Book Image

Hands-On Predictive Analytics with Python

By : Alvaro Fuentes
Book Image

Hands-On Predictive Analytics with Python

By: Alvaro Fuentes

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.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
The Predictive Analytics Process
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
2
Problem Understanding and Data Preparation
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
3
Dataset Understanding – Exploratory Data Analysis
Dataset Understanding – Exploratory Data Analysis
Technical requirements
What is EDA?
Univariate EDA
Bivariate EDA
Introduction to graphical multivariate EDA
Summary
Further reading
4
Predicting Numerical Values with Machine Learning
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
5
Predicting Categories with Machine Learning
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
6
Introducing Neural Nets for Predictive Analytics
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
7
Model Evaluation
Model Evaluation
Technical requirements
Evaluation of regression models
Evaluation for classification models
The k-fold cross-validation
Summary
Further reading
8
Model Tuning and Improving Performance
Model Tuning and Improving Performance
Technical requirements
Hyperparameter tuning
Improving performance
Summary
9
Implementing a Model with Dash
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
10
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What is EDA?

As we stated in Chapter 1, The Predictive Analytics Process, EDA is a combination of numerical and visualization techniques that allow us to understand different characteristics of a dataset, its features, and the potential relationships between them.

Keep in mind the goal of this phase: to understand your dataset. The goal is not to produce summary statistics, pretty visualizations, or complex multivariate analysis. These are simple activities that accomplish the ultimate goal of data understanding.

Also, please don't confuse calculation with understanding. Anyone can calculate the standard deviation of a numerical feature; it can be done (for example) with the std() pandas Series method. Your job here is to use that number to understand your features and your dataset better.

Another example—after reading the definitions of symmetric and skewed distribution...

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