--- a
+++ b/benchmark.py
@@ -0,0 +1,40 @@
+# import all necessary libraries
+import pandas
+from pandas.tools.plotting import scatter_matrix
+from sklearn import cross_validation
+from sklearn.metrics import matthews_corrcoef
+from sklearn.metrics import classification_report
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import accuracy_score
+
+# load the dataset (local path)
+url = "data.csv"
+# feature names
+features = ["MDVP:Fo(Hz)","MDVP:Fhi(Hz)","MDVP:Flo(Hz)","MDVP:Jitter(%)","MDVP:Jitter(Abs)","MDVP:RAP","MDVP:PPQ","Jitter:DDP","MDVP:Shimmer","MDVP:Shimmer(dB)","Shimmer:APQ3","Shimmer:APQ5","MDVP:APQ","Shimmer:DDA","NHR","HNR","RPDE","DFA","spread1","spread2","D2","PPE","status"]
+dataset = pandas.read_csv(url, names = features)
+
+# store the dataset as an array for easier processing
+array = dataset.values
+# X stores feature values
+X = array[:,0:22]
+# Y stores "answers", the flower species / class (every row, 4th column)
+Y = array[:,22]
+validation_size = 0.3
+# randomize which part of the data is training and which part is validation
+seed = 7
+# split dataset into training set (80%) and validation set (20%)
+X_train, X_validation, Y_train, Y_validation = cross_validation.train_test_split(X, Y, test_size = validation_size, random_state = seed)
+
+# 10-fold cross validation to estimate accuracy (split data into 10 parts; use 9 parts to train and 1 for test)
+num_folds = 10
+num_instances = len(X_train)
+seed = 7
+# use the 'accuracy' metric to evaluate models (correct / total)
+scoring = 'accuracy'
+
+predictions = []
+for instance in X_validation:
+    predictions.append(1)
+
+print(accuracy_score(Y_validation, predictions)*100)
+print(matthews_corrcoef(Y_validation, predictions))