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Practical Machine Learning Cookbook
Practical Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Sections
Getting ready
How to do it…
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Introduction to Machine Learning
What is machine learning?
An overview of classification
An overview of clustering
An overview of supervised learning
An overview of unsupervised learning
An overview of reinforcement learning
An overview of structured prediction
An overview of neural networks
An overview of deep learning
2. Classification
Introduction
Discriminant function analysis - geological measurements on brines from wells
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training the model
Step 5 - classifying the data
Step 6 - evaluating the model
Multinomial logistic regression - understanding program choices made by students
Getting ready
Step 1 - collecting data
How to do it...
Step 2 - exploring data
Step 3 - training the model
Step 4 - testing the results of the model
Step 5 - model improvement performance
Tobit regression - measuring the students' academic aptitude
Getting ready
Step 1 - collecting data
How to do it...
Step 2 - exploring data
Step 3 - plotting data
Step 4 - exploring relationships
Step 5 - training the model
Step 6 - testing the model
Poisson regression - understanding species present in Galapagos Islands
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - plotting data and testing empirical data
Step 4 - rectifying discretization of the Poisson model
Step 5 - training and evaluating the model using the link function
Step 6 - revaluating using the Poisson model
Step 7 - revaluating using the linear model
3. Clustering
Introduction
Hierarchical clustering - World Bank sample dataset
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training and evaluating the model performance
Step 5 - plotting the model
Hierarchical clustering - Amazon rainforest burned between 1999-2010
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training and evaluating model performance
Step 5 - plotting the model
Step 6 - improving model performance
Hierarchical clustering - gene clustering
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training the model
Step 5 - plotting the model
Binary clustering - math test
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - training and evaluating model performance
Step 4 - plotting the model
Step 5 - K-medoids clustering
K-means clustering - European countries protein consumption
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - clustering
Step 4 - improving the model
K-means clustering - foodstuff
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - clustering
Step 5 - visualizing the clusters
4. Model Selection and Regularization
Introduction
Shrinkage methods - calories burned per day
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - building the model
Step 4 - improving the model
Step 5 - comparing the model
Dimension reduction methods - Delta's Aircraft Fleet
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - applying principal components analysis
Step 4 - scaling the data
Step 5 - visualizing in 3D plot
Principal component analysis - understanding world cuisine
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - preparing data
Step 4 - applying principal components analysis
5. Nonlinearity
Generalized additive models - measuring the household income of New Zealand
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - setting up the data for the model
Step 4 - building the model
Smoothing splines - understanding cars and speed
How to do it...
Step 1 - exploring the data
Step 2 - creating the model
Step 3 - fitting the smooth curve model
Step 4 - plotting the results
Local regression - understanding drought warnings and impact
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - collecting and exploring data
Step 3 - calculating the moving average
Step 4 - calculating percentiles
Step 5 - plotting results
6. Supervised Learning
Introduction
Decision tree learning - Advance Health Directive for patients with chest pain
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing the data
Step 4 - training the model
Step 5- improving the model
Decision tree learning - income-based distribution of real estate values
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - training the model
Step 4 - comparing the predictions
Step 5 - improving the model
Decision tree learning - predicting the direction of stock movement
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - calculating the indicators
Step 4 - preparing variables to build datasets
Step 5 - building the model
Step 6 - improving the model
Naive Bayes - predicting the direction of stock movement
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing variables to build datasets
Step 4 - building the model
Step 5 - creating data for a new, improved model
Step 6 - improving the model
Random forest - currency trading strategy
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing variables to build datasets
Step 4 - building the model
Support vector machine - currency trading strategy
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - calculating the indicators
Step 4 - preparing variables to build datasets
Step 5 - building the model
Stochastic gradient descent - adult income
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing the data
Step 4 - building the model
Step 5 - plotting the model
7. Unsupervised Learning
Introduction
Self-organizing map - visualizing of heatmaps
How to do it...
Step 1 - exploring data
Step 2 - training the model
Step 3 - plotting the model
Vector quantization - image clustering
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - data cleaning
Step 4 - visualizing cleaned data
Step 5 - building the model and visualizing it
8. Reinforcement Learning
Introduction
Markov chains - the stocks regime switching model
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing the regression model
Step 4 - preparing the Markov-switching model
Step 5 - plotting the regime probabilities
Step 6 - testing the Markov switching model
Markov chains - the multi-channel attribution model
Getting ready
How to do it...
Step 1 - preparing the dataset
Step 2 - preparing the model
Step 3 - plotting the Markov graph
Step 4 - simulating the dataset of customer journeys
Step 5 - preparing a transition matrix heat map for real data
Markov chains - the car rental agency service
How to do it...
Step 1 - preparing the dataset
Step 2 - preparing the model
Step 3 - improving the model
Continuous Markov chains - vehicle service at a gas station
Getting ready
How to do it...
Step 1 - preparing the dataset
Step 2 - computing the theoretical resolution
Step 3 - verifying the convergence of a theoretical solution
Step 4 - plotting the results
Monte Carlo simulations - calibrated Hull and White short-rates
Getting ready
Step 1 - installing the packages and libraries
How to do it...
Step 2 - initializing the data and variables
Step 3 - pricing the Bermudan swaptions
Step 4 - constructing the spot term structure of interest rates
Step 5 - simulating Hull-White short-rates
9. Structured Prediction
Introduction
Hidden Markov models - EUR and USD
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - turning data into a time series
Step 4 - building the model
Step 5 - displaying the results
Hidden Markov models - regime detection
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - preparing the model
10. Neural Networks
Introduction
Modelling SP 500
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - calculating the indicators
Step 4 - preparing data for model building
Step 5 - building the model
Measuring the unemployment rate
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - preparing and verifying the models
Step 4 - forecasting and testing the accuracy of the models built
11. Deep Learning
Introduction
Recurrent neural networks - predicting periodic signals
Getting ready...
How to do it...
12. Case Study - Exploring World Bank Data
Introduction
Exploring World Bank data
Getting ready...
Step 1 - collecting and describing data
How to do it...
Step 2 - downloading the data
Step 3 - exploring data
Step 4 - building the models
Step 5 - plotting the models
13. Case Study - Pricing Reinsurance Contracts
Introduction
Pricing reinsurance contracts
Getting ready...
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - calculating the individual loss claims
Step 4 - calculating the number of hurricanes
Step 5 - building predictive models
Step 6 - calculating the pure premium of the reinsurance contract
14. Case Study - Forecast of Electricity Consumption
Introduction
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - time series - regression analysis
Step 4 - time series - improving regression analysis
Step 5 - building a forecasting model
Step 6 - plotting the forecast for a year
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