Stock Market Price Prediction Using Machine Learning Techniques

A stock market is a place where investors may buy and sell shares of a firm. The stock price and the regulatory body have a well-organized system in place, and participants who trade shares are registered there as well. Predicting the future price of a share is extremely concerned about the fact that stock market data is highly time-variant and frequently follows a complex pattern. A prediction is a useful tool for investors who want to stay on top of the latest price movements in the stock market. Consequently, clients may use this information to help them decide whether or not they should invest in certain shares of a given firm. Various data mining methods have been used to anticipate stock market prices in the past. The goal of this research is to use machine learning (ML) methods to forecast the stock price of businesses listed on the National Stock Exchange (NSE) index (NSE). The models will be built and trained using historical data from the chosen stock. The model's outputs will be compared to real-world data to determine the model's correctness. Using ML approaches, this research predicts stock prices for big and small market caps and in three separate markets, using both daily and up-to-the-minute data. Prediction errors may be quantified, and the suggested approach may produce better outcomes in the future.