Current Volume 9
Abstract
This project presents “Machine Learning-Based Nifty 50 Stock Prediction and Analysis Dashboard”, a web-based intelligent financial analytics system designed to predict and analyze the movements of the Nifty 50 stock market index. The system combines machine learning techniques, technical indicator engineering, and interactive data visualization to support both daily and intraday market forecasting. The application is developed using Python technologies such as pandas, NumPy, scikit-learn, Dash, Plotly, and yfinance. The primary objective of the project is to provide an integrated platform that can collect historical market data, preprocess it, generate technical indicators, train predictive machine learning models, and display the results through an interactive dashboard. The system supports two prediction workflows: a daily prediction model for long-term trend analysis and a one-minute intraday prediction model for short-term tactical forecasting. These models use lagged market features, rolling averages, volatility measures, and technical indicators to improve forecasting accuracy. Several regression algorithms, including Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, Decision Tree Regressor, Random Forest Regressor, and Gradient Boosting Regressor, are implemented and compared using evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² Score. The trained models are serialized using joblib and integrated into a Dash-based web application for real-time inference and visualization. The dashboard enables users to view historical market trends, predicted prices, technical charts, and analytical insights through an easy-to-use interface. In addition, the system supports PDF-based exploratory data analysis reports for offline analysis and documentation. The project demonstrates how machine learning and interactive visualization can be combined to create a practical decision-support system for financial market analysis and forecasting.
Citations
IRE Journals:
R. Magesh, R. Saravanan "Machine Learning-Based Nifty 50 Stock Prediction and Analysis Dashboard" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2825-2836 https://doi.org/10.64388/IREV9I11-1717939
IEEE:
R. Magesh, R. Saravanan
"Machine Learning-Based Nifty 50 Stock Prediction and Analysis Dashboard" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717939
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