In this project, I have employed a machine learning approach to enhance the prediction of stock prices, specifically the closing prices. This implementation leverages the effectiveness of machine learning to make more precise and well-informed investment decisions. The primary goal is to forecast stock prices, enabling investors to improve the accuracy of their decision-making and potentially identify profitable trading opportunities.
To achieve this, I have developed a stock price prediction system that integrates mathematical functions, machine learning techniques, and external factors. This holistic approach is designed to enhance the accuracy of stock price predictions and facilitate more successful trading strategies.
LSTMs (Long Short-Term Memory networks) play a pivotal role in this project, particularly in the context of sequence prediction. LSTMs excel at retaining and utilizing past information, a crucial factor when predicting stock prices. Given that a stock's previous price is often a key indicator for its future price, LSTMs prove invaluable in this regard. While predicting the exact price of a stock remains a challenging endeavor, I have constructed a model that can predict the stock's price movement, whether it will rise or fall, offering valuable insights for investors.
https://www.kaggle.com/datasets/akshaydattatraykhare/nsetataglobal