In this chapter, we have started collecting the data needed to develop our model to predict bank failures. In this case, we have downloaded a large amount of data and we have structured it. Moreover, we have created our target variable. At the end of this chapter, you should have learned that data collection is the first and one of the most important steps in the model development. When you deal with your own problems, take time to understand the problem and then think about what kind of data you need and how to obtain it. In the next chapter, we will do a descriptive analysis of the data that we have acquired.
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Book Overview & Buying
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Table Of Contents
Machine Learning with R Quick Start Guide
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Machine Learning with R Quick Start Guide
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Overview of this book
Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline.
From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling.
By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.
Table of Contents (9 chapters)
Preface
Free Chapter
R Fundamentals for Machine Learning
Predicting Failures of Banks - Data Collection
Predicting Failures of Banks - Descriptive Analysis
Predicting Failures of Banks - Univariate Analysis
Predicting Failures of Banks - Multivariate Analysis
Visualizing Economic Problems in the European Union
Sovereign Crisis - NLP and Topic Modeling
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