 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21653
Article Published : 8541
Total Authors : 22459
Total Reviewer : 811
Total Countries : 159
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Stock Market Price Prediction Using Deep Learning |
| Authors Name: | Prachi Brahmankar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_204881
|
| Published Paper Id: | IJRTI2507017 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | |
| Abstract: | Predicting stock market prices can be tricky because the markets are so unpredictable. This paper introduces a stock market price analysis system that uses Long Short-Term Memory (LSTM) networks to sift through historical stock data and predict future trends. The goal is to create a user-friendly platform where users can easily look up stocks, visualize price trends, keep track of their browsing history, and save key predictions. The system has three main parts: frontend, backend, and deep learning model. The frontend, designed with React.js, delivers a smooth and engaging experience, featuring a search bar, stock visualization, and a personalized browsing history. The backend, which runs on Node.js and Express.js, ensures everything works smoothly between the user interface and the predictive model through RESTful APIs and WebSocket’s for real-time updates. To speed things up, a caching mechanism (Redis) is used, while a user database stores preferences and saved forecasts. At the core of the system is the LSTM-based deep learning model, which processes historical stock data to make pretty accurate predictions about future prices. This means users can make smarter investment choices based on insights powered by AI. The system is hosted on Vercel, ensuring it’s both scalable and reliable. This work shows how deep learning can be paired with an interactive user experience to improve stock market analysis and make financial forecasting more accessible and data driven. |
| Keywords: | |
| Cite Article: | "Stock Market Price Prediction Using Deep Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a132-a141, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507017.pdf |
| Downloads: | 000518 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2507017
Registration ID:204881
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: a132-a141 Country: Wagholi, Pune, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507017 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507017 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||