简单线性回归模型

第一步：数据预处理

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
dataset = pd.read_csv('../../datasets/Day2.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 = 1/4, random_state = 0)

第二步：训练集使用简单线性回归模型来训练

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

第三步：预测结果

 Y_pred = regressor.predict(X_test)

第四步：可视化

训练集结果可视化

 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

 plt.scatter(X_test , Y_test, color = 'red')
 plt.plot(X_test , regressor.predict(X_test), color ='blue')
 plt.show()