Machine Learning Based Indian Stock Market Forecasting

The financial stock market is unpredictable and nonlinear, making forecasting it difficult. A number of factors, including traders' irrational and reasonable actions as well as their psychological and emotional states, contribute to the stock market's growing volatility. Deep learning and AI-based algorithms have changed the financial stock market's movement prediction in recent years after receiving their education. Traders are using these Machine Learning (ML) approaches to help them make decisions on stock values. A model predicting the future direction of the Indian stock market's movement has been made available to the public. This model integrates historical Indian stock data from companies that have been part of the Nifty 50 from its establishment with a range of financial and social elements, such as stock-related comments and economic news items. In an attempt to improve accuracy and reduce RMSE error, this sequence of data is pre-processed, normalized, and then subjected to a diversity of machine learning (ML) techniques, including gradient boosting regression, long short-term memory (LSTM), support vector machine (SVM), K-nearest neighbor (KNN), and random forest. We are also applying a range of machine learning approaches and fine-tuning the LSTM in novel ways to improve accuracy. We are also computing metrics such as MAE and MSE. Those who invest in equities may use this tactic to halt large losses. Forecasts for the investing sector will become clearer thanks to the social indices. Tweets, trade volume, and previous closing prices will all be utilized by the LSTM network.