Finding Features of Multiple Linear Regression On Currency Exchange Pairs - Raymond Sunardi Oetama; Ford Lumban Gaol; Benfano Soewito; Harco Leslie Hendric Spits Warnars (Universitas Multimedia Nusantara, 2022)
Due to the prospects for financial gain, forex is always attractive to many people. However, because forex market analysis is not simple, a computer is needed to assist in creating predictions using features that are understandable to people. This study employs the Multilinear Regression technique to identify these kinds of features. The features and prediction target have a very strong correlation. With a very low RMSE and a very high R square, the prediction quality is quite outstanding. The outcome will help academics in the forex field use machine learning algorithms to make better predictions.