Visualizing and Forecasting Stocks Using Machine Learning

Computer Engineering

In the era of digital finance, predicting stock market trends and presenting market data in an intuitive manner has become increasingly important for investors and analysts. This project, Visualizing and Forecasting Stocks Using Machine Learning, aims to combine data visualization techniques with machine learning models to enhance the understanding and prediction of stock market movements. Historical stock data is collected from reliable financial APIs and preprocessed to handle missing values, normalize scales, and extract meaningful features. For visualization, dynamic and interactive charts such as candlestick plots, line graphs, and correlation heatmaps are used to help users gain insights into stock behavior over time. On the forecasting side, the project implements and compares various machine learning models, including Linear Regression, ARIMA, LSTM (Long Short-Term Memory) networks, and Random Forests, to predict future stock prices. The models are evaluated based on metrics such as RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R² Score. Our results demonstrate that deep learning models like LSTM, when properly tuned, provide more accurate predictions for time-series financial data compared to traditional methods. This project not only serves as a practical tool for financial forecasting but also as a foundation for further research in algorithmic trading and investment strategy development. The integration of visualization with predictive analytics enables better decision-making and deeper insight into market dynamics

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