RECOMMENDATION SYSTEM FOR BSNL NETWORK IN SSA SRINAGAR

Abstract

Recommender is a Web service that recommends users using their browsing patterns for the type of material they prefer. It tries to forecast a user's preferences or ratings for an item. It leverages the web mining idea, which includes a collection of data mining techniques employed to gather usable knowledge and implicit information from web data. My research is built on a system of collaborative advice. It delivers better result than content filter algorithm. I use a collaborative filtering algorithm. I propose a BSNL telecom network recommendation system based on a variety of collaborative algorithms applied for BSNL networking sites' complicated data set. Experiments are carried out to examine the varying effects of various algorithms on a dataset supplied from BSNL at SSA Srinagar BTS locations on recommended system performance and accuracy. I have identified the high traffic locations and the channel traffic rate. I evaluate SDCCH block calls sites, cell use, SDCCH dropping percentage, total site calls, TCH drop, total cell derivatives. During the analysis of this data set of BSNL telecommunications websites, I found the losing sites, the biggest income sites and those typically downloaded sites (non-working sites).