The financial market system is too complex for man to do predict the outcome. Due to the uncertain behaviour of the stock price has become very challenging. The most common way is to predict stock prices by using historical information about the data. But the predictions are great it is important for investors to predict the major assets they will receive profit. Also those who are willing to invest in the stock market but for the risks of stock fluctuations are not very sure to invest in the stock market, so to overcome this problem our research work is focused on looking at it changes in stock prices. In this research project there are four predictive algorithms propose to use historical data to predict stock market movements. I surveillance algorithms proposed by K-Nearest Neighbor (KNN), Random Forest, Vector Support Machine (SVM) and Line Down
₹10000 (INR)
NON IEEE -2022