Theoretical Background
Machine learning is the development of algorithms that permit machines to learn. ML has been used in medical diagnosis, bioinformatics, Money fraud, stock market analysis, classifying DNS, speech recognition, computer games, and spam filtering (Bhuiyan et al., 2018),(R Manikandan, 2018).
Neural Network (NN) is a beautiful biologically inspired programming paradigm, which enables a computer to learn from observational data. Currently, the NN algorithm used widely in many problems, such as text categorizations, image, and speech recognition.
However, extracting the emails and classify them needs knowledge of Natural Language Processing (NLP) to normalize the datasets, extract and select the features to feed the classifiers (Ndumiyana, Magomelo, and Sakala, 2013)(Jayanthi and Subhashini, 2016).
NN has more efficiency in detecting spam because its supervised learning method and also errors can be corrected NB, DT, SVM, KNN are also good classifiers (Sharma, 2014).
The study will use BPNN to improve accuracy and performance in detecting email spam.