STOCK MARKET TREND VISUALIZATION USING DATA VISUALIZATION

Abstract

This project focuses on visualizing stock market trends using Python data visualization libraries such as Matplotlib and Seaborn. Stock market data often contains large amounts of numerical information, which can be difficult to interpret in raw form. The main objective of this project is to transform stock market data into meaningful graphical representations to better understand price movements and trading patterns. The dataset used in this project includes attributes such as date, open price, high price, low price, close price, and trading volume. Using Python libraries like Pandas, Matplotlib, and Seaborn, various visualizations such as line charts, bar charts, pie charts, moving average graphs, and correlation heatmaps are generated to analyze stock trends. The visualizations help identify patterns in stock prices, market fluctuations, and trading activity. This project demonstrates how data visualization techniques can simplify complex financial data and make it easier to analyze stock market behavior. The results highlight the usefulness of visualization tools in understanding stock market trends and supporting data-driven analysis.