Review Web Data Analysis using Naive Bayes Algorithm
Sujata roniya, Smriti Dwivedi, Richa Nehra · Journal of Emerging Technologies and Innovative Research · 2021
The convergence of computing and communication has resulted in an information-based civilization. However, the majority of the information is in its most basic form: data. If data is defined as facts that have been recorded, then information is the set of patterns or expectations that exist beneath the data. Databases contain a vast amount of data that is potentially useful but has yet to be discovered or expressed. It is our mission to bring it to fruition. The extraction of implicit, previously unknown, and possibly beneficial information from data is known as data mining.The objective is to create computer algorithms that automatically comb through databases looking for regularities or patterns. If strong patterns are discovered, they will most likely generalise so that reliable predictions may be made on future data. Naturally, there will be issues. Many of the patterns will be dull and mundane. Others will be fictitious, based on chance coincidences in the dataset. The technical foundation for data mining is machine learning. It's used to extract information from databases' raw data—information that's written in a readable format and can be used for a variety of reasons. The process incorporates abstraction, which entails taking the data as-is and inferring whatever structure lies underlying it. This book covers the machine learning tools and techniques that are used in real data mining to uncover and describe structural patterns in data.