The project titled "Currency Exchange Rate Prediction'' is the regression problem in Machine Learning. In the financial market, Current Exchange is playing the biggest part and expanding its wings day by day by the concept of Globalization. If you look into the current US Dollar which speaks 81.40 in Indian rupees. [fig:1]. Here the value of one US dollar is different from country to country. There are many factors which affect the exchange rates of currency like psychological aspects, political and economic etc. [Fig:3]. The problem of Currency Exchange prediction is difficult to deal with. Through this project, our team is going to solve the problem of currency exchange with Machine learning technology using python. Predicting the currency rate gives the investor an added edge in making their investment in a better method because the forex market is the foundation of worldwide investing and international trade. It's crucial to accurately calculate the forex rate so that we don't give people incorrect information. EMD-RNN and ARIMA are two models that we are utilizing to make an accurate prediction. To demonstrate which is superior, compare their output with the same data set. The historical dataset obtained through foreign exchange is used to test the aforementioned strategies. Predicting currency exchange rate predictions need to look into all the changes and consider them daily globally. [Fig:3]. These predictions affect the income of every citizen of a person and show impacts on businesses as well as on a country's economy. Thus, with the currency exchange rate prediction we can help every individual as well as country in many ways. The future currency exchange predictions are derived by studying all possibilities of historical data in the FOREX Market. There are four Machine Learning models which support currency exchange predictions. They are Backpropagation, Radial Basis Function, Long Shortterm Memory, Support Vector Regression.

Keywords : Currency Exchange Rate Prediction, ARIMA, FOREX Marketing, Regression, Supervised Machine Learning, Decision Node Regression Algorithm, CART.