A Framework for Private Hospitals Service Cost Recommendation Based on Page Ranking Technique

Abstract

The Search engine is a software program designed to identify and respond to specific questions called keywords and display relevant informational data available on the web. Thousands of websites are available but we want only specific information. The Solution is a search engine, it will find the relevant information related to that keyword and display that information in an aggregated format. With the rapid growth of data and information sources on the internet, finding the relevant and required information is becoming more tedious as well as important for internet users, for this reason, web mining is becoming popular day by day. We proposed a system for private hospital cost aggregation for hospital recommendation system by page ranking algorithm. Sample results are collected. This paper gives depictions of different web mining methodology. It gives an examination of three classifications of web mining. The page ranking algorithm assumes a noteworthy job in making the client look route less demanding in the after-effects of a web crawler. The correlation rundown of different page rank algorithms is recorded in this paper which helps in the best usage web assets by giving expected data to the guides. For performance evaluation, we have collected samples as well as real-time data set from UCI data repository and hospitals. For case 1 and case 2 implementation is done in python with numpy package and panda’s package. For Case 3 implementation is done in python as well as C#. Deep learning methodology can be applied for greater efficiency and accuracy. The improved result can be in the range of 85 to 95%.