'''decision tree''' from sklearn.tree import DecisionTreeClassifier as DTC dtc = DTC(criterion = 'entropy') dtc.fit(x,y) '''Artificial neural network ANN''' from keras.models import Sequential from keras.layers.core import Dense, Activation model = Sequential() #set up a model model.add(Dense(2,input_dim = 10)) #output_dim is 2, input_dim is 10 model.add(Activation('relu')) model.add(Dense(1, input_dim = 2)) model.add(Activation('sigmoid')) #for 0-1 output model.compil(loss = , optimizer = , class_mode = ) model.fit(x, y , nb_epoch = 1000 ,) #train 1000 times model.predict_classes(x) '''K-means''' data = 1.0 * (data - data.mean())/data.std() #standardizing data from sklearn.cluster import KMeans model = KMeans(n_cluster = 4, n_jobs = 4, max_iter = 1000) model.fit(data) r1 = pd.Series(model.labels_).value_counts() r2 = pd.DataFrame(model.cluster_centers_) r = pd.concat([r2,r1], axis = 1) #get the result matrix '''clustering visualization tool TSNE'''