版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/yangyang_yangqi/article/details/82691480
数据集：
Hours,Scores
2.5,21
5.1,47
3.2,27
8.5,75
3.5,30
1.5,20
9.2,88
5.5,60
8.3,81
2.7,25
7.7,85
5.9,62
4.5,41
3.3,42
1.1,17
8.9,95
2.5,30
1.9,24
6.1,67
7.4,69
2.7,30
4.8,54
3.8,35
6.9,76
7.8,86
数据预处理常规步骤：
导入相关库
导入数据集
检查缺失值
划分数据集
特征缩放
#_*_coding:utf-8_*_
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.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1.0/4, random_state = 0)
#注意：Python2.x 1/4=0 1.0/4=0.25 Python3.x 1/4=0.25
# Fitting Simple Linear Regression Model to the training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
#拟合模型
regressor = regressor.fit(X_train, Y_train)
# Predecting the Result
Y_pred = regressor.predict(X_test)
# Visualising the Training results
plt.scatter(X_train , Y_train, color = 'red')
plt.plot(X_train , regressor.predict(X_train), color ='blue')
# Visualizing the test results
plt.scatter(X_test , Y_test, color = 'yellow')
plt.plot(X_test , regressor.predict(X_test), color ='green')
plt.show()