From 6e2e608f4a7a2eab299716b718ed401d6666bfb7 Mon Sep 17 00:00:00 2001
From: wengJJ <31797645+wengJJ@users.noreply.github.com>
Date: Mon, 6 Aug 2018 09:18:05 +0800
Subject: [PATCH] Update Day2_Simple_Linear_Regression.md

---
 Code/Day2_Simple_Linear_Regression.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/Code/Day2_Simple_Linear_Regression.md b/Code/Day2_Simple_Linear_Regression.md
index e420646..59365c1 100644
--- a/Code/Day2_Simple_Linear_Regression.md
+++ b/Code/Day2_Simple_Linear_Regression.md
@@ -2,11 +2,11 @@
 
 
 <p align="center">
-  <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg">
+  <img src="https://github.com/wengJJ/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg">
 </p>
 
 
-# Step 1: Data Preprocessing
+# 第一步：数据预处理
 ```python
 import pandas as pd
 import numpy as np
@@ -20,24 +20,24 @@ from sklearn.cross_validation import train_test_split
 X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) 
 ```
 
-# Step 2: Fitting Simple Linear Regression Model to the training set
+# 第二步：训练集使用简单线性模型来训练
  ```python
  from sklearn.linear_model import LinearRegression
  regressor = LinearRegression()
  regressor = regressor.fit(X_train, Y_train)
  ```
- # Step 3: Predecting the Result
+ # 第三步：预测结果
  ```python
  Y_pred = regressor.predict(X_test)
  ```
  
- # Step 4: Visualization 
- ## Visualising the Training results
+ # 第四步：可视化 
+ ## 训练集结果可视化
  ```python
  plt.scatter(X_train , Y_train, color = 'red')
  plt.plot(X_train , regressor.predict(X_train), color ='blue')
  ```
- ## Visualizing the test results
+ ## 测试集结果可视化
  ```python
  plt.scatter(X_test , Y_test, color = 'red')
  plt.plot(X_test , regressor.predict(X_test), color ='blue')