A Scalable Analytics System for Live Stock Market

Abstract

In the fast-paced world of financial
markets, the ability to analyze stock
market trends in real-time is crucial for
making informed investment decisions.
This study explores the application of
machine learning algorithms, including
XGBoost, Decision Trees (DT), and KNearest
Neighbors (KNN), for predicting
stock market trends based on historical
data and real-time market indicators. By
leveraging these machine learning
models, we aim to predict stock price
movements, identify trends, and detect
anomalies that could indicate potential
market shifts. The integration of these
algorithms with Power BI, a powerful
data visualization and business analytics
tool, allows for real-time analysis and
dynamic dashboard reporting. This
system processes large datasets from
various market sources and presents
actionable insights through interactive
Power BI dashboards, enabling investors
to make data-driven decisions. The
effectiveness of the proposed approach is
evaluated based on performance metrics
such as accuracy, precision, and recall.
Results demonstrate the potential of
machine learning in financial
forecasting, improving decision-making
processes and offering a competitive edge
in the stock market.