Stock Price Prediction using Machine Learning Algorithms

Abstract

Abstract: Machine learning has broad applications in the finance industry. Risk Analytics, Consumer Analytics, Fraud Detection, and Stock Market Predictions are some of the domains where machine learning methods can be implemented. Accurate prediction of stock market returns is extremely difficult due to volatility in the market. The main factor in predicting a stock market is a high level of accuracy and precision. With the introduction of artificial intelligence and high computational capacity, efficiency has increased. In the past few decades, the highly theoretical and speculative nature of the stock market has been examined by capturing and using repetitive patterns. Various machine learning algorithms like Multiple Linear Regression, Polynomial Regression, etc. are used here. The financial data contains factors like Date, Volume, Open, High, Low Close, and Adj Close prices. The models are evaluated using standard strategic indicators RMSE and R2 score. Lower values of these two indicators mean higher efficiency of the trained models. Various companies employ different types of analysis tools for forecasting and the primary aim is the accuracy to obtain the maximum profit. The successful prediction of the stock will be an invaluable asset for the stock market institutions and will provide real-life solutions to the problems of the investors. Keywords: Stock prices, Analysis, Accuracy, Prediction, Machine Learning, Regression, Finance