STOCK RECOMMENDATION SYSTEM

Predicting the stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors, etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. The Stock Recommendation system aims to suggest to the user whether the particular stock is worth investing in or not. It is achieved by predicting the closing price of the stock by using the historical data of that stock. It uses Bagging Classifier i.e., the type of ensemble machine learning approach which combines the output of many algorithms to improve performance and pass the result through a series of logical conditions, and the output of the project is displayed to the user.