机器学习100天-day2简单线性回归

机器学习100天-day2简单线性回归

一，数据预处理

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np


#数据预处理
dataset = pd.read_csv('D:\100Daysdatasetsstudentscores.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=0.25,random_state=0)

 数据集

print(dataset.head(5))

   Hours  Scores
0    2.5      21
1    5.1      47
2    3.2      27
3    8.5      75
4    3.5      30

二，训练集使用简单线性回归模型训练

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor = regressor.fit(X_train,Y_train)

三，预测结果

Y_pred = regressor.predict(X_test)

 四，可视化

#训练集结果
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
plt.scatter(X_test,Y_test,color = 'red')
plt.plot(X_test,regressor.predict(X_test),color = 'blue')
plt.show()

 训练集：

 

测试集：