International Research Journal of Education and Technology

Stock market prediction has garnered significant interest in finance and technology due to its potential for guiding investment decisions and portfolio management. Machine learning (ML) offers a robust framework to analyze vast amounts of data, uncover patterns, and improve predictive accuracy. This paper explores the methodologies for stock market prediction using ML techniques, focusing on both traditional and deep learning models.The process begins with defining prediction objectives, such as price forecasting or trend classification, and determining appropriate time horizons. Historical market data, technical indicators, macroeconomic variables, and sentiment analysis from news and social media are leveraged to enhance predictive insights. Preprocessing steps, including feature engineering, normalization, and handling missing values, are critical for ensuring data quality.Various machine learning algorithms, ranging from regression models and support vector machines to advanced deep learning architectures like Long Short-Term Memory (LSTM) networks and transformers, are discussed. Ensemble methods are highlighted for their ability to combine multiple models to improve performance.Challenges, including market volatility, overfitting, and the integration of external data sources, are addressed alongside optimization techniques like hyperparameter tuning and feature selection.

Key Words:Key evaluation metrics, such as mean squared error, accuracy, and F1 scores, are utilized to assess model efficacy.