Website Traffic Forecasting Using Python and Machine Learning
Ram Babu Jaiswal
, Dr. Sheetal Kalra , Neha Bagga
ARIMA, machine learning, time series analysis, regression, prediction.
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.
"Website Traffic Forecasting Using Python and Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 5, page no.670 - 676, May-2024, Available :https://ijsdr.org/papers/IJSDR2405092.pdf
Volume 9
Issue 5,
May-2024
Pages : 670 - 676
Paper Reg. ID: IJSDR_211362
Published Paper Id: IJSDR2405092
Downloads: 000347436
Research Area: Computer Science & Technology
Country: Phagwara, Punjab, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
