Machine Learning Classification Models for Banking Domain

The prime aim of machine learning is to develop techniques or methods which automatically detect patterns in the given data, and later to make use the discovered pattern to predict future or other results of interest. Machine learning practitioners around the globe are trying to model the huge variety of data of varying levels of complexity using their diverse tools and methods. The models thus built are representations of the observed data which will identify some the regularities or some interesting patterns. In this paper, models constructed are based on classification which is a function that maps or classifies a data instance into one of several predefined class labels. A separate testing set is used to test the classifying ability of the learned model or function which uses the features such as Term frequency- Inverse Document Frequency (TF-IDF). In recent years Artificial Intelligence has impacted the Banking industry to a great extent. For organizations working in the banking industry, it has become increasingly crucial to keep up with competition, and increase their standing as an innovative company. In this work, the machine learning classification models are generated for the Banking domain to improve the system by considering and involving the feedback provided by customers, by collecting the text data related to banking from the web. Web crawlers are used on target specific sites to collect raw data