Day_2 简单线性回归模型

 

第一步：数据预处理

In [2]:
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

In [3]:
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor = regressor.fit(X_train, Y_train)

第三步：预测结果

In [4]:
Y_pred = regressor.predict(X_test)
第四步：可视化

训练集结果可视化

In [5]:

plt.scatter(X_train, Y_train, color = 'red')
plt.plot(X_train, regressor.predict(X_train),color = 'blue')
plt.show()

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