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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: June 2024

Volume 9 | Issue 6

Impact factor: 8.15

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Paper Title: Website Traffic Forecasting Using Python and Machine Learning
Authors Name: Ram Babu Jaiswal , Dr. Sheetal Kalra , Neha Bagga
Unique Id: IJSDR2405092
Published In: Volume 9 Issue 5, May-2024
Abstract: In contemporary times, websites serve as digital storefronts worldwide, comprising the largest segment of internet traffic. The forecasting of website traffic involves predicting future visitor numbers, which is beneficial for formulating marketing strategies, allocating resources, and optimizing websites. Measured in terms of sessions within a specific time frame, website traffic varies considerably based on factors such as the time of day, day of the week, and other variables. The capacity of a platform to handle web traffic depends on the size of the servers supporting it. An increase in website visitors may lead to crashes or slow loading times, resulting in potential disruptions. The accuracy of internet traffic flow forecasting is heavily reliant on historical and real-time traffic data collected from various sources that monitor network flow.
Keywords: ARIMA, machine learning, time series analysis, regression, prediction.
Cite Article: "Website Traffic Forecasting Using Python and Machine Learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 5, page no.670 - 676, May-2024, Available :http://www.ijsdr.org/papers/IJSDR2405092.pdf
Downloads: 000342234
Publication Details: Published Paper ID: IJSDR2405092
Registration ID:211362
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 670 - 676
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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