diff --git a/KNN self-learning.py b/KNN self-learning.py
new file mode 100644
index 0000000..478cfc0
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+++ b/KNN self-learning.py	
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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul  1 20:31:05 2019
+
+@author: z
+"""
+###KNN code self-leaerning(self-made KNN algorithm)
+import numpy as np
+from math import sqrt
+import warnings
+from collections import Counter
+import pandas as pd
+import random
+
+def k_nearest_neighbours(data, predict, k=5):
+    if len(data) >= k:
+        warnings.warn('K is set to a value less than the total number of all voting groups!')
+        distances = [] ##Here we use Euclidean distances for convenience reasons
+        for group in data:
+            for features in data[group]:
+                euclidean_distance = np.linalg.norm(np.array(features)-np.array(predict))
+                distances.append([euclidean_distance, group])
+    votes = [i[1] for i in sorted(distances)[:k]]
+    vote_result = Counter(votes).most_common(1)[0][0]
+    confidence = Counter(votes).most_common(1)[0][1] / k
+    
+    return vote_result, confidence
+
+accuracies = []
+for i in range(25):
+    df = pd.read_csv("breast-cancer-wisconsin.data.txt")
+    df.replace('?', -999999, inplace=True)  ###try to make '?' as an outlier 
+    df.drop(['id'], 1, inplace=True)
+    full_data = df.astype(float).values.tolist()
+    random.shuffle(full_data)
+    
+    test_size = 0.2
+    train_set = {2:[], 4:[]}
+    test_set = {2:[], 4:[]}
+    train_data = full_data[:-int(test_size*len(full_data))]
+    test_data = full_data[-int(test_size)*len(full_data):]
+    
+    for i in train_data:
+        train_set[i[-1]].append(i[:-1])
+    for i in test_data:
+        test_set[i[-1]].append(i[:-1])   
+    
+    correct = 0
+    total = 0 
+    
+    for group in test_set:
+        for data in test_set[group]:
+             vote,Confidence = k_nearest_neighbours(train_set, data, k=5)
+             if group == vote:
+                 correct += 1
+             else:
+             total += 1
+            
+    print('Accuracy of self-made KNN algorithm is:', correct/total)
+    accuracies.append(correct/total)
+
+print(sum(accuracies)/len(accuracies))
+
+
+
diff --git a/KNN using scikit-learn(traditional version).py b/KNN using scikit-learn(traditional version).py
new file mode 100644
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--- /dev/null
+++ b/KNN using scikit-learn(traditional version).py	
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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul  1 21:44:33 2019
+
+@author: z
+"""
+
+import numpy as np
+from sklearn import preprocessing, neighbors
+from sklearn.model_selection import train_test_split
+import pandas as pd
+
+accuracies = []
+for i in range(25):
+    df = pd.read_csv("breast-cancer-wisconsin.data.txt")
+    df.replace('?', -999999, inplace=True)  ###try to make '?' as an outlier 
+    df.drop(['id'], 1, inplace=True)
+    
+    X = np.array(df.drop(['class'], 1))
+    y = np.array(df['class'])
+    
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+    
+    clf = neighbors.KNeighborsClassifier()
+    clf.fit(X_train, y_train)
+    
+    accuracy = clf.score(X_test, y_test)
+    
+###just a small example:    
+    example = np.array([[4,2,1,1,1,2,3,2,1], [4,2,1,2,2,2,3,2,1]])
+    example_reshape = example.reshape(len(example), -1)
+    prediction = clf.predict(example_reshape)
+    
+    accuracies.append(accuracy)
+    
+print(sum(accuracies)/len(accuracies))
+    
+
+
+