Hands-On Predictive Analytics with Python电子书

Title Page

Copyright and Credits

Hands-On Predictive Analytics with Python

About Packt

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Packt.com

Contributors

About the author

About the reviewer

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

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Conventions used

Get in touch

Reviews

The Predictive Analytics Process

Technical requirements

What is predictive analytics?

Reviewing important concepts of predictive analytics

The predictive analytics process

Problem understanding and definition

Data collection and preparation

Dataset understanding using EDA

Model building

Model evaluation

Communication and/or deployment

CRISP-DM and other approaches

A quick tour of Python's data science stack

Anaconda

Jupyter

NumPy

A mini NumPy tutorial

SciPy

pandas

Matplotlib

Seaborn

Scikit-learn

TensorFlow and Keras

Dash

Summary

Further reading

Problem Understanding and Data Preparation

Technical requirements

Understanding the business problem and proposing a solution

Context is everything

Define what is going to be predicted

Make explicit the data that will be required

Think about access to the data

Proposing a solution

Define your methodology

Define key metrics of model performance

Define the deliverables of the project

Practical project – diamond prices

Diamond prices – problem understanding and definition

Getting more context

Diamond prices – proposing a solution at a high level

Goal

Methodology

Metrics for the model

Deliverables for the project

Diamond prices – data collection and preparation

Dealing with missing values

Practical project – credit card default

Credit card default – problem understanding and definition

Credit card default – proposing a solution

Goal

Methodology

Metrics for the model

Deliverables of the project

Credit card default – data collection and preparation

Credit card default – numerical features

Encoding categorical features

Low variance features

Near collinearity

One-hot encoding with pandas

A brief introduction to feature engineering

Summary

Further reading

Dataset Understanding – Exploratory Data Analysis

Technical requirements

What is EDA?

Univariate EDA

Univariate EDA for numerical features

Univariate EDA for categorical features

Bivariate EDA

Two numerical features

Scatter plots

The Pearson correlation coefficient

Two categorical features

Cross tables

Barplots for two categorical variables

One numerical feature and one categorical feature

Introduction to graphical multivariate EDA

Summary

Further reading

Predicting Numerical Values with Machine Learning

Technical requirements

Introduction to ML

Tasks in supervised learning

Creating your first ML model

The goal of ML models – generalization

Overfitting

Evaluation function and optimization

Practical considerations before modeling

Introducing scikit-learn

Further feature transformations

Train-test split

Dimensionality reduction using PCA

Standardization – centering and scaling

MLR

Lasso regression

KNN

Training versus testing error

Summary

Further reading

Predicting Categories with Machine Learning

Technical requirements

Classification tasks

Predicting categories and probabilities

Credit card default dataset

Logistic regression

A simple logistic regression model

A complete logistic regression model

Classification trees

How trees work

The good and the bad of trees

Training a larger classification tree

Random forests

Training versus testing error

Multiclass classification

Naive Bayes classifiers

Conditional probability

Bayes' theorem

Using Bayesian terms

Back to the classification problem

Gaussian Naive Bayes

Gaussian Naive Bayes with scikit-learn

Summary

Further reading

Introducing Neural Nets for Predictive Analytics

Technical requirements

Introducing neural network models

Deep learning

Anatomy of an MLP – elements of a neural network model

How MLPs learn

Introducing TensorFlow and Keras

TensorFlow

Keras – deep learning for humans

Regressing with neural networks

Building the MLP for predicting diamond prices

Training the MLP

Making predictions with the neural network

Classification with neural networks

Building the MLP for predicting credit card default

Evaluating predictions

The dark art of training neural networks

So many decisions; so little time

Regularization for neural networks

Using a validation set

Early stopping

Dropout

Practical advice on training neural networks

Summary

Further reading

Model Evaluation

Technical requirements

Evaluation of regression models

Metrics for regression models

MSE and Root Mean Squared Error (RMSE)

MAE

R-squared (R2)

Defining a custom metric

Visualization methods for evaluating regression models

Evaluation for classification models

Confusion matrix and related metrics

Visualization methods for evaluating classification models

Visualizing probabilities

Receiver Operating Characteristic (ROC) and precision-recall curves

Defining a custom metric for classification

The k-fold cross-validation

Summary

Further reading

Model Tuning and Improving Performance

Technical requirements

Hyperparameter tuning

Optimizing a single hyperparameter

Optimizing more than one parameter

Improving performance

Improving our diamond price predictions

Fitting a neural network

Transforming the target

Analyzing the results

Not only a technical problem but a business problem

Summary

Implementing a Model with Dash

Technical requirements

Model communication and/or deployment phase

Using a technical report

A feature of an existing product

Using an analytic application

Introducing Dash

What is Dash?

Plotly

Installation

The application layout

Building a basic static app

Building a basic interactive app

Implementing a predictive model as a web application

Producing the predictive model objects

Building the web application

Summary

Further reading

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