Published Paper Details:

In response to the inherent volatility and complexity of financial markets, predicting stock prices using machine learning (ML) has garnered growing interest. While traditional methods primarily rely on historical data and economic indicators, ML algorithms offer the distinct advantage of analyzing immense datasets, identifying hidden patterns, and generating more informed predictions. This research delves into the efficacy of various ML techniques in forecasting stock price fluctuations. We juxtapose the performance of diverse models, encompassing Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and ensemble methods, utilizing real-world stock data. Our analysis scrutinizes the influence of various features and model parameters on prediction accuracy, shedding light on the strengths and limitations of each approach. Additionally, we delve into the challenges associated with stock prediction using ML, encompassing data preprocessing, feature engineering, and overfitting. The findings aim to enrich the ongoing research in this domain and provide valuable insights for investors and financial analysts seeking to leverage the power of ML for informed decision-making within the stock market.