Book description
Stepbystep guide to build high performing predictive applications
Key Features
 Use the Python data analytics ecosystem to implement endtoend predictive analytics projects
 Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations
 Learn to deploy a predictive model's results as an interactive application
Book Description
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, stepbystep examples, we build predictive analytics solutions while using cuttingedge Python tools and packages.
The book's stepbystep 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 handson 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 highperformance predictive analytics solutions using Python programming.
What you will learn
 Get to grips with the main concepts and principles of predictive analytics
 Learn about the stages involved in producing complete predictive analytics solutions
 Understand how to define a problem, propose a solution, and prepare a dataset
 Use visualizations to explore relationships and gain insights into the dataset
 Learn to build regression and classification models using scikitlearn
 Use Keras to build powerful neural network models that produce accurate predictions
 Learn to serve a model's predictions as a web application
Who this book is for
This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and collegelevel algebra.
Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files emailed directly to you.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Contributors
 Preface
 The Predictive Analytics Process

Problem Understanding and Data Preparation
 Technical requirements
 Understanding the business problem and proposing a solution
 Practical project – diamond prices
 Practical project – credit card default
 Summary
 Further reading
 Dataset Understanding – Exploratory Data Analysis
 Predicting Numerical Values with Machine Learning
 Predicting Categories with Machine Learning
 Introducing Neural Nets for Predictive Analytics
 Model Evaluation
 Model Tuning and Improving Performance
 Implementing a Model with Dash
 Other Books You May Enjoy
Product information
 Title: HandsOn Predictive Analytics with Python
 Author(s):
 Release date: December 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789138719
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