Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction

Title

Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction

Creator

Bose, Manoswita; Chatterjee, Moumita; Sarkar, Dhrubasish

Description

This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed models user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Source

Lecture Notes in Networks and Systems;Volume;1488 LNNS;pp.497-507

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Analytics; Prediction; Stocks; Visualization

Coverage

Bose M., CHRIST (Deemed to be University), Bengaluru, India; Chatterjee M., Aliah University, Kolkata, India; Sarkar D., Supreme Institute of Management and Technology, West Bengal, Hooghly, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981968308-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-8309-3_35

https://www.scopus.com/pages/publications/105028271532?origin=resultslist