Predicting the Ranking of Web Page on SERP by Applying Machine Learning Techniques

Garg, Shubham (2022) Predicting the Ranking of Web Page on SERP by Applying Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Analyzing the rank of web pages related to the query is always been a problem and many organization work for it to improve the ranking in search engine result page. It depends on more than 200’s of factors but no one can justify its original algorithm through which it can be enhanced quickly with correct input. It changes regularly based on the meta data behind the web page but also it depends upon the no. of visitors on web page, total no. of time they spend on it etc. this project aim to predict the ranking on the basis of meta data , title and snippet of web page. The proposed architecture is Recurrent Neural Network(RNN) by using the Long Short Term Memory(LSTM). and after applying the proposed model it achieve the accuracy of 64%. with the loss of 0.93. Their are several other models have been compiled but no can get the accuracy of more than 64%. The model has been compared in the reference of Dense Layer and weights. The batch sizes and Epochs have been changed to achieve more accuracy. Along with this the EDA is also performed where it shows that how the ranking of web page impact on traffic visiting the websites.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	24 Jan 2023 15:27
Last Modified:	03 Mar 2023 12:18
URI:	https://norma.ncirl.ie/id/eprint/6121

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