|
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter
Copyright Infringement Claims
Indexing Partner
|
Published Paper Details
|
|
| Paper Title: | MACHINE LEARNING BASED SYSTEM TO PREDICTING STOCK MARKET TRENDS VIA CONTINUES AND BINARY DATA |
| Authors Name: | Miss. Jamdhade Akshda J , Miss. Thakre Priyanka R , Miss. Jadhav Priyanka B , Miss. Jagzap Payal C , Prof. Patil P. A |
| Unique Id: | IJSDR2205027 |
| Published In: | Volume 7 Issue 5, May-2022 |
| Abstract: | The main objective of this project is to find the best model to predict the value of the stock market. During the process of considering various techniques and variables that must be taken into account, We found out that techniques like random forest, support vector machine were not exploited fully. In, this project we are going to present and review a more feasible method to predict the stock movement with higher accuracy. The first thing we have taken into account is the dataset of the stock market prices from previous year. The dataset was preprocessed and tuned up for real analysis. Hence, our project will also focus on data pre-processing of the raw dataset. Secondly, after pre processing the data, we will review the use of random forest, support vector machine on the dataset and the outcomes it generates. In addition, the proposed project examines the use of the prediction system in real-world settings and issues associated with the accuracy of the overall values given. The project also presents a machine-learning model to predict the longevity of stock in a competitive market. The successful prediction of the stock will be a great asset for the stock market institutions and will provide real-life solutions to the problems that stock investors face. |
| Keywords: | Stocks, Machine Learning, Processing, Dataset, Support Vector Machine, Database, Investor. |
| Cite Article: | "MACHINE LEARNING BASED SYSTEM TO PREDICTING STOCK MARKET TRENDS VIA CONTINUES AND BINARY DATA", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.7, Issue 5, page no.140 - 145, May-2022, Available :http://www.ijsdr.org/papers/IJSDR2205027.pdf |
| Downloads: | 000337070 |
| Publication Details: | Published Paper ID: IJSDR2205027 Registration ID:200370 Published In: Volume 7 Issue 5, May-2022 DOI (Digital Object Identifier): Page No: 140 - 145 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijsdr.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 |
Track Paper
Important Links
Conference Proposal
ISSN
 |
 |
DOI (A digital object identifier)
  Providing A digital object identifier by DOI How to GET DOI and Hard Copy Related |
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
 |
 |
 |
Social Media
Indexing Partner |
|||
| Copyright © 2024 - All Rights Reserved - IJSDR | |||
Facebook Twitter Instagram LinkedIn