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