the model with a set threshold

      Summary

      Other Books You May Enjoy

      Leave a review - let other readers know what you think

      Preface

      Python is a programming language that provides various features in the field of data science. In this book, we will be touching upon two Python libraries, scikit-learn and TensorFlow. We will learn about the 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 studying ensemble methods and their features. We will look at how scikit-learn provides the right tools to choose hyperparameters for models. From there, we will get down to the nitty-gritty of predictive analytics and explore its various features and characteristics. We will be introduced to artificial neural networks, TensorFlow, and the core concepts used to build neural networks.

      In the final section, we will consider factors such as computational power, improved methods, and software enhancements for efficient predictive analytics. You will become well versed in using DNNs to solve common challenges.

      Who this book is for

      This book is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to go from basic predictive models to building advanced models and producing better predictions. Knowledge of Python and familiarity with predictive analytics concepts are assumed.

      What this book covers

      Chapter 1, Ensemble Methods for Regression and Classification, covers the application of ensemble methods or algorithms to produce accurate predictions of models. We will go through the application of ensemble methods for regression and classification problems. 

      Chapter 2, Cross-validation and Parameter Tuning, explores various

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