机器学习100天: Day2 简单线性回归

本文介绍简单线性回归算法及其代码实现

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('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 = 1/4, 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()