Predictive Analysis of Stock market return using Machine Learning Algorithms

Stock market return is inherently volatile and uncertain, driving investors toconstantly seek methods to forecast future trends in order to minimize losses andmaximize profits. However, it remains a fact that no technique can predict marketmovements with absolute accuracy. Despite this, various approaches are beingexplored to enhance the predictive performance of models as much as possible.With the rapid progress in Machine Learning (ML) over recent years, numerous algorithms are now being applied to improve stock price prediction. This paperresearches 5 algorithms namely Linear Regression, Random Forest, Support VectorMachine (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANN) forpredicting stock market return of 10 top companies listed in National StockExchange (NSE) over the last 10 years was collected and used. In detail, themethodology followed, till acquire the results has been talked about step-wise. Theconclusion about this research work have been presented it has been inferred thatLinear Regression and Artificial Neural Networks (ANN) algorithm outperforms allthe other algorithms for stock market return and provides results with extensiveaccuracy.