Binary classification for teacher donor's project

Classification always plays an important role in statistical machine learning, which contains both binary classification problems and multi-label classification problems. This article focuses on binary classification models including natural language processing for text objects to help teachers to improve their chances of being funded based on real data sets collected by DonorsChoose.org. Comparing about two natural language processing methods for projects proposals proposed by teachers, we also implement various statistical algorithms on our data sets, aiming to enhance the classification accuracy which can be measured by model accuracy and the area under the curve(AUC). In conclusion, the text objects are important for computer to conduct supervised learning and the length of the proposal and the price column are the crucial features. In addition, the best model will be the LightBGM with AUC 0.77 and accuracy 86%.