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Predicting Employee Attrition Using Ensemble Models

If you reviewed the recent machine learning competitions, one key observation I am sure you would make is that the recipes of all three winning entries in most of the competitions include very good feature engineering, along with well-tuned ensemble models. One conclusion I derive from this observation is that good feature engineering and building well-performing models are two areas that should be given equal emphasis in order to deliver successful machine learning solutions.

While feature engineering most times is something that is dependent on the creativity and domain expertise of the person building the model, building a well-performing model is something that can be achieved through a philosophy called ensembling. Machine learning practitioners often use ensembling techniques to beat the performance benchmarks yielded by...

Key benefits

Master machine learning, deep learning, and predictive modeling concepts in R 3.5

Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains

Implement smart cognitive models with helpful tips and best practices

Description

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.

Who is this book for?

If you’re a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.

What you will learn

Explore deep neural networks and various frameworks that can be used in R

Develop a joke recommendation engine to recommend jokes that match users' tastes

Create powerful ML models with ensembles to predict employee attrition

Build autoencoders for credit card fraud detection

Work with image recognition and convolutional neural networks

Make predictions for casino slot machine using reinforcement learning

Implement NLP techniques for sentiment analysis and customer segmentation

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