The document describes a project to predict forest cover type using machine learning models. It analyzes data on forest cover types in Colorado using random forest, naive bayes, decision tree, support vector, and DNN classifiers. Random forest performed best with an accuracy of 82.4%, while decision tree achieved 67% accuracy. The random forest model was determined to be best suited for predicting forest cover type from the given data.