#https://pythonprogramming.net/sklearn-scikit-learn-nltk-tutorial/




# # Random Forest Classification
# import pandas
# from sklearn import model_selection
# from sklearn.ensemble import RandomForestClassifier
# url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
# names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
# dataframe = pandas.read_csv(url, names=names)
# array = dataframe.values
# X = array[:,0:8]
# Y = array[:,8]
# seed = 7
# num_trees = 100
# max_features = 3
# kfold = model_selection.KFold(n_splits=10, random_state=seed)
# model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)
# results = model_selection.cross_val_score(model, X, Y, cv=kfold)
# print(results.mean())
