Support Vector Regression (SVR) has emerged as a potent method for stock market forecasting. SVR is a machine learning technique capable of capturing complicated nonlinear correlations in financial data, as opposed to typical linear models. When used to forecast the stock market, SVR uses historical stock prices, trade volumes, and other important financial information to build a predictive model. SVR excels at managing both bullish and bearish trends by locating the best hyperplane that maximises the margin between forecast values and actual market data. Its capacity to adapt to shifting market circumstances and detect subtle trends makes it a vital tool for traders and investors looking to make educated judgements in the volatile world of stock markets.

You can run the jupyter notebook or run it on google collab