  The careful examination of sacred texts gives valuable insights into human
psychology, different ideas regarding the organization of societies as well as
into terms like truth and God. To improve and deepen our understanding of
sacred texts, their comparison, and their separation is crucial. For this
purpose, we use our data set has nine sacred scriptures. This work deals with
the separation of the Quran, the Asian scriptures Tao-Te-Ching, the Buddhism,
the Yogasutras, and the Upanishads as well as the four books from the Bible,
namely the Book of Proverbs, the Book of Ecclesiastes, the Book of
Ecclesiasticus, and the Book of Wisdom. These scriptures are analyzed based on
the natural language processing NLP creating the mathematical representation of
the corpus in terms of frequencies called document term matrix (DTM). After
this analysis, machine learning methods like supervised and unsupervised
learning are applied to perform classification. Here we use the Multinomial
Naive Bayes (MNB), the Super Vector Machine (SVM), the Random Forest (RF), and
the K-nearest Neighbors (KNN). We obtain that among these methods MNB is able
to predict the class of a sacred text with an accuracy of about 85.84 %.
