Scala Machine Learning Projects

Scala Machine Learning Projects

Overview of this book

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.

Title Page

Packt Upsell

Contributors

Preface

Free Chapter

Analyzing Insurance Severity Claims

Machine learning and learning workflow

Hyperparameter tuning and cross-validation

Analyzing and predicting insurance severity claims

LR for predicting insurance severity claims

GBT regressor for predicting insurance severity claims

Boosting the performance using random forest regressor

Comparative analysis and model deployment

Summary

Analyzing and Predicting Telecommunication Churn

Why do we perform churn analysis, and how do we do it?

Developing a churn analytics pipeline

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

High-level data pipeline of the prototype

Historical and live-price data collection

Model training for prediction

Scala Play web service

Predicting prices and evaluating the model

Demo prediction using Scala Play framework

Summary

Population-Scale Clustering and Ethnicity Prediction

Population scale clustering and geographic ethnicity

1000 Genomes Projects dataset description

Algorithms, tools, and techniques

Configuring programming environment

Data pre-processing and feature engineering

Summary

Topic Modeling - A Better Insight into Large-Scale Texts

Topic modeling and text clustering

Topic modeling with Spark MLlib and Stanford NLP

Other topic models versus the scalability of LDA

Deploying the trained LDA model

Summary

Developing Model-based Movie Recommendation Engines

Recommendation system

Spark-based movie recommendation systems

Selecting and deploying the best model

Summary

Options Trading Using Q-learning and Scala Play Framework

Reinforcement versus supervised and unsupervised learning

A simple Q-learning implementation

Developing an options trading web app using Q-learning

Summary

Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks

Client subscription assessment through telemarketing

Summary

Fraud Analytics Using Autoencoders and Anomaly Detection

Outlier and anomaly detection

Autoencoders and unsupervised learning

Developing a fraud analytics model

Hyperparameter tuning and feature selection

Summary

Human Activity Recognition using Recurrent Neural Networks

Working with RNNs

Human activity recognition using the LSTM model

Implementing an LSTM model for HAR

Tuning LSTM hyperparameters and GRU

Summary

Image Classification using Convolutional Neural Networks

Image classification and drawbacks of DNNs

CNN architecture

Large-scale image classification using CNN

Tuning and optimizing CNN hyperparameters

Summary

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Random Forest for churn prediction

As described in Chapter 1, Analyzing Insurance Severity Claim, Random Forest is an ensemble technique that takes a subset of observations and a subset of variables to build decision trees—that is, an ensemble of DTs. More technically, it builds several decision trees and integrates them together to get a more accurate and stable prediction.

Figure 7: Random forest and its assembling technique explained

This is a direct consequence, since by maximum voting from a panel of independent juries, we get the final prediction better than the best jury (see the preceding figure). Now that we already know the working principle of RF, let's start using the Spark-based implementation of RF. Let's start by importing the required packages and libraries:

import org.apache.spark._
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.sql._
import org.apache.spark.ml.Pipeline
import org...

Scala Machine Learning Projects

Scala Machine Learning Projects

Overview of this book

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Scala Machine Learning Projects

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