Safe Trade – A Stock Recommender using Machine Learning Algorithms

In today's global economy, the business and financial sector wields significant influence, with stock market trading serving as a pivotal activity. This project leverages Google's TensorFlow's Keras API to employ Long Short-Term Memory (LSTM) neural networks, trained on historical data, to predict stock prices. The primary focus lies in forecasting a stock's value trend over the ensuing days following the prediction. Stock market forecasting plays a vital role in anticipating the future worth of a company's stocks, either to prevent losses or capitalize on potential profits. This study employs various quantitative measures to showcase the potential of machine learning in achieving accurate predictions. Our proposed machine learning algorithm learns from diverse datasets of past company performances. While the efficient market hypothesis posits that such predictions are unattainable with current data, our research challenges this notion. We have achieved an impressive frame detection accuracy of demonstrating the feasibility of our approach. The aim of stock market forecasting is to achieve measurable and substantial gains while minimizing potential losses. With the aid of advanced tools and technology, it is indeed possible to gain insights into future market trends, enabling investors to make informed decisions and generate real income. This research contributes to the growing body of evidence suggesting that machine learning can provide valuable insights for investors in navigating the complex world of stock markets, ultimately leading to more informed and profitable investment strategies.

 Keywords: LSTM, ANN, CNN, Stock Market