Abstract
Millions of web pages carrying massive amounts of data make up the World Wide Web. Real-time data has been generated on a wide scale on the websites. However, not every piece of data is relevant to the user. While scouring the web for information, a user may come upon a web page that contains irrelevant or incomplete information. As a response, search engines can alleviate this issue by displaying the most relevant pages. Two web page ranking algorithms are proposed in this study along with the Dijkstra algorithm; the PageRank algorithm and the Weighted PageRank algorithm. The algorithms are used to evaluate a web page's importance or relevancy within a network, such as the Internet. PageRank evaluates a page's value based on the quantity and quality of links leading to it. It is commonly utilized by nearly all search engines around the world to rank web pages in order of relevance. This algorithm is used by Google, the most widespread Internet search engine. In the process of Web mining, page rank is quite weighty. The most important component of marketing is online use mining, which investigates how people browse and operate a business on a company's website. The study presents two proposed models that try to optimize web links and improve search engine results relevancy for users.