Application of machine learning using R-programming for financial forecasting
Author(s):
Dr NITIN UNTWAL
Keywords:
Machine learning, Supervised learning, unsupervised learning, Knowledge Representation, working capital, return on investment, R-programming
Abstract
Machine learning is an important branch of Artificial intelligence which includes designing and construction of algorithms. It allows computers to behave in a way based on the empirical dat. It automatically trains the system for pattern recognition which helps in decision making based on input data. The main objective of machine learning is to develop system that are too complex or costly to develop manually. Here we had applied machine learning for financial forecasting. The researcher had applied knowledge representation structure, supervised and unsupervised learning in the form of data frame with multiple regression model and factor analysis. The unsupervised learning is applied by way of factor analysis to group the variables used in the regression model. The multiple regression model is created where financial ratios viz, Debt to equity ratio, Inventory turnover ratio, current ratio, receivables turn over ratios are independent variable and return on investment is a dependent variable.
