StockInsights: Stock Market Prediction Using Machine Learning

Stock market prediction is difficult and important due to its fragility and volatility.Modern calculation methods often struggle to capture the nonlinear aspects and inefficiencies of financial markets.In this research, we present a new method that combines the predictive power of horizontal regression with the simplicity of support vector machine (SVM) learning algorithms to predict the stock market.We use historical market data including various economic indicators through self-reported regression models and SVM models.We then use a clustering technique to combine the predictions made by these models.Our tests on real-world stock data show that the combination outperforms the single model in terms of accuracy and robustness.The plan not only improves forecast performance but also provides valuable insight into business dynamics.Our findings highlight the potential of integrating multiple machine learning systems for effective business forecasting and thus contributing to informed business decision-making.