Prediction Stock Movement by using Gradient Boosting
DOI:
https://doi.org/10.15662/IJRAI.2026.0903009Keywords:
Stock Price Prediction, Financial Forecasting, Tesla, Google, Machine Learning, Predictive Modeling, Data Analytics, Django Web ApplicationAbstract
This project presents a comprehensive study on the predictive analytics of Tesla and Google stock prices using advanced machine learning frameworks. Financial markets are highly dynamic and influenced by numerous factors, including historical performance, market sentiment, and external economic indicators. To address the complexity of stock price forecasting, this work utilizes data-driven modeling approaches capable of identifying hidden patterns and temporal trends within large volumes of historical data. The system collects and processes stock market data, followed by extensive preprocessing to ensure accuracy and consistency. Key financial indicators are extracted to enhance the quality of predictions. These features are then used to train robust machine learning models that learn from past behavior to provide future stock value estimates. A web-based interface, built using the Django framework, facilitates user interaction with the predictive system. Users can input parameters, view historical data, compare trends, and visualize predicted values through interactive graphs and charts. This interface bridges the gap between complex data science and practical financial analysis, making the system accessible to users with varying levels of technical expertise.
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