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.
The total volume of the stocks in the market is provided, with this information, it is up to the job of a Machine LearningData Scientist to look at the data and develop different algorithms that may help in finding appropriate stocks values. With the application of machine learning for stock market forecasts, the procedure has become much simpler.
The algorithms can reveal complex patterns characterized by non-linearity as well as some relations that are difficult to detect with linear algorithms. These algorithms also prove more effectiveness and multicollinearity than the linear regressions ones.
Our project's aim is to use ML algorithms based on LSTM to forecast the adjusted closing prices for a portfolio of assets,the main objective here is to obtain the most accurate trained algorithm, to predict future values for our portfolio.
A full stack web application is used to display the stock prediction graphs to the user. Tools & Technology Used: Python, JavaScript, ReactJS, NodeJS, MongoDB