From b534b422e9d82395bacdbdb46f9e55a8e13c0af5 Mon Sep 17 00:00:00 2001
From: wengJJ <31797645+wengJJ@users.noreply.github.com>
Date: Tue, 7 Aug 2018 12:37:09 +0800
Subject: [PATCH] Update Day 11 K-NN.md

---
 Code/Day 11 K-NN.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/Code/Day 11 K-NN.md b/Code/Day 11 K-NN.md
index 2afaa1a..61375e4 100644
--- a/Code/Day 11 K-NN.md	
+++ b/Code/Day 11 K-NN.md	
@@ -1,54 +1,54 @@
-# K-Nearest Neighbors (K-NN)
+# K近邻法 (K-NN)
 
 <p align="center">
-  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg">
+  <img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg">
 </p>
 
-## The DataSet | Social Network 
+## 数据集 | 社交网络
 
 <p align="center">
   <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/data.PNG">
 </p> 
 
 
-## Importing the libraries
+## 导入相关库
 ```python
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 ```
 
-## Importing the dataset
+## 导入数据集
 ```python
 dataset = pd.read_csv('Social_Network_Ads.csv')
 X = dataset.iloc[:, [2, 3]].values
 y = dataset.iloc[:, 4].values
 ```
 
-## Splitting the dataset into the Training set and Test set
+## 将数据划分成训练集和测试集
 ```python
 from sklearn.cross_validation import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 ```
-## Feature Scaling
+## 特征缩放
 ```python
 from sklearn.preprocessing import StandardScaler
 sc = StandardScaler()
 X_train = sc.fit_transform(X_train)
 X_test = sc.transform(X_test)
 ```
-## Fitting K-NN to the Training set
+## 使用K-NN对训练集数据进行训练
 ```python
 from sklearn.neighbors import KNeighborsClassifier
 classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
 classifier.fit(X_train, y_train)
 ```
-## Predicting the Test set results
+## 对测试集进行预测
 ```python
 y_pred = classifier.predict(X_test)
 ```
 
-## Making the Confusion Matrix
+## 生成混淆矩阵
 ```python
 from sklearn.metrics import confusion_matrix
 cm = confusion_matrix(y_test, y_pred)