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+# 多元线性回归
+
+
+<p align="center">
+  <img src="https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%203.png">
+</p>
+
+
+## 第1步: 数据预处理
+
+### 导入库
+```python
+import pandas as pd
+import numpy as np
+```
+### 导入数据集
+```python
+dataset = pd.read_csv('50_Startups.csv')
+X = dataset.iloc[ : , :-1].values
+Y = dataset.iloc[ : ,  4 ].values
+```
+
+### 将类别数据数字化
+```python
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+labelencoder = LabelEncoder()
+X[: , 3] = labelencoder.fit_transform(X[ : , 3])
+onehotencoder = OneHotEncoder(categorical_features = [3])
+X = onehotencoder.fit_transform(X).toarray()
+```
+
+### 躲避虚拟变量陷阱
+```python
+X = X[: , 1:]
+```
+
+### 拆分数据集为训练集和测试集
+```python
+from sklearn.cross_validation import train_test_split
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)
+```
+## 第2步： 在训练集上训练多元线性回归模型
+```python
+from sklearn.linear_model import LinearRegression
+regressor = LinearRegression()
+regressor.fit(X_train, Y_train)
+```
+
+## Step 3: 在测试集上预测结果
+```python
+y_pred = regressor.predict(X_test)
+```
+
+