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Home > Data > Deep Learning > Scala Machine Learning Projects
Scala Machine Learning Projects
Scala Machine Learning Projects

Scala Machine Learning Projects: Build real-world machine learning and deep learning projects with Scala

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Scala Machine Learning Projects

Analyzing and Predicting Telecommunication Churn

In this chapter, we will develop a machine learning (ML) project to analyze and predict whether a customer is likely to cancel the subscription to his telecommunication contract or not. In addition, we'll do some preliminary analysis of the data and take a closer look at what types of customer features are typically responsible for such a churn.

Widely used classification algorithms, such as decision trees, random forest, logistic regression, and Support Vector Machines (SVMs) will be used for analyzing and making the prediction. By the end, readers will be able to choose the best model to use for a production-ready environment.

In a nutshell, we will learn the following topics throughout this end-to-end project:

  • Why, and how, do we do churn prediction?
  • Logistic regression-based churn prediction
  • SVM-based churn prediction
    • ...

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

Customer churn is the loss of clients or customers (also known as customer attrition, customer turnover, or customer defection). This concept was initially used within the telecommunications industry when many subscribers switched to other service providers. However, it has become a very important issue in other areas of business, such as banks, internet service providers, insurance companies, and so on. Well, two of the primary reasons for churn are customer dissatisfaction and cheaper and/or better offers from the competition.

As you can see in Figure 1, there are four possible contracts with the customer in a business industry: contractual, non-contractual, voluntary, and involuntary. The full cost of customer churn includes both the lost revenue and the (tele-) marketing costs involved with replacing those customers...

Developing a churn analytics pipeline

In ML, we observe an algorithm's performance in two stages: learning and inference. The ultimate target of the learning stage is to prepare and describe the available data, also called the feature vector, which is used to train the model.

The learning stage is one of the most important stages, but it is also truly time-consuming. It involves preparing a list of vectors, also called feature vectors (vectors of numbers representing the value of each feature), from the training data after transformation so that we can feed them to the learning algorithms. On the other hand, training data also sometimes contains impure information that needs some pre-processing, such as cleaning.

Once we have the feature vectors, the next step in this stage is preparing (or writing/reusing) the learning algorithm. The next important step is training...

LR for churn prediction

LR is one of the most widely used classifiers to predict a binary response. It is a linear ML method, as described in Chapter 1, Analyzing Insurance Severity Claim. The loss function is the formulation given by the logistic loss:

For the LR model, the loss function is the logistic loss. For a binary classification problem, the algorithm outputs a binary LR model such that, for a given new data point, denoted by x, the model makes predictions by applying the logistic function:

In the preceding equation, z = WTX and if f(WTX)>0.5, the outcome is positive; otherwise, it is negative.

Note that the raw output of the LR model, f(z), has a probabilistic interpretation.

Note that compared to linear regression, logistic regression provides you with a higher classification accuracy. Moreover, it is a flexible way to regularize a model for custom adjustment...

SVM for churn prediction

SVM is also used widely for large-scale classification (that is, binary as well as multinomial) tasks. Besides, it is also a linear ML method, as described in Chapter 1, Analyzing Insurance Severity Claim. The linear SVM algorithm outputs an SVM model, where the loss function used by SVM can be defined using the hinge loss, as follows:

L(w;x,y):=max{0,1−ywTx}

The linear SVMs in Spark are trained with an L2 regularization, by default. However, it also supports L1 regularization, by which the problem itself becomes a linear program.

Now, suppose we have a set of new data points x; the model makes predictions based on the value of wTx. By default, if wTx≥0, then the outcome is positive, and negative otherwise.

Now that we already know the SVMs working principle, let's start using the Spark-based implementation of SVM. Let's start...

DTs for churn prediction

DTs are commonly considered a supervised learning technique used for solving classification and regression tasks.

More technically, each branch in a DT represents a possible decision, occurrence, or reaction, in terms of statistical probability. Compared to naive Bayes, DTs are a far more robust classification technique. The reason is that at first, the DT splits the features into training and test sets. Then, it produces a good generalization to infer the predicted labels or classes. Most interestingly, a DT algorithm can handle both binary and multiclass classification problems.

For instance, in the following example figure, DTs learn from the admission data to approximate a sine curve with a set of if...else decision rules. The dataset contains the record of each student who applied for admission, say, to an American university. Each record contains...

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...

Selecting the best model for deployment

From the preceding results, it can be seen that LR and SVM models have the same but higher false positive rate compared to Random Forest and DT. So we can say that DT and Random Forest have better accuracy overall in terms of true positive counts. Let's see the validity of the preceding statement with prediction distributions on pie charts for each model:

Now, it's worth mentioning that using random forest, we are actually getting high accuracy, but it's a very resource, as well as time-consuming job; the training, especially, takes a considerably longer time as compared to LR and SVM.

Therefore, if you don't have higher memory or computing power, it is recommended to increase the Java heap space prior to running this code to avoid OOM errors.

Finally, if you want to deploy the best model (that is, Random Forest in our...

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

Customer churn is the loss of clients or customers (also known as customer attrition, customer turnover, or customer defection). This concept was initially used within the telecommunications industry when many subscribers switched to other service providers. However, it has become a very important issue in other areas of business, such as banks, internet service providers, insurance companies, and so on. Well, two of the primary reasons for churn are customer dissatisfaction and cheaper and/or better offers from the competition.

As you can see in Figure 1, there are four possible contracts with the customer in a business industry: contractual, non-contractual, voluntary, and involuntary. The full cost of customer churn includes both the lost revenue and the (tele-) marketing costs involved with replacing those customers with new ones. However, this type of loss can cause a huge loss to a business. Think back to a decade ago, when Nokia...

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Key benefits

  • Explore machine learning techniques with prominent open source Scala libraries such as Spark ML, H2O, MXNet, Zeppelin, and DeepLearning4j
  • Solve real-world machine learning problems by delving complex numerical computing with Scala functional programming in a scalable and faster way
  • Cover all key aspects such as collection, storing, processing, analyzing, and evaluation required to build and deploy machine models on computing clusters using Scala Play framework.

Description

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.

Who is this book for?

If you want to leverage the power of both Scala and Spark to make sense of Big Data, then this book is for you. If you are well versed with machine learning concepts and wants to expand your knowledge by delving into the practical implementation using the power of Scala, then this book is what you need! Strong understanding of Scala Programming language is recommended. Basic familiarity with machine Learning techniques will be more helpful.

What you will learn

  • Apply advanced regression techniques to boost the performance of predictive models
  • Use different classification algorithms for business analytics
  • Generate trading strategies for Bitcoin and stock trading using ensemble techniques
  • Train Deep Neural Networks (DNN) using H2O and Spark ML
  • Utilize NLP to build scalable machine learning models
  • Learn how to apply reinforcement learning algorithms such as Q-learning for developing ML application
  • Learn how to use autoencoders to develop a fraud detection application
  • Implement LSTM and CNN models using DeepLearning4j and MXNet
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Length: 470 pages
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Table of Contents

12 Chapters
Analyzing Insurance Severity Claims Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
Clients Subscription Assessment for Bank Telemarketing using Deep Neural Networks
Client subscription assessment through telemarketing
Summary
Fraud Analytics Using Autoencoders and Anomaly Detection Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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 Chevron down icon Chevron up icon
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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About the author

Profile icon Karim
Karim
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Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.
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Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela