47 lines
1.2 KiB
Markdown
47 lines
1.2 KiB
Markdown
# 简单线性回归模型
|
|
|
|
|
|
<p align="center">
|
|
<img src="https://github.com/MachineLearning100/100-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')
|
|
plt.show()
|
|
```
|
|
## 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')
|
|
plt.show()
|
|
```
|