Hands-On Predictive Analytics with Python

By : Alvaro Fuentes

4.4 (8)

Buy this Book

Hands-On Predictive Analytics with Python

4.4 (8)

By: Alvaro Fuentes

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

The Predictive Analytics Process

Technical requirements

What is predictive analytics?

Reviewing important concepts of predictive analytics

The predictive analytics process

A quick tour of Python's data science stack

Summary

Further reading

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

Technical requirements

What is EDA?

Univariate EDA

Bivariate EDA

Introduction to graphical multivariate EDA

Summary

Further reading

Predicting Numerical Values with Machine Learning

Technical requirements

Introduction to ML

Practical considerations before modeling

MLR

Lasso regression

KNN

Training versus testing error

Summary

Further reading

Predicting Categories with Machine Learning

Technical requirements

Classification tasks

Credit card default dataset

Logistic regression

Classification trees

Random forests

Training versus testing error

Multiclass classification

Naive Bayes classifiers

Summary

Further reading

Introducing Neural Nets for Predictive Analytics

Technical requirements

Introducing neural network models

Introducing TensorFlow and Keras

Regressing with neural networks

Classification with neural networks

The dark art of training neural networks

Summary

Further reading

Model Evaluation

Technical requirements

Evaluation of regression models

Evaluation for classification models

The k-fold cross-validation

Summary

Further reading

Model Tuning and Improving Performance

Technical requirements

Hyperparameter tuning

Improving performance

Summary

Implementing a Model with Dash

Technical requirements

Model communication and/or deployment phase

Introducing Dash

Implementing a predictive model as a web application

Summary

Further reading

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Bivariate EDA

Now that we understand our features individually, it is time to start exploring whether there are any relationships between them. Bivariate EDA techniques are used to explore pairs of variables, and start understanding how they relate to each other.

How many pair relationships will we have? For a dataset of k features, we will have distinct pairs. In our original dataset we have 10 features, so we will have pairs of variables to analyze. This is a very small dataset, (in terms of the number of features)—but as you can see, the formula is basically a quadratic term, so for a large dataset, the number of pairs goes up quickly.

Of course, you don't actually have to analyze every possible pair; only choose those that are interesting or which will answer a particular question you may have about the dataset. In addition, pandas and Seaborn will make our task...