机器学习（3）-简单线性回归：数据集与源码下载

import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression dataset = pd.read_csv('SD.csv') print("查看dataset") print(dataset) X = dataset.iloc[:, :-1].values# 矩阵 y = dataset.iloc[:, 1].values # 向量 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0) print("X_train") print(X_train) print("X_test") print(X_test) regressor = LinearRegression() regressor.fit(X_train,y_train) y_pred = regressor.predict(X_test) print("y_pred") print(y_pred) plt.scatter(X_train, y_train, color = 'red') plt.plot(X_train, regressor.predict(X_train), color = 'blue') plt.title('Salary -training )') plt.xlabel('Years ') plt.ylabel('Salary') plt.show() # Visualising the Test set results plt.scatter(X_test, y_test, color = 'red') plt.plot(X_train, regressor.predict(X_train), color = 'blue') plt.title('Salary -test )') plt.xlabel('Years') plt.ylabel('Salary') plt.show()