Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)

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Stock Price Prediction: An Integrated Approach

Authors
Gaurav K. Poojary1, *
1Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Vishwothama Nagar, Bantakal, 574115, Udupi, India
*Corresponding author. Email: gauravpoojary893@gmail.com
Corresponding Author
Gaurav K. Poojary
Available Online 16 June 2025.
DOI
10.2991/978-94-6463-762-5_17How to use a DOI?
Keywords
Machine learning; KNeighbors Regressor; Linear Regression; XGBoost Regressor; Extra Trees Regressor
Abstract

This work presents a stock price prediction system that combines market trend analysis and sentiment insights using modern machine learning methods. The primary goal is to provide accurate short-term stock price forecasts by integrating historical stock data, technical indicators, and sentiment analysis from news and social platforms. A user-friendly web application built with Streamlit allows users to explore stock trends, review technical indicators (Bollinger Bands, MACD, RSI, SMA, EMA), and access machine learning-based predictions. The app also displays the latest stock updates, including the most recent data points, ensuring informed decision-making. The system employs multiple machine learning models, including Linear Regression, Random Forest, Extra Trees, KNeighbors, and XGBoost Regressors, trained on historical stock data and technical indicators. Additionally, sentiment analysis of financial news and social media provides a holistic view of market conditions. By combining sentiment insights, technical data, and machine learning, this work offers a powerful tool for traders and investors, delivering reliable and timely stock price forecasts.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
Series
Advances in Engineering Research
Publication Date
16 June 2025
ISBN
978-94-6463-762-5
ISSN
2352-5401
DOI
10.2991/978-94-6463-762-5_17How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Gaurav K. Poojary
PY  - 2025
DA  - 2025/06/16
TI  - Stock Price Prediction: An Integrated Approach
BT  - Proceedings of the International Conference on Materials, Energy, Environment & Manufacturing Sciences & Computational Intelligence and Smart Communication (MEEMS-CISC-2024)
PB  - Atlantis Press
SP  - 177
EP  - 186
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-762-5_17
DO  - 10.2991/978-94-6463-762-5_17
ID  - Poojary2025
ER  -