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Scala Machine Learning Projects

更新時間：2021-06-30 19:06:29

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IfyouwanttoleveragethepowerofbothScalaandSparktomakesenseofBigData,thenthisbookisforyou.IfyouarewellversedwithmachinelearningconceptsandwantstoexpandyourknowledgebydelvingintothepracticalimplementationusingthepowerofScala,thenthisbookiswhatyouneed!StrongunderstandingofScalaProgramminglanguageisrecommended.BasicfamiliaritywithmachineLearningtechniqueswillbemorehelpful.

目錄(267章)

倒序

coverpage
Title Page
Packt Upsell
Why subscribe?
PacktPub.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
Analyzing Insurance Severity Claims
Machine learning and learning workflow
Typical machine learning workflow
Hyperparameter tuning and cross-validation
Analyzing and predicting insurance severity claims
Motivation
Description of the dataset
Exploratory analysis of the dataset
Data preprocessing
LR for predicting insurance severity claims
Developing insurance severity claims predictive model using LR
GBT regressor for predicting insurance severity claims
Boosting the performance using random forest regressor
Random Forest for classification and regression
Comparative analysis and model deployment
Spark-based model deployment for large-scale dataset
Summary
Analyzing and Predicting Telecommunication Churn
Why do we perform churn analysis and how do we do it?
Developing a churn analytics pipeline
Description of the dataset
Exploratory analysis and feature engineering
LR for churn prediction
SVM for churn prediction
DTs for churn prediction
Random Forest for churn prediction
Selecting the best model for deployment
Summary
High Frequency Bitcoin Price Prediction from Historical and Live Data
Bitcoin cryptocurrency and online trading
State-of-the-art automated trading of Bitcoin
Training
Prediction
High-level data pipeline of the prototype
Historical and live-price data collection
Historical data collection
Transformation of historical data into a time series
Assumptions and design choices
Data preprocessing
Real-time data through the Cryptocompare API
Model training for prediction
Scala Play web service
Concurrency through Akka actors
Web service workflow
JobModule
Scheduler
SchedulerActor
PredictionActor and the prediction step
TraderActor
Predicting prices and evaluating the model
Demo prediction using Scala Play framework
Why RESTful architecture?
Project structure
Running the Scala Play web app
Summary
Population-Scale Clustering and Ethnicity Prediction
Population scale clustering and geographic ethnicity
Machine learning for genetic variants
1000 Genomes Projects dataset description
Algorithms tools and techniques
H2O and Sparkling water
ADAM for large-scale genomics data processing
Unsupervised machine learning
Population genomics and clustering
How does K-means work?
DNNs for geographic ethnicity prediction
Configuring programming environment
Data pre-processing and feature engineering
Model training and hyperparameter tuning
Spark-based K-means for population-scale clustering
Determining the number of optimal clusters
Using H2O for ethnicity prediction
Using random forest for ethnicity prediction
Summary
Topic Modeling - A Better Insight into Large-Scale Texts
Topic modeling and text clustering
How does LDA algorithm work?
Topic modeling with Spark MLlib and Stanford NLP
Implementation
Step 1 - Creating a Spark session
Step 2 - Creating vocabulary and tokens count to train the LDA after text pre-processing
Step 3 - Instantiate the LDA model before training
Step 4 - Set the NLP optimizer
Step 5 - Training the LDA model
Step 6 - Prepare the topics of interest
Step 7 - Topic modelling
Step 8 - Measuring the likelihood of two documents
Other topic models versus the scalability of LDA
Deploying the trained LDA model
Summary
Developing Model-based Movie Recommendation Engines
Recommendation system
Collaborative filtering approaches
Content-based filtering approaches
Hybrid recommender systems
Model-based collaborative filtering
The utility matrix
Spark-based movie recommendation systems
Item-based collaborative filtering for movie similarity
Step 1 - Importing necessary libraries and creating a Spark session
Step 2 - Reading and parsing the dataset
Step 3 - Computing similarity
Step 4 - Testing the model
Model-based recommendation with Spark
Data exploration
Movie recommendation using ALS
Step 1 - Import packages load parse and explore the movie and rating dataset
Step 2 - Register both DataFrames as temp tables to make querying easier
Step 3 - Explore and query for related statistics
Step 4 - Prepare training and test rating data and check the counts
Step 5 - Prepare the data for building the recommendation model using ALS
Step 6 - Build an ALS user product matrix
Step 7 - Making predictions
Step 8 - Evaluating the model
Selecting and deploying the best model
Summary
Options Trading Using Q-learning and Scala Play Framework
Reinforcement versus supervised and unsupervised learning
Using RL
Notation policy and utility in RL
Policy
Utility
A simple Q-learning implementation
Components of the Q-learning algorithm
States and actions in QLearning
The search space
The policy and action-value
QLearning model creation and training
QLearning model validation
Making predictions using the trained model
Developing an options trading web app using Q-learning
Problem description
Implementating an options trading web application
Creating an option property
Creating an option model
Putting it altogether
Evaluating the model
Wrapping up the options trading app as a Scala web app
The backend
The frontend
Running and Deployment Instructions
Model deployment
Summary
Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks
Client subscription assessment through telemarketing
Dataset description
Installing and getting started with Apache Zeppelin
Building from the source
Starting and stopping Apache Zeppelin
Creating notebooks
Exploratory analysis of the dataset
Label distribution
Job distribution
Marital distribution
Education distribution
Default distribution
Housing distribution
Loan distribution
Contact distribution
Month distribution
Day distribution
Previous outcome distribution
Age feature
Duration distribution
Campaign distribution
Pdays distribution
Previous distribution
emp_var_rate distributions
cons_price_idx features
cons_conf_idx distribution
Euribor3m distribution
nr_employed distribution
Statistics of numeric features
Implementing a client subscription assessment model
Hyperparameter tuning and feature selection
Number of hidden layers
Number of neurons per hidden layer
Activation functions
Weight and bias initialization
Regularization
Summary
Fraud Analytics Using Autoencoders and Anomaly Detection
Outlier and anomaly detection
Autoencoders and unsupervised learning
Working principles of an autoencoder
Efficient data representation with autoencoders
Developing a fraud analytics model
Description of the dataset and using linear models
Problem description
Preparing programming environment
Step 1 - Loading required packages and libraries
Step 2 - Creating a Spark session and importing implicits
Step 3 - Loading and parsing input data
Step 4 - Exploratory analysis of the input data
Step 5 - Preparing the H2O DataFrame
Step 6 - Unsupervised pre-training using autoencoder
Step 7 - Dimensionality reduction with hidden layers
Step 8 - Anomaly detection
Step 9 - Pre-trained supervised model
Step 10 - Model evaluation on the highly-imbalanced data
Step 11 - Stopping the Spark session and H2O context
Auxiliary classes and methods
Hyperparameter tuning and feature selection
Summary
Human Activity Recognition using Recurrent Neural Networks
Working with RNNs
Contextual information and the architecture of RNNs
RNN and the long-term dependency problem
LSTM networks
Human activity recognition using the LSTM model
Dataset description
Setting and configuring MXNet for Scala
Implementing an LSTM model for HAR
Step 1 - Importing necessary libraries and packages
Step 2 - Creating MXNet context
Step 3 - Loading and parsing the training and test set
Step 4 - Exploratory analysis of the dataset
Step 5 - Defining internal RNN structure and LSTM hyperparameters
Step 6 - LSTM network construction
Step 7 - Setting up an optimizer
Step 8 - Training the LSTM network
Step 9 - Evaluating the model
Tuning LSTM hyperparameters and GRU
Summary
Image Classification using Convolutional Neural Networks
Image classification and drawbacks of DNNs
CNN architecture
Convolutional operations
Pooling layer and padding operations
Subsampling operations
Convolutional and subsampling operations in DL4j
Configuring DL4j ND4s and ND4j
Convolutional and subsampling operations in DL4j
Large-scale image classification using CNN
Problem description
Description of the image dataset
Workflow of the overall project
Implementing CNNs for image classification
Image processing
Extracting image metadata
Image feature extraction
Preparing the ND4j dataset
Training the CNNs and saving the trained models
Evaluating the model
Wrapping up by executing the main() method
Tuning and optimizing CNN hyperparameters
Summary
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Scala Machine Learning Projects