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Predicting Failures of Banks - Data Collection

In each model development, we will need to obtain enough data to build the model. It is very common to read the expression garbage in, garbage out, which relates to the fact that if you develop a model with bad data, the resulting model will be also bad.

Especially in machine learning applications, what we expect is to have a huge amount of data, although in many cases that's not the case. Regardless of the amount of information available, the quality of this data is the most important issue.

Moreover, as a developer, it is important to have structured data, because it can be immediately manipulated. However, data is commonly found in an unstructured form, meaning that it takes a lot of time to process and prepare for development. Many people consider machine learning applications to only be based on the use of new algorithms...

Key benefits

Use R 3.5 to implement real-world examples in machine learning

Implement key machine learning algorithms to understand the working mechanism of smart models

Create end-to-end machine learning pipelines using modern libraries from the R ecosystem

Description

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.

What you will learn

Introduce yourself to the basics of machine learning with R 3.5

Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results

Learn to build predictive models with the help of various machine learning techniques

Use R to visualize data spread across multiple dimensions and extract useful features

Use interactive data analysis with R to get insights into data

Implement supervised and unsupervised learning, and NLP using R libraries

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