Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2294
Title: Proposed Model For Prediction of Stock Market Price of Netflix
Authors: Patwal, Pratap Singh
Srivastava, Anoop Kumar
Keywords: Kernel density
Matplotlib
Pandas
Prediction
Seaborn
Stock
Time series
Issue Date: 2023
Publisher: 2023 International Conference on Artificial Intelligence and Applications, ICAIA 2023 and Alliance Technology Conference, ATCON-1 2023 - Proceeding
Citation: pp. 1-5
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.
URI: https://doi.org/10.1109/ICAIA57370.2023.10169347
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2294
ISBN: 9781665456272
Appears in Collections:Conference Papers

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