Pruned Random Forests for Effective and Efficient Financial Data Analytics

It is evident that Machine Learning (ML) has touched all walks of our lives! From checking the weather forecast to applying for a loan or a credit card, ML is used in almost every aspect of our daily life. In this chapter, ML is explored in terms of algorithms and applications. Special consideration is given to ML applications in the financial data analytics domain including stock market analysis, fraud detection in financial transactions, credit risk analysis, loan defaulting rate analysis, and profit–loss analysis. The chapter establishes the significance of Random Forests as an effective machine learning method for a wide variety of financial applications.