  Today text classification becomes critical task for concerned individuals for
numerous purposes. Hence, several researches have been conducted to develop
automatic text classification for national and international languages.
However, the need for an automatic text categorization system for local
languages is felt. The main aim of this study is to establish a Pashto
automatic text classification system. In order to pursue this work, we built a
Pashto corpus which is a collection of Pashto documents due to the
unavailability of public datasets of Pashto text documents. Besides, this study
compares several models containing both statistical and neural network machine
learning techniques including Multilayer Perceptron (MLP), Support Vector
Machine (SVM), K Nearest Neighbor (KNN), decision tree, gaussian na\"ive Bayes,
multinomial na\"ive Bayes, random forest, and logistic regression to discover
the most effective approach. Moreover, this investigation evaluates two
different feature extraction methods including unigram, and Time Frequency
Inverse Document Frequency (IFIDF). Subsequently, this research obtained
average testing accuracy rate 94% using MLP classification algorithm and TFIDF
feature extraction method in this context.
