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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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Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration
Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics
Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics
Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations
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