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+# 简单线性回归模型
+
+
+<p align="center">
+  <img src="https://github.com/wengJJ/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg">
+</p>
+
+
+# 第一步：数据预处理
+```python
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+dataset = pd.read_csv('studentscores.csv')
+X = dataset.iloc[ : ,   : 1 ].values
+Y = dataset.iloc[ : , 1 ].values
+
+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) 
+```
+
+# 第二步：训练集使用简单线性回归模型来训练
+ ```python
+ from sklearn.linear_model import LinearRegression
+ regressor = LinearRegression()
+ regressor = regressor.fit(X_train, Y_train)
+ ```
+ # 第三步：预测结果
+ ```python
+ Y_pred = regressor.predict(X_test)
+ ```
+ 
+ # 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')
+ ```
+ ## 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')
+ ``` 
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 <p align="center">
   <img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%201.jpg">
 </p>
+
+## 简单线性回归模型 | [第2天](https://github.com/wengJJ/100-Days-Of-ML-Code/blob/master/Code/Day2_Simple_Linear_Regression.md)
+
+<p align="center">
+  <img src="https://github.com/wengJJ/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%202.jpg">
+</p>
+