# 1.数据预处理
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset = pd.read_csv('dataset/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)

# 2.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)

# 3.预测结果
Y_pred = regressor.predict(X_test)
# print(Y_pred)

# 4.可视化
plt.scatter(X_train, Y_train, color='red')
plt.plot(X_train, regressor.predict(X_train))
plt.show()

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