Revitalizing Stock Predictions with Machine Learning Algorithms – An Empirical Study

The accurate prediction of stock prices has a significant role to play in the present economic world. However, stocks have an ever-changing nature coupled with a risk factor that always seems much of a gamble, making it worthwhile for researchers to analyze them with machine learning algorithms. We thus seek to develop a machine-based model replacing the traditional yet unreliable time-series forecasting to predict the stocks from the past historical data of a firm and to uncover the underlying patterns to improve the accuracy of the prediction of the model. As a beginner, we have decided to use a couple of conventional machine learning algorithms to study the behavior of learning techniques for stock prediction. This paper presents an empirical study to study and analyze the behavior of decision Tree, Linear Regression, K-Nearest Neighbours, and LSTM learning algorithms to bet on the algorithm that best predicts the stock prices.