--- a
+++ b/knn.py
@@ -0,0 +1,48 @@
+# import all necessary libraries
+import pandas
+import sklearn
+from sklearn.model_selection import cross_validate,cross_val_score,train_test_split
+from sklearn.metrics import matthews_corrcoef
+from sklearn.metrics import classification_report
+from sklearn.metrics import confusion_matrix
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import MinMaxScaler
+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
+scaler = MinMaxScaler(feature_range=(0,1))
+scaled = scaler.fit_transform(array)
+# X stores feature values
+X = scaled[:,0:22]
+# Y stores "answers", the flower species / class (every row, 4th column)
+Y = scaled[:,22]
+validation_size = 0.25
+# 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 = train_test_split(X, Y, test_size = validation_size, random_state = seed)
+print(X_train)
+# 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'
+
+results = []
+clf = KNeighborsClassifier()
+kfold = sklearn.model_selection.KFold(n_splits=num_instances,random_state = seed)
+cv_results = cross_val_score(clf, X_train, Y_train, cv = kfold, scoring = scoring)
+clf.fit(X_train, Y_train)
+predictions = clf.predict(X_validation)
+print("KNN")
+print(accuracy_score(Y_validation, predictions)*100)
+print(matthews_corrcoef(Y_validation, predictions))
+print(classification_report(Y_validation, predictions))