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Problem Understanding and Data Preparation

In the last chapter, we learned about the predictive analytics process; we also learned about some of the fundamental definitions and the main libraries in the Python data ecosystem. In this chapter, we will start getting our hands on a couple of datasets and delve deeper into the first and second phases of the predictive analytics process: Problem understanding and definition and Data collection and preparation.

In the first part of this chapter, we talk about some of the most important considerations when defining and understanding the problem: having enough context and domain knowledge about the problem, and defining what is being predicted and the data that we have to work with. This phase also includes proposing a solution; we talk about some of the main topics to consider.

We put this idea into practice in the second part of the...

Key benefits

Use the Python data analytics ecosystem to implement end-to-end 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

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, 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.

Who is this book 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 college-level algebra.

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 scikit-learn

Use Keras to build powerful neural network models that produce accurate predictions

Learn to serve a model s predictions as a web application

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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reviewer of books #102395141 Dec 01, 2021

This is really educational content. My computer science graduate course used this textbook and the coding and problems in the book teach you alot. The majority of the code ran as written. I suspect there's some version conflicts between my interpreter and the book. There's a few errors/typos in the code, but they have a free downloadable jupyter notebook version that written and runs correctly but you have to go to their site. Learned alot about regression and classification problems, and this book doesn't require loads of math knowledge to get the point. If you have some python experience this book should be rather straightforward and useful.

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roudan Nov 15, 2019

I love it. It is clear and well written.

AGUILAR MONTOYA HUGO GEOVANNY Apr 08, 2019

Hands-On Predictive Analytics with Python is a practical manual that will lead you from the basics of analysis to a model deployment. It starts with theroy on the predictive analytics process from the very beggining (problem definition, data collection and preparation, etc.) that more advanced readers migth skip. However, in same Chapter One, theory of Python starts, so if you are a first timer on this tool, it will be very useful.Then, the manual gets better, with EDA, Machine Learning, and Neural Nets becoming the chapters. This is a must read and practice manual for everyone who wants to learn a begginer's lever on Python applied to analytics. Have a great practice time while learning deep Python!

Susie GJ Mar 04, 2019

Recommended book if you want to understand how to do predictive analytics in practice, I have followed many online resources about predictive modelling, but they never go beyond simple cases. In this book, I learned how to actually deploy a model as a web application. Great examples!

Carlos en Urbana Mar 12, 2019

This is a great book to really understand predictive analytics. Unlike other books it is not only a list of algorithms, but a complete guide of all the stages in the process of using predictive analytics to solve problems. Going from problem understanding to deploying a usable neural network model for a final user was really awesome!

Hands-On Predictive Analytics with Python: Master the complete predictive analytics process, from problem definition to model deployment

What do you get with eBook?

Hands-On Predictive Analytics with Python

Problem Understanding and Data Preparation

Technical requirements

Understanding the business problem and proposing a solution

Context is everything

Practical project – diamond prices

Diamond prices – problem understanding and definition

Practical project – credit card default

Credit card default – problem understanding and definition

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