Long-term memory (LSTM) is a deep learning artificial recurrent neural network (RNN) architecture.
Unlike traditional feed-forward neural networks, LSTM has feedback connections.
It can handle single data points (such as pictures) as well as full data sequences (such as speech or video).
Stock market prediction and analysis are some of the most difficult jobs to complete. There are numerous causes for this, including market volatility and a variety of other dependent and independent variables that influence the value of a certain stock in the market. These variables make it extremely difficult for any stock market expert to anticipate the rise and fall of the market with great precision.
However, with the introduction of Machine Learning and its strong algorithms, the most recent market research and Stock Market Prediction advancements have begun to include such approaches in analyzing stock market data.
In summary, Machine Learning Algorithms are widely utilized by many organizations in Stock market prediction. This article will walk through a simple implementation of analyzing and forecasting the stock prices of a Popular Worldwide Online Retail Store in Python using various Machine Learning Algorithms.
MSFT’s stocks are listed on NASDAQ and their value is updated every working day of the stock market. It should be noted that the market does not allow trading on Saturdays and Sundays, therefore there is a gap between the two dates. The Opening Value of the stock, the Highest and Lowest values of that stock on the same days, as well as the Closing Value at the end of the day, are all indicated for each date.