PREDICTING STOCK MARKET TRENDS WITH DATA SCIENCE

Abstract

The project on Predicting Stock Market Trends with Data Science focuses on developing a predictive model that uses historical stock data, technical indicators, and machine learning algorithms to forecast stock price movements and market trends. The stock market is inherently volatile, and investors face challenges in making informed decisions due to the complexity and unpredictability of price fluctuations. Traditional methods rely on manual analysis or basic statistical tools, which are often insufficient for accurate forecasting. This project integrates data science techniques, including data preprocessing, feature extraction, and model training, to analyse past stock behaviour and identify patterns that can predict future trends. Models such as

linear regression, decision trees, random forests, and neural networks are utilized to capture both linear and nonlinear relationships in the data. The system also implements performance evaluation metrics, such as mean squared error (MSE) and accuracy, to validate prediction models. By leveraging real-time data feeds, the system provides timely insights for investors and traders, enabling data-driven decision-making. Security and privacy are considered in storing financial data, while scalability is ensured for handling large datasets. This project demonstrates the integration of data science, machine learning, and stock market analysis to create a predictive system that can assist investors, improve trading strategies, and enhance understanding of financial market dynamics.