Text classification based on machine learning

Under the era of technical surge in recent years, the weight of artificial intelligence in people's life is increasing over time. This paper will focus on classification. How to classify efficiently and accurately has become the top priority. In this research, we are trying to use four different algorithms SVM, Naive Bayes, Random Forest, and KNN to classify the text of descriptions. In the experiment, we are going to compare the result of each algorithm by prediction accuracy then summarize the various advantages and disadvantages turns out in each algorithm. The result shows that, the best model we made is Naïve Bayes which can hold for the 55% accuracy in predicting the application number by genre.