Stock Market Prediction and Forecasting Using Historical Data

Abstract

Stock market prediction is a critical area of financial research that aims to forecast future price movements of stocks using historical data, economic indicators, and advanced machine learning algorithms. This thesis focuses on utilizing the dataset titled "Stock Market Prediction & Forecasting" from Kaggle to develop predictive models capable of generating accurate market forecasts. The study investigates various methodologies, including time series analysis, feature engineering, and the application of machine learning techniques such as Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks. Results demonstrate the feasibility of achieving meaningful insights into stock price trends, enabling investors and analysts to make informed decisions.

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