### Projects for Machine Learning ## Before running---- # Package requirement # Decision Tree: Rpart; Neural Network (NN): neuralnet Boosting: gbm Support Vector Machine(SVM): e1071 k-Nearest Neighbors (KNN): class ## Start to Run ------ ##Scripts: (Rscript *.R ) Decision_Tree.R: for decision tree Boost.R: for boosting method KNN.R: for k nearest method NN.R: for neural network SVM.R: for SVM # Data test.csv: testing data, about 25% of origianl train data train.csv: training data, about 75% of original train data Original_train.csv: this is the original file of trainning data. I split a certain proportion of this as the train data (train.csv), while keep others as test (test.csv) ## Function format_output.R: R function for format the output process_input.R: R function for prepare the input #### Attention: I am using test.csv and train.csv in this directory as my default input. In my report, I also compared the effect of different sample size. If you want to run that analysis, you should replace files in following directory with file in current direcotry ## data66 test.csv: testing data, about 33% of origianl train data train.csv: training data, about 66% of original train data ## data50 test.csv: testing data, about 50% of origianl train data train.csv: training data, about 50% of original train data