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Hands-On Predictive Analytics with Python
Hands-On Predictive Analytics with Python

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

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Hands-On Predictive Analytics with Python

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...

Technical requirements

  • Python 3.6 or higher
  • Jupyter Notebook
  • Recent versions of the following Python libraries: NumPy, pandas, and matplotlib

Understanding the business problem and proposing a solution

In this section, we talk about problem understanding and definition, and other aspects related to the activity of defining the problem that will be solved using predictive analytics. Of course, the specifics of this stage depend entirely on the project, so we will provide only very generic guidance about this. However, when discussing the practical examples, we will touch on some of the important considerations when understanding the problem in a predictive analytics project.

Context is everything

What we call Problem understanding and definition is the first stage in the process, and as we mentioned in the last chapter, this is a key stage because here is where we...

Practical project – diamond prices

In this section, we introduce the diamond prices dataset. Let's start implementing the predictive analytics process we discussed in the first chapter. We begin with the stage we just discussed in the last section, Problem understanding and definition.

Diamond prices – problem understanding and definition

A new company, Intelligent Diamond Reseller (IDR), wants to get into the business of reselling diamonds. They want to innovate in the business, so they will use predictive modeling to estimate how much the market will pay for diamonds. Of course, to sell diamonds in the market, first they have to buy them from the producers; this is where predictive modeling becomes useful...

Practical project – credit card default

This is our second practical project, in which we will solve a classification problem. As we did with the diamonds dataset, let's begin the predictive analytics process for this new project by understanding and defining the problem.

Credit card default – problem understanding and definition

TFI is the Taiwanese Financial Institution and it offers credit cards. It has been detecting an increase in defaults among its customers; a default is defined as a customer missing a payment for a single month. This situation is negatively affecting the revenue of the company and they know they can do something about it if they could anticipate which credit card holders are going...

Summary

In this chapter, we have covered two stages in the predictive analytics process: Problem understanding and definition and Data collection and preparation. We learned about important considerations for understanding the problem and proposing the solution; we also introduced the concepts of regression tasks and classification tasks. We got our hands dirty with a couple of datasets that we will continue within the following chapters, and in going through the second phase, Data collection and preparation, with these datasets, we introduce important concepts such as one-hot encoding, outliers, missing values, collinearity, and feature engineering. In addition, we got to practice how to use pandas for loading, exploring, transforming, and preparing a dataset to continue with the next stages of the predictive analytics process.

In the next chapter, we will study the goals of...

Further reading

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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
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Length: 330 pages
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Language : English
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Table of Contents

10 Chapters
The Predictive Analytics Process Chevron down icon Chevron up icon
Problem Understanding and Data Preparation Chevron down icon Chevron up icon
Dataset Understanding – Exploratory Data Analysis Chevron down icon Chevron up icon
Predicting Numerical Values with Machine Learning Chevron down icon Chevron up icon
Predicting Categories with Machine Learning Chevron down icon Chevron up icon
Introducing Neural Nets for Predictive Analytics Chevron down icon Chevron up icon
Model Evaluation Chevron down icon Chevron up icon
Model Tuning and Improving Performance Chevron down icon Chevron up icon
Implementing a Model with Dash Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(8 Ratings)
5 star 75%
4 star 12.5%
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2 star 0%
1 star 12.5%
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reviewer of books #102395141 Dec 01, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I love it. It is clear and well written.
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AGUILAR MONTOYA HUGO GEOVANNY Apr 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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!
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Susie GJ Mar 04, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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!
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Carlos en Urbana Mar 12, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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!
Amazon Verified review Amazon
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