Python多元线性回归sklearn

# -*- coding: utf-8 -*-
"""
Created on 2024.1.22

@author: rubyw
"""

import numpy as np
from numpy import genfromtxt
from sklearn import linear_model
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 读入数据
data = genfromtxt('Delivery.csv', delimiter=',')
print(data)

# 切分数据
x_data = data[:, :-1]
y_data = data[:, -1]
print(x_data)
print(y_data)

# 创建模型
model = linear_model.LinearRegression()
model.fit(x_data, y_data)

# 系数
print("coefficients:", model.coef_)

# 截距
print("intercept:", model.intercept_)

# 测试
x_test = [[102, 4]]
predict = model.predict(x_test)
print("predict:", predict)

ax = plt.figure().add_subplot(111, projection='3d')
ax.scatter(x_data[:, 0], x_data[:, 1], y_data, c='r', marker='o', s=100)  # 点为红色三角形
x0 = x_data[:, 0]
x1 = x_data[:, 1]
# 生成网格矩阵
x0, x1 = np.meshgrid(x0, x1)
z = model.intercept_ + x0 * model.coef_[0] + x1 * model.coef_[1]
# 画3D图
ax.plot_surface(x0, x1, z)
# 设置坐标轴
ax.set_xlabel('Miles')
ax.set_ylabel('Num of Deliveries')
ax.set_zlabel('Time')

# 显示图像
plt.show()