Hands-On Predictive Analytics with Python
更新时间：2021-07-02 14:22:37
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Title Page
Copyright and Credits
Hands-On Predictive Analytics with Python
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
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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更新时间：2021-07-02 14:22:37