Please use this identifier to cite or link to this item:
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2294
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Patwal, Pratap Singh | - |
dc.contributor.author | Srivastava, Anoop Kumar | - |
dc.date.accessioned | 2023-12-09T08:56:05Z | - |
dc.date.available | 2023-12-09T08:56:05Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | pp. 1-5 | en_US |
dc.identifier.isbn | 9781665456272 | - |
dc.identifier.uri | https://doi.org/10.1109/ICAIA57370.2023.10169347 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2294 | - |
dc.description.abstract | Accurate prediction of stock market price is highly challenging. This paper presents a proposed model for prediction of stock market price of Netflix. We have considered a five-year data set (April, 2017 - April, 2022) of Netflix. An Exploratory Data Analysis (EDA) of Netflix's stock price data for predicting its stock market prices using time series is done. The implementation of the model is done using Python language. We imported five-years data and applied several techniques: importing libraries, calculating stock return, line plot, plot all, plot return year wise, plot histogram, plot kernel density, plot box plot, differencing method, resample daily to monthly data etc. EDA proved that using time series technique achieved better results in prediction of stock price and visualizing. © 2023 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.publisher | 2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023 - Proceeding | en_US |
dc.subject | Kernel density | en_US |
dc.subject | Matplotlib | en_US |
dc.subject | Pandas | en_US |
dc.subject | Prediction | en_US |
dc.subject | Seaborn | en_US |
dc.subject | Stock | en_US |
dc.subject | Time series | en_US |
dc.title | Proposed Model For Prediction of Stock Market Price of Netflix | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.